The big idea: The transition tax

Reading time: 5 minutes

“The future is here, it’s just not evenly distributed.”

William Gibson wrote that way before AI arrived. But the quote feels especially relevant as I write this week’s edition from 35,000 feet, using United’s Starlink Wi-Fi, powered by SpaceX, to send instructions to my AI agents working across multiple projects in parallel.

The stack itself feels improbable. SpaceX powers the high-speed satellite Wi-Fi. OpenAI and Anthropic power the AI agents. Three companies, all sitting near the center of trillion-dollar-scale speculation about where the next platform shift is headed.

Yet these technologies remain astonishingly scarce in practice. Out of curiosity, I asked ChatGPT to estimate how many people on Earth might be using Claude Code on a Starlink-enabled United flight at the same time.

Its answer: maybe 20.

The future often arrives this way, slowly as a strange little pocket of improbability that barely anyone notices. Then suddenly.

That future is here. But increasingly, the technologies themselves are not the only things unevenly distributed. The more I live in this new world, the more hyperaware I become of another uneven distribution: mankind’s capacity to absorb change.

Two Parallel Realities

Getting off the plane, I stepped back into the old world, which was fittingly O’Hare’s Terminal 2. It has not been updated in decades. You can still see where the telephone booths once were.

Spend a couple of hours using all this new AI, then try to do the same work the old way. No way. The new way is just too good.

But here’s the thing: the old systems have not disappeared just because the new ones arrived.

Knowledge workers are now balancing two parallel realities: the existing job they are still accountable for, and the emerging AI-native workflow they suspect may eventually redefine the job entirely.

It is energizing, invigorating, concerning, and exhausting all at once.

I feel this tension personally every week.

Most Friday mornings, after a long week of travel, meetings, operational cadence, and finally hitting publish on Reading Ambitiously, I let out a sigh of relief. Then I typically message our CTO and VP of AI some version of the same thought:

“Gosh, I had so much to get done this week based on my traditional set of responsibilities, but I can’t wait to continue building out all the AI to redefine my next set.”

More often than not, I do not feel like I have enough hours in the day for the latter.

This AI revolution is not arriving into empty calendars.

Microsoft calls this the “Transformation Paradox.” Organizations want all the benefits of AI while still measuring performance through legacy systems and incentives.

And unfortunately, new technology tends to add complexity before it reduces it, leading to burnout and what feels like a cognitive tax.

This is normal. When email arrived, it increased the volume of communication and raised expectations for timely response. Now that AI is here, it is incredibly good at generating content. But reviewing and validating that content is becoming more demanding.

Ahead of a recent meeting, I received more than 50 pages of pre-reading material, much of it carefully curated by our teams and some of it helpfully generated by AI.

What am I supposed to do? Have AI summarize the AI-assisted pre-read?

The Deployment Layer

Companies are beginning to realize they may need help with this transition. For many, AI-native work is still happening in the shadows. But the market is beginning to professionalize that shadow work.

Carlota Perez, my spirit guide for technological revolutions, would recognize this moment immediately. We are somewhere past the frenzy, approaching the turning point, and the deployment period is beginning to take shape. That is not a metaphor. It is literally what they are calling it. OpenAI announced the OpenAI Deployment Company this week, a new venture staffed with forward-deployed engineers who will help companies install AI.

Stripe seems to understand this, too. The company recently posted a new role, “Forward Deployed AI Accelerator,” describing it as embedding AI-native operators directly into its marketing department to help teams of roughly 20 make AI the default mode for all work.

The hard part is no longer simply getting access to AI, although it is astonishing to me how many people still do not have it in the workplace. The hard part is integrating AI into the business's daily operations and embracing change.

Why It Matters

The lesson is that new technologies do not create an advantage simply because they exist. They create an advantage when people reorganize around them.

At first, the new tool gets bolted onto the old system. Email becomes faster mail. Websites become digital brochures. Cloud becomes rented servers. AI becomes a better search engine.

Useful, but the real leverage shows up when the work changes shape.

Electricity is the classic example. Before factories were electrified, many were organized around a central steam engine. The engine turned a shaft. The shaft ran through the building. Belts and pulleys carried power to individual machines. The entire factory was designed around the constraints of the power source.

Then electricity arrived. But many factory owners did not immediately redesign the factory. They simply replaced the steam engine with an electric motor. They had a new power source running an old operating model.

The real gains came later, when factories put smaller electric motors on individual machines. Once power no longer had to flow through a single central shaft, the work could be reorganized. Machines could move. Production lines could change. Buildings could change. The flow of work could change.

The technology mattered, but the reorganization mattered more.

A century later, Tesla gave the same idea a modern form. The car mattered, but so did the machine that built the car. Tesla framed it as an effort to turn the factory itself into a product and build the machine that makes the machine.

That may be the best way to think about AI.

The tech is impressive. But the long-term advantage will likely come from the operating system around it.

Right now, many companies are using AI like an electric motor attached to the old shaft. AI-generated pre-reads. AI-assisted summaries. AI pilots. AI copilots layered on top of work designed before AI existed.

That is useful. It is not the unlock.

The better question is not: how do we use AI to do the old work faster?

The better question is: what would this work look like if AI were assumed from the beginning?

The old world is still here. The new one has arrived. And most of us are living in the gap between them, landing in Terminal 2 while our agents are still working somewhere above the clouds.

The advantage goes to the people and companies willing to close that gap. Not by electrifying the shaft. By redesigning the factory.

The ultimate long-term advantage will go to individuals and companies that pay the transition tax.

Stay ambitious!

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Best of the rest:

🤖 OpenAI launches the OpenAI Deployment Company to help businesses build around intelligence — OpenAI is turning enterprise AI deployment into a company-level bet, combining FDEs, Tomoro’s applied AI talent, and a heavyweight partner network to move customers from AI experiments to production systems. — OpenAI

💸 How a Job at OpenAI Became the Greatest Lottery Ticket of the AI Boom – OpenAI’s latest employee stock sale shows just how much wealth the AI boom is creating before IPO, turning hundreds of employees into multimillionaires and giving Silicon Valley a preview of the economic shockwave still to come. – The Wall Street Journal

🍽️ QR Codes Didn't Kill Waiters. And Neither Will AI. — Sophie Bakalar makes the clearest case yet that AI will automate tasks, not jobs, because the human layer still matters wherever trust, care, and accountability are part of the product. — Sophie’s Notes

🛠️ Learning on the Shop floor — Shopify’s River shows that the real unlock in enterprise AI may not be private productivity, but public apprenticeship, where every agent interaction becomes searchable, teachable, and compounding company knowledge. — Tobi Lütke

⚡ Compute Futures Are Here – As AI demand turns GPUs and advanced chips into strategic infrastructure, financial markets are beginning to treat semiconductors like oil or electricity, creating new ways to hedge price volatility and speculate on the backbone of the AI economy. – CNBC

Charts that caught my eye:

→ Why does it matter? Tech startups are 5% of active U.S. entrepreneurs using ChatGPT. The other 95%: agency owners, plumbers, Etsy sellers, dentists. One read: the AI adoption story for small businesses is already further along than the coverage of it.

→ Why does it matter? SalesForce.com has announced they’re going headless. Seema Amble argues that as agents bypass the UI, software defensibility shifts from human workflow and muscle memory to data models, permissions, proprietary context, compliance, and real-world execution. — a16z

→ Why does it matter? The clearest snapshot I've seen of where software adoption is actually moving: 33 of 69 major vendors declining YoY. Great work from Deedy Das — worth spending time in every quadrant.

Tweets that stopped my scroll:

→ Why does it matter? In the pursuit of organizing around the work, some CEOs are trying entirely new designs. Dorsey's version: collapse Block's five management layers to two or three, with AI doing the information routing that middle management used to own. (Fortune)

→ Why does it matter? Few read the market better than Coatue, and their take: the market is pricing the supply chain, not the applications.

→ Why does it matter? 🤯

→ Why does it matter? The mouse pointer is a 50-year-old interface, and Google DeepMind is experimenting with replacing it with motion, voice, and gesture.

→ Why does it matter? Shopify's internal AI agent increased its merge rate from 36% to 77% in two months. Not by switching models, but by making all AI work visible in public Slack channels. Everyone watching, everyone learning.

Worth a watch or listen at 1x:

→ Why does it matter? Naval's blunt take: pure software is uninvestable. If your only edge is code that others can't build, that edge is gone. Worth 1x for the contrarian framing on venture and his walkthrough of building a personal app store on his iPhone.

→ Why does it matter? Anthropic's CFO walks through how they went from a $9B to a $30B run rate in a single quarter. Krishna Rao's view from inside: compute is the canvas, not a variable cost, and 90% of Anthropic's own code is now written by Claude Code.

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Quotes & eyewash:

→ Why does it matter? There is simply not enough time in the day to do all of the things you want (or should) do. What can you say “NO” to?

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The mission:

The Wall Street Journal once used “Read Ambitiously” as a slogan, but I took it as a personal challenge. Our mission is to give you a point of view in a noisy, changing world. To unpack big ideas that sharpen your edge and show why they matter. To fit ambition-sized insight into your busy life and channel the zeitgeist into the stories and signals that fuel your next move. Above all, we aim to give you power, the kind that comes from having the words, insight, and legitimacy to lead with confidence. Together, we read to grow, keep learning, and refine our lens to spot the best opportunities. As Jamie Dimon says, “Great leaders are readers.”

Disclaimer: This content is for informational purposes only and does not constitute financial, investment, or legal advice. Readers should do their own research and consult with a qualified professional before making any decisions.

The big idea: Building AI-Native Products. Where’s the IP?

Reading time: 8 minutes

A strange thing is happening in software.

For most of my career, the origin of value creation was relatively clear. The company owned the source code. The code became the product. The product created the workflow. The workflow created the business.

AI has made that answer less obvious.

That is why I keep coming back to this topic. Not because I have the answer. I don’t. But because I think this is the question sitting underneath almost every AI-native product right now:

Where’s the IP?

If you’re one of our weekly readers, by now you might be thinking I’ve become a bit obsessive with this topic.

I think you’re right. It’s also for good reason.

Software is, was, or will be again a $4 trillion asset class. Software, now mostly delivered “as a Service,” has matured over the course of my career. I remember the early days of cloud, well before AWS, GCP, and Azure became the dominant hyperscalers. I remember when Salesforce.com and Workday paved the way.

Today, you can practically get a bachelor’s degree in building a SaaS company. The business model has a well-understood set of unit economics: CAC, LTV, net retention, gross margins, the Rule of 40, and other metrics that have helped create a common language for valuing software companies.

If a SaaS company delivered a product, found the market, and demonstrated efficient growth, investors felt reasonably confident in its ability to generate long-term, predictable cash flows.

AI has put that idea into question.

Some people call this the “SaaSpocalypse”: the notion that SaaS becomes less valuable as AI can write software, automate workflows, and compress the value of many point solutions. We’ve written about that before and believe that not all software is created equally.

And it is an important context.

Because my obsession with AI-native products is really a pursuit of understanding the fundamentals of how value gets created in the new era.

If the above is the well understood, time tested way to create value in SaaS? What is that in AI?

The harness is the product

Many of us assumed AI products might be monetized in ways that look familiar. SaaS companies launched AI features and priced them as an add-on.

But the pattern emerging for building and monetizing AI-native products may differ significantly from SaaS.

I am not talking about model producers like OpenAI or Anthropic. I’m talking about the application companies that rent the model through an API and then harness it to deliver predictable output.

Glean is a good example: a harness around Anthropic and OpenAI models designed to help enterprises find answers across their knowledge base, including Google Drive, Slack, and the other systems where work already happens.

There is now a rapidly growing ecosystem of AI-native applications companies doing something similar. These are the application companies built on this new technology.

What they are doing is harnessing the LLM and pointing it at a specific output.

The harness is the AI-native product.

At least today.

Think of it this way. You strap the LLM into an exoskeleton. Then you build everything into the suit that the AI needs to deliver reliable work.

The suit needs memory, so it can remember what matters.

It needs tools to know when to use a calculator instead of asking the model to guess.

It needs plug-ins and integrations, because real work doesn’t happen in a blank chat window. It happens in Google Drive, Slack, Salesforce, Outlook, Word, Excel, and the systems where the business already lives.

It needs proprietary data, so the model can be augmented with examples and documents that improve the quality of the output.

It needs permissions to know what it can touch, what it can change, and what needs approval.

It needs skills, so repeated work can be encoded into reusable procedures with your secret sauce baked in.

And it needs evals, so the system can learn the difference between output that sounds right and output that actually is right.

Building this exoskeleton is what R&D teams at AI-native application companies are working on. If you are building one of these products, technical breakthroughs across these areas, and the specific way they are combined, can add up to real competitive differentiation.

That is the theory.

But a strange thing is happening in legal AI that puts the theory under pressure.

A real-world test case

Legal AI is becoming the perfect test case because the work is valuable, text-heavy, high-stakes, and still deeply dependent on trust.

There is currently a race in the market between Harvey and Legora. Recently, Legora hired Jude Law to be its spokesperson.

Harvey has done an incredible job creating urgency in the legal industry, turning a profession that is typically late to adopt technology into one of the earliest enterprise adopters of AI.

Harvey recently raised $200 million at an $11 billion valuation. Legora raised $550 million at a $5.55 billion valuation. You might think they are running away with the market.

Then last week, former Latham & Watkins lawyer Will Chen launched Mike, an open-source legal AI platform. Mike describes itself as an open-source alternative to Harvey and Legora, with “feature parity,” zero cost, and a self-hostable codebase that law firms can own and extend.

Mike’s point was not subtle: much of the visible application layer can now be rebuilt and distributed for free.

To pile on, Microsoft introduced a Legal Agent directly inside Word, where much of legal work already happens today.

So it begs the question sitting underneath almost every AI application company right now, and those valuations:

Where’s the IP?

In SaaS, this question was easier to answer. The company owned the code. The code became the product. The product created the workflow. The workflow created switching costs. Customers paid for access.

AI-native software is harder to understand.

The model may belong to OpenAI, Anthropic, Google, Meta, or xAI. Those companies are making a lot of money, or at least they are today, although they also face pressure from open-source alternatives.

The interface may be easy to copy. The integrations may sit on top of APIs available to everyone. And the customer may not actually care about the software at all.

They may just care about the work getting done.

That is why the legal AI moment forces the harder question: is the value of the product in the visible application layer, or does it lie deeper?

This is where I keep getting stuck.

The easy answer is that the harness is the IP. I think that is directionally right. But it is also incomplete.

Because not every harness is equally defensible.

If the harness is just a chat interface, a project folder, a tabular review workflow, and a few saved prompts, then maybe the critics are right. Maybe that application layer is thinner than we thought. Maybe it can be rebuilt faster than investors want to admit. Maybe Microsoft can absorb it into a Word feature. Maybe an open-source project can give customers enough of the visible product to make the premium vendor harder to justify.

That is the uncomfortable version of the argument.

But there is another version.

Maybe the visible application layer is not the real product. Maybe it is just the part we can see.

In most serious enterprise workflows, the value is not just the screen. It is the accumulated understanding of how work gets done: the weird edge cases, approval paths, partner preferences, security model, integrations, and trust required to put the system into production.

That stuff does not show up cleanly in a demo. It sits underneath the iceberg.

In enterprise software, the visible product has never been the whole story. The real product is often the system of work behind it.

AI makes that more true, not less.

A good harness does not just help the model answer questions. It teaches the model how a specific kind of work should be done. It encodes the difference between a plausible answer and an acceptable one. It knows what should never be touched without a human in the loop.

That is a different kind of product.

It is less like selling software and more like building an operating system for a category of work.

The question is whether that operating system becomes defensible enough to support large standalone companies, or whether the value gets pulled apart by platforms, model companies, open source, and customers building for themselves.

I do not know the answer.

But I think this is the right question.

Because the next generation of AI application companies will be judged on whether they can turn that output into a repeatable, trusted, governed workflow that customers are willing to depend on.

That is where the moat may get seriously deep.

At the point where the system becomes trusted enough to own part of the work.

The “business” of AI

This is the part I keep coming back to.

In SaaS, the product was the software. In AI-native companies, the product may be the harness. But the business of AI may be delivering the outcome.

That distinction feels important because the buyer may not actually want another application.

The buyer wants the work done.

There is an old adage:

“People don’t want to buy a quarter-inch drill. They want a quarter-inch hole.”

A law firm needs documents reviewed, contracts redlined, and work product delivered to a standard that partners and clients will trust.

A CFO does not need another accounting copilot. She needs the books closed.

An insurer does not need another claims assistant. It needs claims to be processed accurately.

This is where the IP question starts to move beyond software.

Maybe the most defensible AI-native companies will not just sell the harness. Maybe they will use the harness to do more of the work.

My friend Sean O’Connell, who is fittingly a lawyer, came up with a phrase for this: tech-shoring.

Offshoring moved labor to a cheaper geography. Tech-shoring moves labor into a technology-mediated system. The work is still supervised by humans where judgment, trust, and accountability matter. But the leverage comes from the AI and how it is harnessed.

This is not pure software. It is not traditional managed services. It is not simply giving people a copilot and hoping productivity improves.

It is using software to deliver the service itself.

That changes the business model.

If you sell the harness, the customer can ask whether they should build it. If you sell the outcome, the customer has to ask a different question: Can you deliver the work better than I can, and are you willing to stand behind it?

Better may mean cheaper. It may mean faster. It may mean more consistent. It may mean more auditable. It may mean less dependence on hard-to-find labor. It may mean the service improves over time because every job teaches the harness something new.

That is a more interesting moat because the value is not just in the tool. It is in the operating system around the work: the process, the data, the human review, the liability, the quality control, the service levels, the customer-specific knowledge, and the feedback loop from doing the work repeatedly.

This is where the IP may start to look less like software IP and more like institutional capability.

That is harder to see from the outside.

It is also harder to copy if it compounds.

Why it matters

We are still using an old software map to navigate a new market.

In SaaS, the questions were relatively familiar: Who owns the code? How sticky is the workflow? What is the retention? How efficient is the growth?

In AI-native products, the questions are different.

If the model is rented, the interface can be copied. The company has to prove it owns something deeper than the screen.

That might be a harness that compounds. It might be proprietary workflows. It might be customer-specific data. It might be trust, governance, or liability. It might be the ability to deliver the outcome better than the customer can.

For builders, this changes what is worth building.

For buyers, it changes what is worth buying.

For investors, it changes what is worth valuing.

The easy answer is to call everything a wrapper. The harder answer is to understand which systems are merely wrapping the model and which are learning to own the work.

The most valuable AI-native companies may not be the ones with the best demo. They may be the ones who turn rented intelligence into work.

The model may be rented. The harness may be the product. The outcome may be the business.

And the IP may live in the system that connects all three.

Stay ambitious, my friends.

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Best of the rest:

🤖 DeepSeek nears $50 billion valuation in first fundraising round — China is signaling serious intent in the global AI race, with state-backed capital and tech giants lining up behind a breakout model player that could reshape competitive dynamics. — Reuters

💸 Corporate Card Startup Ramp Raising Funds at $40 Billion Valuation — Ramp’s reported $750 million raise shows investor appetite for AI-powered finance automation is still running hot, with fintech’s category winners continuing to pull away from the pack. — The Wall Street Journal

🧠 Do I belong in tech anymore? – A raw, searching essay on AI burnout and the collapse of tech’s old self-image, arguing that speed without care is hollow because the real work of building software is human judgment, friction, and craft. – ky.fyi

⚙️ A.I. Should Elevate Your Thinking, Not Replace It – A sharp line in the sand for the AI era, arguing that the best engineers will use AI to remove drudgery and deepen judgment, while the ones who outsource thinking will quietly make themselves irrelevant. – Koshy John

Charts that caught my eye:

→ Why does it matter? Financial services went from a rounding error to Anthropic’s #2 vertical in about 18 months, with 40% of top customers being financial institutions. Banks tend to be slow buyers of frontier tech. Not this time.

→ Why does it matter? Talent density is everything when capital is abundant. Paraform’s ranking is a view into where the market thinks the best builders are clustered.

Tweets that stopped my scroll:

→ Why does it matter? Sierra went from four design partners to 40% of the Fortune 50 in roughly two years. And now they’re raising a $1B warchest. Also, Bret is the current chairman of OpenAI and is seen as a logical successor to Sam Altman.

→ Why does it matter? Perplexity Computer is an always-on AI harness, and this week, they’re announcing a new offering focused on Financial Services, giving access to datasets from MorningStar, Daloopa, and beyond. This is a direct shot at Bloomberg Terminal, and it’s compelling.

→ Why does it matter? Sam Altman texts then-OpenAI CTO Mira Murati during the BOD meeting where he was ousted.

→ Why does it matter? Incredible growth from Palantir. A nice architectural overview of AIP, their AI-focused front-end to the Palantir platform.

Worth a watch or listen at 1x:

→ Why does it matter? You’re doing something right if your first customer conversation is with Jamie Dimon. Anthropic laid out a compelling vision for financial services, its second-fastest-growing market behind technology, and made clear why the category matters: high-stakes workflows, dense information, and customers with both the urgency and resources to put AI to work.

→ Why does it matter? A master class in presenting company strategy from Bill McDermott and the ServiceNow team, with cameos from Jensen Huang and Anthropic’s lead engineer on Claude Code. ServiceNow told a clear story about where enterprise AI is headed and why workflow sits at its center. Financial Analyst Days are always incredible forcing functions. They make companies do the hard work of turning strategy into story, and story into conviction.

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Quotes & eyewash:

→ Why does it matter? Build a high agency team and strive for intellectual honesty. A few years ago, I stumbled upon this great book on the subject: Mistakes Were Made (but Not by Me): Why We Justify Foolish Beliefs, Bad Decisions, and Hurtful Acts.

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The mission:

The Wall Street Journal once used “Read Ambitiously” as a slogan, but I took it as a personal challenge. Our mission is to give you a point of view in a noisy, changing world. To unpack big ideas that sharpen your edge and show why they matter. To fit ambition-sized insight into your busy life and channel the zeitgeist into the stories and signals that fuel your next move. Above all, we aim to give you power, the kind that comes from having the words, insight, and legitimacy to lead with confidence. Together, we read to grow, keep learning, and refine our lens to spot the best opportunities. As Jamie Dimon says, “Great leaders are readers.”

Disclaimer: This content is for informational purposes only and does not constitute financial, investment, or legal advice. Readers should do their own research and consult with a qualified professional before making any decisions.

The big idea: The next killer app is AI computer use

Reading time: 8 minutes

The next killer application for AI may sound like a bad idea.

Give it access to your computer.

Access to the actual machine where knowledge work happens: your browser, file system, productivity tools like Excel and Word, inbox, calendar, and all the half-finished context that makes modern work feel modern.

That sounds reckless.

It may also be the most logical next step toward realizing AI’s full potential.

It’s called computer use. The AI uses a keyboard, mouse, browser, and operating system just like humans do.

If AI is going to automate knowledge work, it eventually has to get “hands on” with the knowledge worker’s primary tools.

The strange part is not that this is technically possible, although the first time you see AI driving around your computer, it’s mind-boggling.

The strange part is that it may make sense to let AI operate the machine where work already happens.

The factory floor of knowledge work

The Industrial Revolution did not eliminate work. It reorganized it.

Labor moved from farms to factories, then from factories to offices. The modern knowledge worker was not born in opposition to automation. In many ways, knowledge work was created by it.

Today, the factory floor of knowledge work is a screen.

The machinery is familiar: inboxes, spreadsheets, decks, agendas, tabs, calendar invites, PDFs.

This is where most work happens.

And the dominant provider of the tools for this work is Microsoft, anchored in Office and the broader Microsoft 365. Microsoft 365 has more than 80% enterprise market share, more than 500 million paid seats, and more than $80 billion in revenue.

Paul Graham once called Microsoft “a mountain that can walk.”

AI computer use is not circling a small category. It is circling this mountain.

And this makes a lot of sense. The AI is moving out of the chat window and into the environments where real work happens.

This has already proven to work in agentic coding, which is nearly perfect terrain for AI: codebases, terminals, tests, logs, and GitHub.

Knowledge work is messier. It does not have the cleaner, more structured world of software development.

It has the computer.

The first wave of products is here

For decades, productivity software assumed humans pushed the buttons. Users opened the application, wrote the words, attached the file, updated the spreadsheet, and sent the email.

Computer use gives AI the ability to press the buttons.

Humans still provide the desired outcome, but the AI performs as many steps as possible.

Claude Cowork is a great example. It moves beyond the prompt window and starts working across your files, browser, folders, and applications to complete tasks autonomously.

Microsoft recently released its competitor: Copilot Cowork.

Other examples include Perplexity Computer, Factory’s Droid Computers, OpenClaw, and Hermes. Just yesterday, Manus (acquired by Meta) launched their competitor.

And, of course, OpenAI is going with Codex here. In this demo, you can actually see the AI using a mouse.

Different products, same idea.

Move the AI into an “always on” position and into the place where work happens.

The harness is the product

A framework for building these types of AI computer-use products is coming into clarity.

A few weeks ago, we wrote that the harness is the product. A 2x user and a 100x user can be using the same AI model. The difference is the harness.

The harness is the program that wraps the model and helps it do useful work. A good harness gives the model context, tools, and memory management. It decides what belongs in the context window and when. It decides what the model should handle and what should be routed to more precise software.

With a good harness, the AI can make better decisions to achieve accurate, high-quality outcomes.

A simple example is arithmetic.

It makes no sense to ask the AI to do arithmetic when a deterministic calculator already exists. The AI should know when to call the calculator. It should know when to query the database. It should know when to use the browser, open the file, run the script, or ask the human for approval.

That is the difference between a model with access to tools and a system that knows how to work.

The model is useful for judgment: reading, synthesis, and classification.

Deterministic tools are useful for precision: queries, calculations, and workflows where the same input should produce the same output every time.

The worst agent systems confuse the two. They ask the model to do arithmetic. They force it to reason through tasks that should have been handled by software.

The best systems push intelligence up into skills and push execution down into deterministic tools.

This is how AI computer use becomes a lot more than AI clicking around on your screen.

It becomes a system that lives in the knowledge worker’s environment and can get work done.

Building a moat into the wrapper

If the model is not the whole product, the next question is where the IP lives.

• It may live in the harness: tooling, memory, permissions, routing, evals, and integration.

• It may live in the skills: reusable procedures that encode how work gets done.

• It may live in the proprietary datasets: examples, corrections, approvals, exceptions, and workflows that teach the system what “right” looks like.

• It may live in the integrations: the ability to touch the systems you need to drive outcomes.

• It may live in the tacit knowledge that gets captured over time: your deep understanding of the domain, the judgment embedded in your workflows, the “secret sauce” of how you do what you do.

This is not traditional software IP. The crown jewels used to be the code.

This is not code.

It is institutional knowledge that augments the intelligence of the AI.

A skill is a reusable procedure. It captures the process, judgment, and context required to perform a task. The user supplies the specific request. The skill supplies the method.

Imagine a skill for investigating a company. It might tell the model to pull filings, build a timeline, compare management commentary against financial results, identify contradictions, summarize both sides of the argument, and cite the sources.

The same skill can be applied to different companies, questions, and datasets.

That is not prompt engineering. It is software design in a language the model understands.

If the AI can help perform the work, observe the pattern, and codify the process, the workflow itself becomes an asset.

Trust is essential

By now, you’ve probably formulated an opinion on whether you’d give AI access to your computer.

My hunch is that most readers are a “no.”

You could buy a new computer and give the AI its own. That’s what I’m doing. The base Mac mini was recently unavailable in Apple’s online store, and TechCrunch reported that marked-up Mac minis were appearing on eBay amid demand for machines that can run on-device AI.

Giving AI access to your computer is not a small permission. It is like giving a junior employee your laptop, your badge, and your password, then telling them to log in and help you get things done.

That employee may be capable. They may also misunderstand the task, click the wrong button, expose sensitive data, or post the wrong thing in the wrong place with confidence.

In the human world, we manage that risk with roles, permissions, training, audit trails, and supervision. In security, ideas like Zero Trust start from a similar premise: do not assume trust just because someone or something is inside the system. Verify access. Limit permissions. Require the right approval at the right time.

Maybe unglamorous, but AI will need this version of trust machinery for both consumer and enterprise usage.

And this is a big reason enterprise incumbents have a leg up. They have already built much of the machinery that makes software usable in real companies.

Computer use will raise the bar here.

The AI cannot just use the computer. It has to use the computer as someone, with a specific role, a specific set of permissions, and a specific boundary around what it can and cannot do.

Without that, it is not a product that companies can trust.

It is a very impressive prototype.

From productivity tools to supervising the work

The knowledge worker’s current tools give humans what they need to do the work.

Documents. Spreadsheets. Slides. Email. Calendars. Chat.

The new productivity layer may use all of these tools. It may not replace Excel. It may use Excel. It may not replace Outlook. It may use Outlook.

This is the strategic tension for Microsoft and Google. They may be mountains, but the terrain around them could change quickly.

The question is not whether AI gets added to productivity software. It will. The question is whether the knowledge worker’s primary tools remain the place where work happens, or become the surface on which AI operates.

For decades, the model was simple: learn the software, push the buttons, produce the work, share it.

The next model may be different: describe the outcome, watch the system work, approve what matters, share it.

From pushing the buttons yourself to supervising the work.

We may have to give the AI access to a computer to get there.

Stay ambitious.

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Best of the rest:

🤖 OpenAI Misses Key Revenue, User Targets in High-Stakes Sprint Toward IPO — OpenAI’s growth story is colliding with the brutal economics of compute, as missed user and revenue targets raise harder questions about whether frontier AI can scale fast enough to justify its infrastructure arms race. — The Wall Street Journal

💰 Sources: Anthropic could raise a new $50B round at a valuation of $900B — Anthropic’s reported mega-round shows the AI capital race shifting from model capability to balance sheet capacity, with Claude’s coding momentum turning investor demand into a direct challenge to OpenAI’s scale advantage. — TechCrunch

🛡️ Our evaluation of OpenAI's GPT-5.5 cyber capabilities – AISI’s latest cyber eval shows GPT-5.5 pushing into frontier-risk territory, with one of the strongest performances they’ve measured and a rare end-to-end solve of a multi-step attack simulation. – AISI

⚠️ Cursor's warchest, xai's redemption – Ethan Ding argues Cursor’s rumored sale to xAI is less a victory lap than a warning shot for the AI application layer, where even breakout products can get squeezed when their model suppliers decide to own the margin pool. – mandates

Charts that caught my eye:

→ Why does it matter? Acquirers are paying scarcity prices for AI Native companies. At a 17.7x median and half the deals clearing 20x, buyers are betting that quality AI assets won’t get cheaper.

→ Why does it matter? AI labs built consumption pricing from day one. Traditional SaaS spent decades optimizing seat contracts, and that model is sticky: 3.8% consumption share after 11 months of AI pressure.

→ Why does it matter? Ten companies are now larger than the entire G7 national economies. Eight of those ten are American tech firms.

→ Why does it matter? Half the new $5B+ private companies in 2025 are AI apps, with the application layer (Cursor, Cognition, Harvey) clustering at the top. Capital is rewarding the layer closest to end users, not just the model labs underneath!

→ Why does it matter? The spike lines up almost exactly with ChatGPT’s public release. LLMs love dramatic rhetorical pivots and engagement-seeking styles. Your earnings calls may already sound like everyone else’s.

Tweets that stopped my scroll:

→ Why does it matter? Jane Street generates $8M in profit per employee, with 3,500 people. 🤯

→ Why does it matter? OpenAI building a phone with Luxshare (Apple’s own manufacturer) signals ambition well beyond ChatGPT and Codex. And they, of course, have Jonny Ive!

→ Why does it matter? Telling people they slept well (even falsely) measurably improved their cognitive test scores. What you believe about your condition shapes your actual performance, not just your mood.

Worth a watch or listen at 1x:

→ Why does it matter? CEO’s have the mandate from their BODs to figure out AI. A pleasure to join Marley Kayden and Sam Vadas on Schwab’s Market on Close to discuss a wide range of topics, including Ridgeline’s AIOS for Asset & Wealth Management, AI computer use, the “it” in AI is the data set, and the SaaS apocalypse. Complete with Harry Potter references.

→ Why does it matter? Karpathy reframes LLMs as ghosts rather than animals: jagged, statistical, summoned. He also separates vibe coding from agentic engineering and argues you can outsource thinking but not understanding.

→ Why does it matter? Tudor Jones walks through the trader-versus-investor split, lessons from 1987 and the 1980 silver collapse, and his current read on the debt bubble and AI risk. This is a wide-ranging conversation with an industry legend. Must watch!

→ Why does it matter? Dwarkesh is on a hot streak (NYT profile this week), and this episode is a good example of why. Reiner Pope walks through how frontier models actually get trained and served, and a striking amount of it reduces to a handful of equations.

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Quotes & eyewash:

→ Why does it matter? I pre-ordered David Epstein’s new book, Inside the Box, this week. Epstein’s previous bestseller, Range, made the case for generalists. His new book asks a different question: why do limits often produce better work? That feels relevant to AI right now. Look at DeepSeek. The company did not have the cleanest supply chain, the largest American-style lab budget, or unfettered access to Nvidia’s best chips. And yet, DeepSeek trained V3 on 2,048 H800 GPUs and claimed performance comparable to leading closed-source models at a fraction of the cost. The question is not whether DeepSeek is the best model in the world. It isn’t. The question is: why did a constrained team get this close? One answer: scarcity forced better architecture, better training efficiency, and a sharper focus on what mattered.

Subscribe now

The mission:

The Wall Street Journal once used “Read Ambitiously” as a slogan, but I took it as a personal challenge. Our mission is to give you a point of view in a noisy, changing world. To unpack big ideas that sharpen your edge and show why they matter. To fit ambition-sized insight into your busy life and channel the zeitgeist into the stories and signals that fuel your next move. Above all, we aim to give you power, the kind that comes from having the words, insight, and legitimacy to lead with confidence. Together, we read to grow, keep learning, and refine our lens to spot the best opportunities. As Jamie Dimon says, “Great leaders are readers.”

Disclaimer: This content is for informational purposes only and does not constitute financial, investment, or legal advice. Readers should do their own research and consult with a qualified professional before making any decisions.

The big idea: How I use AI in my daily life

Reading time: 10 minutes

The most frequent question I get from friends, family, and readers is simple:

“Jack, how do you actually use AI?”

A friend asked me that on Saturday night, standing at the kitchen counter.

What this person meant, though, is something deeper. They do not want to fall behind.

I think a lot of people feel that right now. The world is moving quickly, AI is suddenly everywhere, and there is a quiet fear that everyone else has already figured it out.

They haven’t.

The data makes that clear. Gallup now shows that half of U.S. employees use AI in their role. But only 28% use it frequently, and just 13% use it daily. It is still early. Very early.

Most people are still using AI like a slightly better version of Google.

That alone is useful. I do it too. This morning I asked, “How many days until Mother’s Day?”

But AI is much more powerful than that. The more interesting shift happens when AI stops being a better search box and starts becoming your assistant. When it starts taking real work off your plate.

A lot of people may have tried AI now. Very few are using it anywhere close to its potential. In fact, the group using Agentic Coding is tiny, about 0.04% of the world’s total population.

So, good news. We are still super early and can learn this together.

Getting Started

By now, I assume most readers have tried at least one of the major AI products: ChatGPT, Claude, Gemini, or Grok.

I use all four.

ChatGPT is my all-around daily driver. Best overall product experience across desktop and mobile, broad tooling, strong multimodal capabilities, meaning text, photos, and video, and the easiest place to start.

Gemini is especially useful if you use Google Workspace. Gmail, Docs, Sheets, and Drive are all right there. Its image generation, Nano Banana, is also excellent.

Claude, and especially Claude Code, is where I go when I want to build software or create workflows that persist beyond a chat window. It is the difference between getting an answer and building a working system.

Grok is useful when I want a real-time read on the internet, especially around X, headlines, and what people are actually talking about right now.

You do not need all four. You just need to understand what each is good at.

One practical tip before anything else: turn on voice.

The best way to interact with an LLM is often to talk to it. MacOS has native voice dictation. The apps do too. There are also paid tools like WhisperFlow that are even better. Over the course of a day, I often record a two- or three-minute stream-of-consciousness voice memo just to get the model started on my thinking.

Context and Prompts

Before I start any real project with AI, I think about two things: context and prompts.

Start with context.

The model can only work with what it can see. So before I ever type anything, I ask myself: what would I put on the table if I were doing this project manually?

A spreadsheet. A few notes. A draft. An email thread. A PDF. Maybe some screenshots. Maybe a voice memo.

I think of context as the model’s working memory for the task at hand.

I often create a folder on my desktop and put everything in it before I start. Then I upload the whole thing to the model. If I want a good answer, I need to give it a good view of the problem.

Think of it like setting up shop at a library table. You want the right books open before you start working.

One small habit that helps: when I switch topics, I usually start a new chat. Context is useful until it becomes a distraction.

Then come prompts.

Yes, better prompts usually lead to better output. But people overcomplicate this.

One of the best prompting tricks I know is to ask the AI to help me write the prompt. Open a fresh chat and say: “I want your help writing a prompt for this project.”

Then paste in your rough notes, or, better yet, use a 2-minute voice memo to talk through what you are trying to do.

You will be surprised how good the model is at turning messy thinking into a clear instruction set.

When people ask how to begin, my advice is simple: start with work you already understand.

The best first projects are usually in areas where you already have expertise. That is how you build confidence. You know what good looks like, so you can tell whether the model is actually helping.

In many ways, what I built with AmbitiousOS is basically Reading Ambitiously turned into software.

That is a useful way to think about AI more broadly. Start with tasks where you already know the destination. Let the model help you get there faster.

The hardest part of AI is not understanding the theory. It is seeing where it fits into the flow of an ordinary day.

Which is why I like starting with a to-do list.

The To-Do List

I love a good to-do list. I make a lot of them.

I have tried every application out there to help me organize and make lists. And maybe it is the technology overload of 2026, but these days I still like to do my best thinking with a pen and paper, away from the electrons.

There is an old story about a man who walked up to J.P. Morgan and said, “I have the secret to success in this envelope, but it will cost you $10,000.” Morgan asked to see it first. He opened the envelope. Inside was a single line: “Make a list and do it.” Morgan shook his head and paid the man.

That is usually where my day begins, with a pen, a legal pad, and a list of what is actually on my mind.

On Monday, it looked something like this.

Before AI helps me do the work, I need to know what the work actually is. A handwritten list helps me think through it.

Then I make a simple move. I take a picture of the list, drop it into ChatGPT, and say: “Turn this into a plan. Do the first pass on anything you can.”

That is the first handoff to AI.

From there, each line item starts to change shape. One becomes a research brief. One becomes a prioritization engine. One becomes a cleanup job I have been putting off. And in a few cases, once I know the workflow is worth repeating, if I need to move from ChatGPT into Claude Code and build something more durable, I will.

Here is what that looked like across a real day.

1. 451 Attendees, 20 Conversations That Matter

A spreadsheet with 451 names is not a plan. It is just a lot of rows.

So I start by giving ChatGPT the file and a little context on the conference, who I want to meet, and what a good outcome would look like.

“Hey ChatGPT, I’m headed to a conference and 451 people are registered. Help me break this list down, prioritize the people I should care most about, find their LinkedIns, and give me a short note on why each one matters. Then draft outreach for the top group.”

I am no longer staring at 451 names. I am reviewing 20 decisions.

If I want that to become a reusable system, with enrichment, scoring, and tracking, that is when I move it into Claude Code.

2. The Meeting Brief

This is one of my favorite ChatGPT use cases because it is so close to how a real assistant should work.

I gather the emails, notes, research, and background materials, then I hand over the problem.

“Hey ChatGPT, I have an important meeting coming up. I’m uploading the background here. Put together a brief for me. Give me the key themes, what has changed recently, what I should watch out for, five questions I should be ready for, and a draft of the point of view I want to open with.”

What comes back is usually a much more complete view of the room I am walking into.

This one is mostly ChatGPT end-to-end.

I treat much of this the same way I would a smart junior analyst. Incredibly useful for a first pass. Still worth checking before I bet something important on it.

3. The Mailing List, or the Work You Avoid

This is the kind of task that sits around longer than it should.

An old spreadsheet. Outdated addresses. Random fixes scattered across texts and emails. Not hard work, just annoying work.

“Hey ChatGPT, this mailing list is a mess. I’m giving you the old Excel file, some emails, and text updates. Reconcile everything you can, clean it up, standardize the format, and flag anything that still needs a human look. Oh, and please give me the final output in XLS. Thank you.”

What comes back is a cleaned-up file. Instead of being something I will get to eventually, it gets finished.

4. Trip Planning, From Searching to Steering

Travel is a perfect example of something AI handles well because the inputs are clear and you have to do a ton of research.

Dates. Preferences. Budget. Meetings. Restaurants. Logistics. Too many Chrome tabs.

“Hey ChatGPT, I need help planning a trip. Here are my dates, where I need to be, who’s coming, and what matters most. Give me a plan. Start with flights and hotel options, then help me map the trip so I’m not wasting time bouncing around.”

What comes back is not a final answer so much as a strong first draft.

5. The Follow-Up, Where Value Is Won or Lost

This is where good work often dies. Not in the meeting, but after it.

You have notes, business cards, partial memory, and every intention of following up well.

“Hey ChatGPT, I just got back from a conference. I’m uploading my notes and the people I met. Help me group them by priority, draft follow-up emails, and suggest the right next step and timing for each one.”

What comes back is a system where there used to be a pile.

People get grouped. Emails get drafted. Next steps get named. Nothing falls through the cracks.

Where Each Tool Fits

When people ask how I use AI, they are usually asking about which tool.

The more useful way to think about it is layers.

Some work is about thinking. Some is about doing. And some is about building something you can reuse.

ChatGPT is where most workflows begin and often end. It is my default for research and synthesis, structuring messy problems, drafting and summarizing, and getting to a strong first version quickly.

That is why it shows up so clearly in the meeting brief and trip planning. Those are thinking problems.

Claude Code shows up when the work needs to persist.

When something moves from a one-time task to something I will do again, I start to think differently. Can this become a tool? Can this run the same way every time? Can I remove myself from the loop entirely?

That is where Claude Code becomes useful.

The conference list if I want a reusable prioritization system. The mailing list if I want a repeatable transformation pipeline. The follow-up process if I want a system that tracks status and cadence.

ChatGPT gets me to the answer. Claude Code helps me build the system.

Most people do not need to start with Claude Code. They need to start with ChatGPT or Gemini and get comfortable handing off pieces of their work. But once a workflow proves valuable, the next question becomes obvious: should this be a one-off, or should it run the same way every time?

That is where the second layer begins and where Claude Code can help you turn that conference attendee list into an AI-based system you can use every time.

Why it matters

When I step back, three things consistently show up: context, clarity, and tools.

Context means gathering everything into one place. Documents, notes, files, screenshots, voice memos. The model works best when it has the full picture.

Clarity means not overengineering prompts. Explain the task, what good looks like, and where you need help.

Tools means accepting that you are not choosing one product. You are assembling your personal AI stack. ChatGPT is my default. Grok for real-time awareness. Gemini for images. Claude Code, when I want to build something repeatable.

Half of people may have tried AI. Most are still using it like Google 2.0.

The shift is learning what can be handed off and what is worth turning into a system. That second step is where things start to compound.

To my friend at the kitchen counter, the answer is not really which AI tool to use. It is time to think differently about your work.

Start with your list and ask a simple question: What can the AI do for me?

Stay ambitious.

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Best of the rest:

🔐 Anthropic’s Mythos Model Is Being Accessed by Unauthorized Users — If frontier models can leak before controlled rollouts even begin, the real bottleneck in AI may be security and distribution, not just raw capability. — Bloomberg

⚙️ Thin Harness, Fat Skills — The real unlock in AI coding isn’t smarter models but better architecture, where lightweight wrappers and reusable “skills” turn LLMs into compounding systems rather than one-off tools. – Garry Tan

🍎 Community Letter from Tim – Tim Cook’s handoff to John Ternus is more than a succession note, it is Apple signaling continuity, stability, and a product-led future at one of the world’s most important companies. – Apple

🤖 Sign of the future: GPT-5.5 – Ethan Mollick’s real point is not just that GPT-5.5 is better, but that the compounding gains across models, apps, and tooling are making AI meaningfully more useful even as the jagged frontier still has not disappeared. – One Useful Thing

🏢 Inside BlackRock’s AI Transformation - BlackRock is moving from AI experimentation to enterprise-wide execution, with a no-code agent platform that lets employees across the firm build and deploy agents, a sign that the real AI race inside large institutions is shifting from models to workflow redesign. - The Wall Street Journal

🚀 xAI Explored Collaborating With Mistral and Cursor — Musk’s AI ambitions are colliding with reality, forcing xAI to look outward for partnerships as the frontier race consolidates around a few dominant players. — Business Insider

☁️ Google Deepens Thinking Machines Lab Ties With New Multi-Billion-Dollar Deal — Google is locking in scarce AI demand early, betting that owning the compute layer for next-gen labs like Mira Murati’s will pay off as model training scales exponentially. — TechCrunch

🧠 The peril of laziness lost — LLMs are supercharging output but eroding the core engineering virtue of thoughtful abstraction, risking a future of bloated systems optimized for speed over simplicity. – Bryan Cantrill

Charts that caught my eye:

→ Why does it matter? All-time highs feel like bad entry points. The data disagrees. Buying at peaks has historically beaten buying on a random day across every time horizon shown, which suggests “wait for a pullback” is often just dressed-up hesitation.

→ Why does it matter? At 75%, Google is approaching a point where human engineers review AI code more than they write it.

→ Why does it matter? The public SaaS market has effectively split into two economies: 96 companies grinding below 10x, and 7 commanding 10x or more. The separating factor looks less like growth and more like AI relevance. If your product isn’t in the labor-replacement conversation, the market is pricing you like a legacy business.

Tweets that stopped my scroll:

→ Why does it matter? This one lives rent-free in my head often: if FTX had not collapsed, its startup investment portfolio would be worth over $100 billion today. 🤯

→ Why does it matter? Thoma Bravo paid $6.4B for Medallia in 2021 and is handing it over to creditors, leaving nothing for equity. 2021 was a tough vintage.

→ Why does it matter? Perplexity’s AI computer looks genuinely so cool. Cannot wait for my Mac Mini to arrive.

→ Why does it matter? Kimi 2.6 is the first open-weight model competitive with top closed models on agentic tasks, at cheaper inference costs. I truly think open source could be the black swan in this whole thing.

→ Why does it matter? Enterprise AI adoption is a services problem as much as a software one. Legacy systems, fragmented data, and change management mean consultants.

→ Why does it matter? OpenAI is moving ChatGPT from a personal tool to a team infrastructure play. Shared agents that persist across workflows put it in direct competition with Zapier, Notion, and enterprise automation software. Rumor has it that Anthropic might be buying Atlassian for this reason.

Worth a watch or listen at 1x:

→ Why does it matter? Rabois has strong opinions on which AI startups are building on borrowed time and which archetypes will actually survive. Many hot takes from someone who has seen enough cycles to back them up.

→ Why does it matter? OpenClaw creator Peter Steinberger takes us back to the transformative moment he let his AI agent loose on the internet, igniting one of the world's fastest-growing open-source projects.

→ Why does it matter? This was one of the most talked-about 1-minute segments of any podcast in the past ~10 days.

→ Why does it matter? Can’t wait!

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Quotes & eyewash:

→ Why does it matter? Seven Dunkin stops turn a legend into a commute. Have to love the USA.

Subscribe now

The mission:

The Wall Street Journal once used “Read Ambitiously” as a slogan, but I took it as a personal challenge. Our mission is to give you a point of view in a noisy, changing world. To unpack big ideas that sharpen your edge and show why they matter. To fit ambition-sized insight into your busy life and channel the zeitgeist into the stories and signals that fuel your next move. Above all, we aim to give you power, the kind that comes from having the words, insight, and legitimacy to lead with confidence. Together, we read to grow, keep learning, and refine our lens to spot the best opportunities. As Jamie Dimon says, “Great leaders are readers.”

Disclaimer: This content is for informational purposes only and does not constitute financial, investment, or legal advice. Readers should do their own research and consult with a qualified professional before making any decisions.

The big idea: What I am teaching my kids about AI

Reading time: 7 minutes

My first-grader and I have been building software to help her learn to spell and type.

She has been doing more of the heavy lifting than you might think. She is six. Give her a whiteboard and Claude Code, and she can now move from idea to software fast.

What fascinates me most, as her dad, is the combination. A six-year-old’s imagination is already wild. Claude turns that imagination into something tangible.

Today, we have a Trolls-centric, Sing 2-themed spelling game with different modes, levels, and mini-game rewards, including her own version of Doodle Jump. And while we wait for Claude to write the code, what do you think we do? We spell the words. Again and again. She has been getting 10 out of 10 on her spelling tests.

When a close friend recently asked me what I am teaching my kids about AI, I found myself coming back to this story.

Not because I think every six-year-old should be building software. And not because I think AI will make the hard parts of learning disappear. It will not. But something has changed. The distance between imagination and execution is shorter than it used to be.

The edge may belong less to the strongest or the smartest, and more to the children who learn how to respond to change without losing themselves, ask better questions, and turn ideas into something real.

That is what I find myself wanting to reinforce at home.

Not a fixed view of which job titles will matter most. Not a prediction about which degrees will age best. Something more basic. A responsible relationship with powerful technology. Character. Curiosity. Communication. Resilience. Judgment.

The new street smarts.

A responsible relationship

I spend a lot of time thinking about what a responsible relationship with technology actually looks like.

With phones and social media, the parental instinct is delay. Hold the line. Buy more time. As a founding member of the Wait til 8 pledge at our daughter’s school, I know that instinct well. It makes sense to me.

But AI feels different.

You may be able to delay a smartphone. I doubt you will be able to delay AI in the same way. AI will not live in one app. It will show up in the software our kids use at school, the devices in our homes, the cars we drive, and the tools they use for work. Software is already everywhere. AI is going to spread through that same surface area.

Which means the job is not just delay. It is preparation.

And preparation, at least to me, does not mean making every decision for them. It means giving them age-appropriate room to make decisions, feel the weight of those decisions, and learn from the outcomes. That is how street smarts are built. Not through lectures alone. Through giving it a go. Through small choices, some good and some bad, while the stakes are still low enough for a parent to help them make sense of what happened.

Too often, what we call protection is really substitution. We make the decision for the child, and then wonder why judgment never strengthens.

I keep coming back to the idea of street smarts. My mom has them. She grew up on the South Side of Chicago. Street smarts meant knowing how to read a situation before it turned on you. Keep your head up. Know who you are with. Pay attention to tone. Leave early when something feels off.

Our kids need a modern version of that.

Not fear. Not techno-doomerism. Good judgment.

They need to know when a confident answer is still wrong. They need to know what information should stay private. They need to know when a tool is helping them think, and when it is doing the thinking for them. They need to know when speed helps, and when speed hides a mistake. They need to know when to close the laptop and ask a human being.

I am nowhere near a complete list. But a few traits keep bubbling to the top, mostly because they help kids build judgment.

The new street smarts

Character.
Every morning on the drive to school, we walk through the Dad Rules. Be kind. Remember people’s names. Treat others how you want to be treated. Do the right thing when nobody is looking.

I ask them because repetition matters. I also ask them because no technology will ever remove the need for character. Powerful tools raise the stakes. AI can help you move faster. It can also help you cut corners, hide bad intent, and avoid responsibility.

Before I care what my kids can do with a tool, I care who they are when they use it.

Curiosity.
A few weeks before we built the spelling game, my oldest walked into my office and pointed at the little door in the ceiling.

“What is that?”

“The attic,” I said.

“Can we go up there?”

“Absolutely. Right now.”

I dropped what I was doing and grabbed the ladder.

Curiosity has a short half-life if adults keep putting it off. Later today. This weekend. Not now. Do that often enough, and a child learns that wonder belongs behind more practical things.

I do not want to teach that lesson by accident.

The world their generation inherits will not hold still for long. The kids who do well in it will be the ones who stay open long enough to notice what changed, ask what it means, and try something new.

Communication.
Speak well. Write clearly. Listen closely. Say what you mean.

More and more, the interface is natural language. The person who can describe what they want, frame the problem cleanly, and hear what is missing has leverage.

My daughter was not just learning words. She was learning how to describe what she wanted to Claude Code. She was learning how to point to an idea that did not yet exist and make it legible enough to build.

There is nothing soft about that skill.

Resilience.
The happy story about AI is that you type a prompt, get a miracle, and move on with your life.

That has not been my experience. The model still gets things wrong. The first version still disappoints. Building software is still messy.

That is part of why I like making things with my kids instead of just handing them polished software. They get to see the real process. You try something. It fails. You revise. You try again. The point is not frustration for its own sake. The point is learning not to fall apart when the first pass is bad. In fact, it’s even better if you can start to love the “bad”.

Curiosity gets you started. Resilience gets you through the broken middle, or what I called a few weeks ago, The Stall.

Knowing which question to ask.
For a long time, a lot of value came from knowing how to answer hard questions with scarce technical skill.

AI is changing the economics of answering questions.

One of the most honest reactions I have seen to recent progress in AI came from someone who had built part of his identity around being very good at math. The models started solving problems that once felt like the province of a tiny elite. What hit him was not just surprise. It was grief.

If your whole identity rests on being the person who can produce the answer, the ground may start to feel shaky. If it rests on knowing what matters, which questions are worth asking, and where to point your attention, you stand on firmer ground.

That is what I saw on the whiteboard with my daughter.

She was not demonstrating mastery of software engineering as a formal discipline. She was doing something more basic, and maybe more durable. She was noticing what she liked. She was imagining what could be better. She was helping shape the thing before it existed. And asking the questions.

That skill will age well.

Why it matters

I am not an absolutist about any of this. I am not an expert on how the next twenty years unfold. I am still making sense of it myself.

But I am starting to know what I want to reinforce in my kids while the ground is moving.

I want them to build. I want them to ask questions. I want them to speak clearly. I want them to treat people well. I want them to stay curious long enough to notice what others miss, and resilient enough to keep going when the first version does not work.

I also want to give them enough agency to feel the consequences of their decisions while the stakes are still manageable. That is how judgment gets built.

I want them to understand that they are more than any one skill the market happens to prize at a given moment. Titles change. Tools change. Status changes. The deeper work is learning how to adapt without losing yourself.

That is what I am teaching my kids about AI.

Not a theory of the labor market. Not a prediction about which degrees will win. Certainly not fear.

Something more basic.

A responsible relationship with powerful tools. Character. Curiosity. Communication. Resilience. Judgment. The instinct to ask better questions.

Those, to me, are the new street smarts.

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Best of the rest:

💸 Beyond the Sky — Jeffrey Yan’s Hyperliquid story shows how a brutally focused team can turn crypto market structure, community distribution, and product obsession into a startup so efficient it makes traditional notions of scale look outdated. — Colossus

🤖 Sam Altman May Control Our Future—Can He Be Trusted? - This New Yorker profile captures the growing tension at the heart of the AI era: one of the most consequential technologies in the world may be shaped by a leader whose ambition, judgment, and governance still inspire deep unease. - The New Yorker

⚙️ How to get your company AI pilled — Ramp’s internal playbook is a vivid case study in what real AI adoption looks like when leadership sets the expectation, removes friction, and treats company-wide experimentation as a cultural advantage instead of an IT project. — X

🛠️ Eight years of wanting, three months of building with AI - Lalit Maganti offers one of the most credible firsthand accounts yet of what AI coding actually does well, what it quietly breaks, and why the real leverage comes from pairing agentic speed with human taste, architecture, and discipline. - Lalit Maganti

Charts that caught my eye:

→ Why does it matter? Nearly half of all private equity deals now target software and tech services, a share that has doubled over the last 15 years and is at an all-time high. This is also what has people concerned about private credit. If AI compresses the value of legacy SaaS products the way SaaS compressed the value of on-premise software before it, the unwind may not be contained to a single fund or vintage year.

→ Why does it matter? A year ago, the question was whether AI revenue would ever show up. Anthropic answered: $87M to $30B in ARR in roughly 15 months. Salesforce took 22 years to cover the same ground.

→ Why does it matter? IBM tracked every shot at the Masters. The result is mostly blue for bogey. Even at golf’s highest level, bogeys outnumber birdies by a lot. Watching the broadcast, you’d never know.

→ Why does it matter? Two years ago, top models stalled at basic reconnaissance. Mythos Preview now reaches the infrastructure compromise threshold with enough tokens. Capable enough to threaten real weakly-defended systems, and the curve is still climbing.

Tweets that stopped my scroll:

→ Why does it matter? McIlroy’s heart rate hit 150 bpm celebrating, higher than his 135 bpm on the 18th tee. Joy is apparently more taxing than pressure.

→ Why does it matter? A user logged actual behavior data showing Claude’s reasoning depth dropped 67% in late February. Anthropic stayed quiet until the numbers were too documented to dismiss. The working theory among many users: Anthropic is throttling Opus to push traffic toward training its next flagship model.

→ Why does it matter? Anthropic posted benchmark numbers that are turning heads with Mythos. This is the first model trained on Blackwells. Buckle up!

→ Why does it matter? Most big-company ideas die by committee veto. One-yes-wins flips the default: contrarian ideas get a fair shot without needing unanimous buy-in up the chain.

→ Why does it matter? Intercom’s R&D team tripled output in 16 months without tripling headcount. That’s the AI productivity story most engineering leaders are chasing right now.

Worth a watch or listen at 1x:

→ Why does it matter? Jesse Genet is running 11 AI agents in the background of real family life. Not a lab. While homeschooling four young kids. Katherine Boyle and Sarah Wang draw out what that actually looks and feels like: the friction, the workflow decisions, and the mindset required to treat agents like an extension of your parental capacity.

→ Why does it matter? Fair warning: This is a highly technical conversation between Dwarkesh Patel and Jensen Huang. But if you want to nerd out on the economics of AI, this is the one for you. I loved Jensen’s explanation of Nvidia: “We turn electrons into tokens”.

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Quotes & eyewash:

“It is not the strongest of the species that survives, nor the most intelligent, but the one most responsive to change.” – Charles Darwin

→ Why does it matter? One to live by with how much change our world is experiencing right now.

“The best way to cherish life is to remind yourself of life's impermanence. It is to remember that every time you see someone that is one less time you see them. It is to remember that everytime you go somewhere that is one less time you visit. By doing this, you naturally slow down. Almost like a reflex, you start to truly live.” - Andrew Anabi

→ Why does it matter? For those of us that don’t live on the coast and aren’t planning to move there, how many times do you think you’ll see the ocean again? Cherish those visits.

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The mission:

The Wall Street Journal once used “Read Ambitiously” as a slogan, but I took it as a personal challenge. Our mission is to give you a point of view in a noisy, changing world. To unpack big ideas that sharpen your edge and show why they matter. To fit ambition-sized insight into your busy life and channel the zeitgeist into the stories and signals that fuel your next move. Above all, we aim to give you power, the kind that comes from having the words, insight, and legitimacy to lead with confidence. Together, we read to grow, keep learning, and refine our lens to spot the best opportunities. As Jamie Dimon says, “Great leaders are readers.”

Disclaimer: This content is for informational purposes only and does not constitute financial, investment, or legal advice. Readers should do their own research and consult with a qualified professional before making any decisions.

The big idea: Always-On Intelligence

Reading time: 7 minutes

Knowledge work carries a hidden tax.

You read an article on Tuesday. By Thursday, it connects to a board conversation, a product decision, or a pattern you are seeing with customers. You know you saved it. Highlighted it. Maybe even sent it to yourself.

And when you need it, you cannot find it.

Most knowledge workers do not suffer from a lack of information. They suffer from a lack of usable memory. The article sits bookmarked in your browser. The notes live in multiple apps. The meeting transcript hides in a folder you forgot about. You earned the insight. But it is scattered across tabs, tools, and your own imperfect recall.

The Second Brain Became a Second Job

We built a whole category of software to solve this. Note-taking apps. Read-it-later tools. Systems for what people started calling a second brain.

I bought the vision. I also bought the tools. I have tried almost all of them.

For me, most of them turned organizing all of this into a second job.

Every new input became another small task. Save it. Name it. Tag it. Link it. Revisit it. Clean it up before the structure gets messy. The tool that was supposed to reduce cognitive load started competing for it.

That is why so many of these systems do not reach their potential. The idea was right. The upkeep was brutal.

Bush Saw the Problem Early

In 1945, Vannevar Bush published As We May Think and described the Memex: a personal system that could store a lifetime of records and let a user move through them by association instead of strict hierarchy. He understood something that still feels true today: the mind does not think in folders. It moves through trails.

Bush could imagine the library. He could not staff it.

Who updates the links when new information arrives? Who decides what belongs together? Who keeps the system current as the archive grows?

For decades, the answer was simple: you did.

AmbitiousOS Changed My Mind

Earlier this year, with the help of Claude Code, I started building AmbitiousOS on top of the full Reading Ambitiously archive. More than 50 subscribers use it today through voice and text. At first, I thought I was building a smarter interface to a body of work.

I now think we’re building a preview of something bigger.

In version one, AmbitiousOS could search the archive, retrieve passages, and answer questions across the corpus.

Version two changed my mind.

Now Claude maintains a living wiki on top of the source material. The original newsletters remain the source of truth. New inputs update pages on companies, people, and concepts. The system adds links, flags contradictions, and writes the changes back into the knowledge base.

The opportunity is an always-on executive assistant with a working knowledge of your world. One that understands your reading, your meetings, your notes, your documents, your decisions, and the threads that connect them. One that feels like you hired your own librarian to build a private Library of Alexandria around your work.

Right now, I still trigger AmbitiousOS by hand. I drop in an article and tell Claude to ingest it. It reads, files, and updates the system in seconds.

Now remove the trigger.

Imagine waking up and the system has already processed the three articles you saved the night before. It tied one to a company you track, another to a theme you have been writing about, and a third to a decision you made last quarter. It noticed that one of them conflicts with an assumption sitting in your notes, and it flagged the issue before your next meeting. It connected these concepts in a recent email you sent.

You did not ask the question. The system did the maintenance, then proactively alerted you.

The Harness Is Becoming the Product

OpenClaw is the clearest open-source version of that idea. A self-hosted gateway that connects apps and chat surfaces to an always-available assistant, with sessions, memory, tools, multi-agent routing, background work, and scheduled jobs. It feels like you gave AI a real operating environment instead of a single prompt box.

Anthropic is building the closed-source version of the same vision in Claude Cowork. Anthropic describes Cowork as a system for autonomous knowledge work that runs on desktop, works across local files and applications, and returns a finished deliverable. It is built around outcomes, not one-time prompts.

Two versions of the same idea. In my view, this could become the iPhone-caliber product of the AI era.

The model still matters.

But the product value is moving into the harness around it: memory, background maintenance, access to the tools where work lives, and skills that turn context into action.

The Missing Layer in Knowledge Work

Knowledge work still runs on the same loop: gather, make sense, retrieve, decide.

We built good tools for gathering. We built decent tools for retrieval. We never built a system that kept the knowledge layer current on its own.

Now we can.

That matters because the cost of broken memory compounds. You lose time. You miss context. You redo research. You walk into meetings with fragments instead of continuity. Over time, your own accumulated knowledge starts to feel less like an asset and more like a pile.

The products that matter next will keep context warm, maintain your knowledge base in the background, and prepare work before you ask. They will return something closer to a deliverable than a response.

Bush imagined the library in 1945. The missing piece was upkeep.

The web gave us infinite information. Search gave us retrieval. These new harnesses point to the missing layer: persistent intelligence that keeps your accumulated knowledge alive while you work.

I started AmbitiousOS by trying to build a smarter interface to a newsletter archive.

I came away with a stronger conviction about where this market is heading.

You do not need more information.

You need an always on intelligent assistant that remembers what you know and gets to work before you ask. And with AI, it’s now possible.

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Best of the rest:

💸 Clouded Judgment Per Token Pricing – Jamin Ball makes the clearest case yet that in AI, pricing is becoming strategy itself, because the winners will be the companies that turn falling inference costs into value-based token and credit models before everyone else does. – Clouded Judgement

⏳ Some Things Just Take Time – Armin Ronacher makes the case that in an AI age obsessed with speed, the real moats are still trust, craftsmanship, and the long, unglamorous work of sticking with something long enough for it to matter. – Armin Ronacher

🤖 OpenAI CEO and CFO Diverge on IPO Timing – The real story is not just IPO timing, but whether OpenAI can outrun the financial strain of a $600 billion compute buildout before public markets start demanding discipline. – The Information

🐢 Thoughts on slowing the f*ck down – A sharp corrective to AI coding hype, this argues that speed without judgment produces brittle software, runaway complexity, and teams that stop understanding the systems they ship. – Mario Zechner

🚀 SpaceX Is Said to Target More Than $2 Trillion Valuation in IPO – This matters because a $2 trillion target would make SpaceX’s debut the biggest IPO ever, and force public markets to underwrite one of the most ambitious growth stories in modern corporate history. – Bloomberg

Charts that caught my eye:

→ Why does it matter? Goldman Sachs found that industries where AI augments workers are adding jobs, while those where AI substitutes for workers are shedding jobs. The unemployment rate for high-substitution occupations is already up more than 1.4 percentage points from its 2022 baseline. The workers feeling the most pressure appear to be entry-level ones, suggesting the rungs at the bottom of the career ladder may be getting harder to find.

→ Why does it matter? The architecture behind one of the most popular apps in live sports: The Masters!

→ Why does it matter? Hat tip to Tey Bannerman for surfacing this one. Microsoft has branded somewhere around 78 separate products “Copilot,” and the chart makes the sprawl hard to ignore: Copilots inside Copilots, Copilots for other Copilots, and a dedicated hardware key on your keyboard to summon them. Will the real copilot please stand up?!

→ Why does it matter? This is the leaked OpenAI cap table (I have no idea if it is fake news). If it's real, it tells a few stories at once. Microsoft’s $13 billion bet is now worth 17.6x, making Satya Nadella’s 2019 call one of the greatest capital allocations of the modern era. The nonprofit foundation is sitting on $220 billion in gains from a $0 cost basis, which is the most unusual wealth creation in corporate history. NVIDIA, despite being the backbone of everything OpenAI builds, is currently underwater on their position. And then there is Ashton Kutcher, who turned $30 million into $1.3 billion in under three years, a 43x return that most professional investors will never see in a lifetime. If this company IPOs at $1 trillion or more, every number on this table increases by 30%, and the nonprofit’s gains become a governance story unlike anything we have seen before.

Tweets that stopped my scroll:

→ Why does it matter? A frontier-class multimodal model, built from the same research that powers Google Gemini 3, is now running entirely on a phone at 40 tokens per second. This could jeopardize cloud-based AI inference. On-device models at this speed and capability level suggest that privacy, latency, and cost constraints that once required a server farm could someday be available locally on the device in your pocket.

→ Why does it matter? The CFO of the world's most valuable AI company is privately raising concerns about spending and IPO timing, and she no longer reports directly to the CEO. Worth watching this relationship closely.

→ Why does it matter? Tony Fadell raises a question worth sitting with: when product management and product marketing operate as separate functions, does the story stay close enough to the product? There is no single right answer to how you organize these roles. But his point is worth understanding. The person who defines what gets built and the person who decides how it gets explained need to be in constant contact, ideally the same person. Greg Joswiak’s superpower at Apple wasn’t marketing genius in isolation. It was empathy deep enough to become the customer, which kept the message and the product aligned.

→ Why does it matter? A year ago, the big question was whether enterprise AI revenue would materialize. Well, here it is. Anthropic added $11B in annualized revenue in a single month. When your million-dollar customer count doubles in under two months, that’s worth paying attention to!

Worth a watch or listen at 1x:

→ Why does it matter? Brad Lightcap is OpenAI’s COO. He covers how OpenAI thinks about the transition from tools to agents, what enterprise adoption actually looks like at scale, and where the real bottlenecks are. Worth a listen!

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Quotes & eyewash:

“If you don’t build your dream, someone else will hire you to help them build theirs.” ‌- Dhirubhai Ambani, Founder, Reliance Industries

→ Why does it matter? Are you climbing the right hill?

Subscribe now

The mission:

The Wall Street Journal once used “Read Ambitiously” as a slogan, but I took it as a personal challenge. Our mission is to give you a point of view in a noisy, changing world. To unpack big ideas that sharpen your edge and show why they matter. To fit ambition-sized insight into your busy life and channel the zeitgeist into the stories and signals that fuel your next move. Above all, we aim to give you power, the kind that comes from having the words, insight, and legitimacy to lead with confidence. Together, we read to grow, keep learning, and refine our lens to spot the best opportunities. As Jamie Dimon says, “Great leaders are readers.”

Disclaimer: This content is for informational purposes only and does not constitute financial, investment, or legal advice. Readers should do their own research and consult with a qualified professional before making any decisions.

Programming note: No issue next Friday. Reading Ambitiously returns April 10.

The big idea: The Stall

Reading time: 6 minutes

The first time I smoked a pork shoulder, the first few hours made me feel smarter than I was.

I bought the meat, got the cooker steady at 225, inserted the fancy digital probe, and watched the internal temperature rise on schedule. For a while, it all felt wonderfully mechanical. Heat goes in, number goes up, dinner gets closer. I cut the lawn. I started imagining the finish before I had earned the right to.

Then the temperature stopped climbing.

Not slowed. Stopped.

It sat there for what felt like an absurd amount of time. Then it barely moved. At one point, I was convinced it had gone backward. I checked the batteries in the thermometer. I tapped the smoker gauge like that might somehow help. Meanwhile, guests were arriving, and the meat was still nowhere near where it needed to be. That is when panic sets in.

Pitmasters have a name for this moment: the stall. It unnerves backyard cooks because the line had been moving so obediently, until suddenly it wasn’t.

That is what makes the stall so unsettling. You did the setup right. You followed the process. The early feedback looked good. Then the signal vanishes, and your confidence goes with it.

What the Stall Really Is

The explanation, at least in barbecue, is simple. As moisture evaporates, it can cool the meat almost as quickly as the smoker heats it. From the outside, it looks like nothing is happening. Underneath, a great deal still is.

The stall is not just a barbecue phenomenon. It is a recurring feature of life and progress. It shows up almost everywhere that the work is real.

Anyone who has lifted weights for more than a few months knows the feeling. Early on, progress is generous. The bar moves. The numbers go up. Effort feels neatly rewarded. Then the curve flattens. The same lift that used to move cleanly starts to feel heavy and stubborn. If you are impatient, you do what the nervous pitmaster does. You force it. You add weight too quickly, let your form erode, and turn a plateau into an injury.

The same pattern shows up in sports, learning, writing, and work. At the beginning of any worthwhile pursuit, improvement is visible enough to keep your confidence high. Then the gains become harder to see. The work continues, but the proof arrives less often and less neatly.

When Progress Goes Quiet

Most plateaus are not dangerous in themselves. What is dangerous is how we interpret them. We mistake a pause in feedback for a failure in process. We assume something is broken because something is no longer obvious, and because we assume something is broken, we start making emotional decisions. In barbecue, that means cranking the heat.

In lifting, it means chasing load rather than technique. In the workplace, it means abandoning a sound process because results are arriving more slowly than your patience would prefer.

The stall is rarely just about progress. It is about psychology. We depend on visible signs that our effort is paying off. When those signs disappear, even temporarily, our judgment gets noisy. The stall asks for continued effort without immediate reassurance.

Systems Over Goals

That is one reason I have become increasingly drawn to the idea of systems over goals.

Goals matter. They give shape to ambition. They tell you what you are aiming at. But goals are often least useful in the middle, and the middle is where most of life is lived. The middle is where the scale stalls. The draft gets worse before it gets better. The business has a quarter that feels flatter than the last three. The visible progress that made the process feel rewarding goes quiet.

A goal is an outcome. A system is a repeatable way of operating. It is the set of behaviors, constraints, and rhythms that still make sense on the days when the scoreboard is silent. Goals tell you where you want to go. Systems tell you what to do this week, especially when the mood shifts against you.

Goals can make you stare at the gap. Systems return your attention to the process. Goals ask, am I there yet. Systems ask, did I run the right play today.

That is why systems matter most in the stall.

When progress is obvious, almost any plan feels motivating. When progress becomes non-obvious, psychology takes over. People improvise. They abandon routines that were working. They reach for intensity when what they needed was consistency. They confuse urgency with wisdom.

What a Good System Does

A good system protects against that. It gives you something sturdier than mood.

It shifts your attention from lagging outcomes to leading behaviors. It lowers the odds that one bad day turns into a broken week. Most importantly, it tells you what to do when the signal disappears. A system does not require you to be heroic every day. It gives you a repeatable way to keep running the play while the deeper work catches up.

That will vary by person and by pursuit. The details are personal. The principle is not. A good system reduces the number of decisions you have to make when you are discouraged.

The better question is not just what outcome do I want. It is what process can I trust when the outcome is not yet visible. Put differently, what kind of operating system will keep me steady when my instincts start telling me to overcorrect.

Your Operating System

This is also where AI starts to feel genuinely useful to me. Most people still approach it as a search tool, a writing tool, or a source of quick answers. It can do all of that. But one of its more practical uses may be helping people build a better system for themselves.

Used well, AI can help you define the inputs that matter, track them, notice patterns across your weeks, and review what is working without having to reconstruct the story from memory after a frustrating day. It can serve as an accountability partner, a thought partner, and a low-friction layer for personal change.

That does not mean outsourcing judgment or borrowing someone else’s routine off the internet. It means more people now have access to an always-on partner that can help them build something highly personal, not a generic productivity system, but their system, shaped around their rhythms, blind spots, and the specific ways they tend to lose the plot when they hit a stall.

One of the best ways to learn how to use AI may be to point it first at the oldest management problem you have: yourself.

Proof Later

With grilling season upon us, I’m thinking about that first cook.

At the time, the stall felt like evidence that I had lost control of the cook. In reality, it was evidence that I did not yet understand the process. The meat was not broken. The thermometer was not lying. The work had simply moved somewhere I could not easily see.

A lot of life is like that.

The hardest moments are often not the beginning, when uncertainty is expected, or the end, when the result is visible. They are the long middles, when effort is still required, but reassurance has gone missing. That is when people start making emotional decisions. That is when they crank the heat.

The stall has made me steadier in those moments, less eager to mistake silence for failure. It has reminded me that some of the most important forms of progress do not announce themselves in real time. Less eager to crank the heat. Consistency first. Proof later.

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Best of the rest:

🤖 More! More! More! Tech Workers Max Out Their A.I. Use. - “Tokenmaxxing” shows how fast AI has shifted from productivity tool to workplace status marker, and why spiraling usage costs may be the next hidden tax of the enterprise AI boom. - The New York Times

💼 OpenAI sweetens private equity pitch amid enterprise turf war with Anthropic - This is the clearest sign yet that the AI leaders are not just selling models, they are building distribution machines to lock up the enterprise before the market settles. - Reuters

🌎 Larry Fink’s 2026 Annual Chairman’s Letter to Investors - Fink’s core argument is bigger than BlackRock, namely that if capital markets are driving more growth and AI is set to concentrate even more wealth, broader ownership, private market access, and tokenized investing start to look less like product innovation and more like economic necessity. - BlackRock

🏢 Productive Individuals Don’t Make Productive Firms - George Sivulka makes the right point at the right moment, namely that AI gains do not compound at the company level until workflows, incentives, and systems are redesigned around the technology instead of simply layered on top of old ways of working. - X

🤖 TurboQuant: Redefining AI efficiency with extreme compression — Google’s latest compression work matters because it points to a future where long-context AI and vector search get dramatically cheaper and faster, without the usual accuracy tradeoffs. — Google Research

Charts that caught my eye:

→ Why does it matter? Thoma Bravo is one of the most sophisticated software investors on the planet, and their thesis here is precise: public markets are pricing software companies on sentiment, not fundamentals. The chart shows the Rule of 40 profile (a measure of real business quality combining growth and profitability) climbing steadily toward 35% even as EV/FCF multiples compress sharply, a divergence that only happens when fear is doing the pricing, not math. The crowd sells the category, the disciplined buyer buys the company. Not all software businesses will recover equally, but the ones with durable unit economics and sticky revenue are trading at a discount that history suggests will not last.

→ Why does it matter? The revenue gap between OpenAI and Anthropic appears to be $6B. It may actually be much larger. Anthropic counts gross hyperscaler revenue before paying out its AWS revenue share, while OpenAI reports net revenue after paying Microsoft Azure. Strip out the accounting difference, and Anthropic's $19B annualized figure shrinks materially. If both companies pursue an IPO this year, the SEC is unlikely to let that comparison stand, and the chart you think you are reading today will look very different once the numbers are restated on a common basis.

→ Why does it matter? Anthropic shipped over 100 product releases in 52 days, averaging roughly two per day, and most users caught only five of them. The pace of AI product development has moved so far ahead of human attention that even power users are operating with an incomplete picture of what the tools can already do.

Tweets that stopped my scroll:

→ Why does it matter? Sierra just shipped Ghostwriter. An agent that builds agents, removing the bottleneck of needing specialized AI talent to deploy customer-facing intelligence. If a platform can now handle voice, chat, multilingual support, and system integrations in one shot, the barrier between "we're exploring AI" and "we have AI in production" collapses.

→ Why does it matter? Guillermo Rauch is the Founder & CEO of Vercel. And his company has quickly become a favorite of AI builders. SORs are highly defensible. UIs not so much. Agents with CLIs and skills. API access. Feels like the future.

→ Why does it matter? Researchers just demonstrated that anonymity online is functionally dead. LLMs can now cross-reference pseudonymous posts against real identity profiles at scale, moving from less than 0.1% to 54% match rates on HackerNews to LinkedIn. Yikes.

Worth a watch or listen at 1x:

→ Why does it matter? Jensen Huang is the most important CEO in technology right now, and this three-hour conversation with Lex Fridman is the closest thing to a masterclass on what it takes to build the infrastructure for a civilizational shift. He covers AGI, scaling laws, the future of coding, data centers in space, and China with the kind of clarity and conviction.

→ Why does it matter? Andrej Karpathy is one of the clearest thinkers in AI, and this conversation with Sarah Guo covers the territory most people are still catching up to: agents that loop, self-improve, and run experiments without a human in the seat. He introduces the idea of the "loopy era," where AI systems don't just execute tasks but iterate on their own outputs, which changes everything about how you build, supervise, and trust them. If you want the mental model for where software development goes next, Andrej has it for you.

→ Why does it matter? Jensen Huang sits down with the All-In crew and goes deep on the inference explosion, physical AI, and what it actually means to run the world's most valuable company during the biggest platform shift in a generation. Jensen thinks deeply about how to put words to some of these big ideas shaping the AI era, and I find those framings so helpful.

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Quotes & eyewash:

→ Why does it matter? Shoutout @rtwlz! The ultimate way to get pumped up before a meeting.

→ Why does it matter? I’m ready for it!

Subscribe now

The mission:

The Wall Street Journal once used “Read Ambitiously” as a slogan, but I took it as a personal challenge. Our mission is to give you a point of view in a noisy, changing world. To unpack big ideas that sharpen your edge and show why they matter. To fit ambition-sized insight into your busy life and channel the zeitgeist into the stories and signals that fuel your next move. Above all, we aim to give you power, the kind that comes from having the words, insight, and legitimacy to lead with confidence. Together, we read to grow, keep learning, and refine our lens to spot the best opportunities. As Jamie Dimon says, “Great leaders are readers.”

Disclaimer: This content is for informational purposes only and does not constitute financial, investment, or legal advice. Readers should do their own research and consult with a qualified professional before making any decisions.

Enjoy this week’s Big Idea read by me:

The big idea: Prompt to Harness: The Future of Software Development

Reading time: 4 minutes

Over time, engineering teams automate more of the mechanics of job and reallocate their effort to where human-only skills matter most.

There was the era of the mainframe, when the work sat closer to the machine. Then came waterfall, which formalized sequencing and handoffs. Then agile, which tightened shortened release cycles. Today, many teams operate in a world of continuous delivery and, increasingly, continuous deployment, where software ships constantly and iteration happens in near real time.

Each shift changed software engineering. More importantly, each shift changed the craft and where to focus.

What teams did not do was stop engineering. They stopped spending time on the parts that had been abstracted away. The locus of value kept moving.

That is what makes the rise of agentic coding feel less like a break from history and more like the next turn in a familiar story.

From Prompt to Harness

The first phase of this AI cycle was prompt engineering.

If we ask the model better questions, we tend to get better answers. Prompting quickly became an important skill for getting useful results from AI. Guides emerged. Websites helped people write better prompts. You can even ask an LLM to help you write one.

But as our ambitions expanded, we moved from one-off outputs to real systems. It became clear that a well-written prompt could still yield weak results if the model lacked the right information at the right time.

Enter context engineering.

In addition to clever instructions, we learned that you also need to protect the context window. Think of context as the model’s memory at runtime. What should the model see? When should it see it? And how do we avoid wasting this precious resource?

A great deal of innovation has gone into addressing context rot, something we wrote about back in January. Tools, MCP, skill libraries, orchestration, and RAG are all, in one way or another, attempts to improve the quality of context.

This was an important deepening of the craft.

Prompting did not go away. It got wrapped inside a growing set of skills and disciplines. We learned that asking well still mattered, but so did supplying the right context, in the right sequence, inside a finite window.

Now the discipline is expanding again.

As the unit of work shifts from answering a question to completing a task over time, the burden moves from the prompt itself to the system around it. Once agents begin taking on longer-running work across multiple steps, tools, and context windows, we also need a harness.

Enter harness engineering: the tooling, practices, tests, guardrails, and feedback loops that keep the system reliable over time.

Once agents are doing meaningful work across files, tools, and dependencies, the quality of the outcome depends less on any single prompt and more on the system wrapped around the model. The harness keeps it on track while the work unfolds.

Prompt, context, and harness engineering are best understood as cumulative ideas. They build on one another. Together, they are beginning to define how software gets built going forward.

That matters because some engineering teams may now be able to increase velocity dramatically, but only if they are willing to redefine the job around these new disciplines.

Over a five-month period, a team at OpenAI built and shipped a product with zero manually written code. The repository reportedly reached roughly one million lines. The application logic, tests, configuration, documentation, and tooling were generated by AI. OpenAI has suggested that it was built in roughly one-tenth the time it would have taken to hand-code.

The engineer’s role in that project appears to have been different.

When something failed, the answer was often not to work harder inside the codebase itself. It was to ask what capability was missing, what artifact was unclear, what test was absent, what feedback loop was too weak, and what part of the environment needed to become more legible to the agent.

This begins to look like the work of a harness engineer.

The deeper shift is that engineering judgment moves from isolated acts of code-writing into the design of systems that can generate, test, and improve code over time.

The Future of Software Development

We may not have fully named this phenomenon yet.

Harness engineering is one candidate. Regenerative software is another. The phrase AI software factories gets at part of the same idea, too. But the label matters less than the shift underneath it.

Prompt engineering, context engineering, and harness engineering increasingly look less like isolated disciplines and more like connected skills of the craft.

Intent goes in. Code comes out. Tests push back. Evaluations refine the path. The harness gets stronger. The next cycle improves.

That is why this looks less like an adjunct to software engineering and more like one path along its next evolution. The craft is not being automated away. It is being relocated toward the design and supervision of the systems that produce software.

There is a historical rhyme here.

Earlier eras of industrialization did not simply replace labor with machinery. They reorganized work around process, measurement, and reliability. Something similar appears to be happening in software, and increasingly across knowledge work more broadly.

Each new layer of abstraction has tended to push more human effort toward the place where judgment mattered most.

Prompt engineering taught us to ask better questions. Context engineering taught us to give the model the right information at the right time. Harness engineering is teaching us that reliability and quality come from the system that wraps around the model, as well as from feedback loops that improve over time.

If that framing holds, the leading software companies are unlikely to treat coding agents merely as point tools. They will begin to reinvent their software development lifecycle around these ideas, because this feels less like a break from history and more like the next turn in a familiar story.

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Best of the rest:

🧠 Tim Ferriss Says Self-Help Might Be the Problem – After 20 years writing and consuming self-improvement content, Ferriss argues the obsession with fixing yourself creates the very unhappiness it promises to cure. – The Blog of Author Tim Ferriss

🤖 Finance Bros to Tech Bros: Don’t Mess With My Bloomberg Terminal – “Keep your stupid AI off my Bloomberg terminal” is the perfect distillation of this whole fight, because it shows how quickly AI hype runs into the hard reality of entrenched workflows, proprietary data, and fanatically loyal power users. – The Wall Street Journal

⌚ How the Swiss Watch Industry Survived by Selling Identity, Not Accuracy – When quartz made precision a commodity, surviving watchmakers pivoted to luxury branding. The same shift is coming for knowledge work. – Paul Graham

🏦 How a $20B hedge fund turned GPT-5 into a senior analyst – Balyasny built a purpose-built AI research engine, evaluated across 12+ dimensions, that reasons through thousands of documents at institutional scale. – OpenAI

🏗️ AI Is Coming for SAP Before It Replaces It – Legacy systems hold decades of critical business data. AI finally makes them programmable, approachable, and worth the upgrade cost. – a16z

Charts that caught my eye:

→ Why does it matter? Last year we were wondering whether the revenue would show up. Well, it did. Anthropic added about $6 billion in annualized revenue in February alone, which is larger than Snowflake’s full FY2025 revenue of $3.6 billion and exceeds Databricks’ last reported $4 billion revenue run rate, suggesting this is not a normal software ramp. It is a sign that when AI starts doing real work, not just helping people use tools, revenue can compound at an incredible pace.

→ Why does it matter? LLMs are starting to tell users to break up, and that should not surprise anyone who understands the data. Train a model on Reddit relationship advice, and you train it on a corpus where a huge share of people default to “leave.” Feed it fifteen years of posts shaped by less patience, less communication, and less willingness to compromise, and the model will learn that exit is the safest, most common answer. The model is doing exactly what the dataset taught it to do.

→ Why does it matter? Software has historically given customers the tools to do the job, which kept the market tied to seats, modules, and trained users. Agents change that equation because the software starts doing the job itself. As these systems develop the skills of their human labor equivalents, the addressable market expands from software budgets to labor budgets, and that is why the TAM gets much bigger.

Tweets that stopped my scroll:

→ Why does it matter? Gokul is describing something most companies still treat as a future concept. A customer problem surfaces in Slack, a coding agent cuts a PR, and 15 minutes later, the fix is live. We did it with AmbitiousOS at 35k feet. That is a new operating model. The companies that build and expand their own software factories over the next 24 months will have a structural velocity advantage.

→ Why does it matter? Ramp just handed AI agents a corporate card with guardrails. Until now, a key missing primitive in agentic systems was not intelligence or tools, it was money. Ramp Agent Cards give AI Agents: spend limits, merchant controls, and full auditability mean agents can now operate in the real economy without a human co-signing every purchase.

→ Why does it matter? Anthropic just shipped the ability to message Claude from your phone and have the finished work return to your desktop. This is the early architecture of an always-on AI collaborator that works while you sleep, commute, or sit in back-to-back meetings. The race to own the persistent AI relationship, one that knows your context, your files, and your intent across devices, is a real platform war of this era.

→ Why does it matter? The barriers between “I have an idea” and “here is a thing you can click” are collapsing fast. Google Stitch is a new product from Google that helps you get to prototypes quickly.

Worth a watch or listen at 1x:

→ Why does it matter? Dylan Patel breaks down the three physical constraints that will determine who wins the AI era: logic, memory, and power. If you want to understand why AI scaling is not simply a money problem, Dylan Patel and Dwarkesh Patel are two of the brightest minds on the subject, oh, and they are cousins.

→ Why does it matter? Jensen Huang does not do keynotes, he does roadmaps, and this one covers the full arc from Blackwell to Rubin to Feynman while making the case that AI factories are the defining infrastructure build of our era.

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Quotes & eyewash:

→ Why does it matter? 10+ years in the making.

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The mission:

The Wall Street Journal once used “Read Ambitiously” as a slogan, but I took it as a personal challenge. Our mission is to give you a point of view in a noisy, changing world. To unpack big ideas that sharpen your edge and show why they matter. To fit ambition-sized insight into your busy life and channel the zeitgeist into the stories and signals that fuel your next move. Above all, we aim to give you power, the kind that comes from having the words, insight, and legitimacy to lead with confidence. Together, we read to grow, keep learning, and refine our lens to spot the best opportunities. As Jamie Dimon says, “Great leaders are readers.”

Disclaimer: This content is for informational purposes only and does not constitute financial, investment, or legal advice. Readers should do their own research and consult with a qualified professional before making any decisions.

Enjoy this week’s Big Idea read by me:

The big idea: Trust is the rate limiter on risk transfer

Reading time: 3 minutes

By the simplest definition, business is the exchange of risk. That’s it.

For that exchange to take place, the most essential ingredient is trust.

The world is adopting AI at a speed we rarely see in technology. And I am optimistic. This is a new operating system.

At the same time, human nature has its own operating system, older than markets and older than modern institutions. One of its core jobs is deciding when reliance is safe.

With AI, we can rapidly go from idea to artifact.

Still, business isn’t the exchange of artifacts. It’s the transfer of risk. When a buyer chooses you, they’re not only buying your product, they’re choosing who they can rely on when the stakes are real.

Maybe that sounds obvious or trite. And you’re saying “duh, Jack”. I think it’s easier said than done.

Evidence

There is an explosion of new companies, and news every day about how fast they’re growing. Speed is impressive, and velocity is important. But can trust be established at the same pace, especially when the risk being handed over is meaningful and priced accordingly?

I’ve worked for two companies in my career: IBM and Ridgeline. Both were shaped by founders who understood this deeply, Thomas Watson and Dave Duffield. IBM is one of only a handful of companies in the “century” club. Dave is one of a small number of founders to have built multiple companies that went public and entered the S&P 500.

Amongst many things, I think they both understood that technology is important, but it’s rarely the deciding factor on its own.

Imagine for a second if you gave peak-era IBM sales teams today’s AI copilots, agents, and automation tools. They’d sign customers up. They’d probably crush sales targets. But the deeper advantage wouldn’t be the tools. It would be the culture and the way customers learned to rely on IBM over time.

Lately, I keep thinking about that point as the x-factor in company building.

Trust Engine

My first year on the job at IBM was spent in training programs designed to teach employees how to show up as an IBMer. At the first official meeting I ever attended with Dave Duffield about what would become Ridgeline, core values were the first order of business.

Buffett and Munger seemed to understand this too. If you read their letters carefully, the “people” part is foundational to their investment thesis.

So what do we do about it?

The takeaway for me is not “slow down.” It’s “build the trust engine as deliberately as you build the product.” Put the same care into culture and people as you put into the shiny objects, especially right now when so many of us are trying to figure out AI.

When I say “trust engine,” I mean a set of core values, practices, and culture that make you consistently reliable.

Because the decision to hand over risk won’t be made on demos and feature comparisons alone. It’s made on pattern recognition. Who has shown up consistently? Who has history? Who understands the domain beyond the slide?

That’s what I mean by trust turning into something personal, almost emotional, in business relationships.

Rate Limiter

AI makes it a better time than ever to launch a new product or start a company. You can build faster, iterate faster, and learn faster.

But the thing you’re really asking a customer for is still the same. You’re asking them to trust you enough to underwrite risk with you.

That’s why trust is the rate limiter on risk transfer. You can accelerate the build cycle and generate evidence sooner. Reliance still takes time, and it still gets earned the old-fashioned way: by doing what you say you’re going to do.

And that’s my personal favorite definition of the word trust.

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Best of the rest:

💼 OpenAI Selects Law Firms Cooley, Wachtell for IPO Prep – The company has taken its first concrete step toward a potential public listing that could rank among the largest in history, signaling that the AI leader may soon test public markets at a pivotal moment for the industry. – The Information

🤖 Meta acquired Moltbook, the AI agent social network that went viral because of fake posts — Meta just bought one of the internet’s strangest experiments in AI-generated social activity, signaling that even chaotic, synthetic engagement can be strategic when you are racing to define the future of online interaction. – TechCrunch

🗣️ The stranger secret: how to talk to anyone – and why you should — In an age of headphones, phantom phone use, and social caution, this piece argues that small, low-stakes conversations with strangers are not awkward relics but essential practice for rebuilding our social muscles and civic trust. – The Guardian

Charts that caught my eye:

→ Why does it matter? Curious what websites ChatGPT is visiting to help source its response?

Tweets that stopped my scroll:

→ Why does it matter? This feels like the future user experience of how we’ll use computers. Bravo, Perplexity!

→ Why does it matter? This is a crazy story, and it’s the second time I’ve heard about an incredible entrepreneur asking someone they wanted to hire, “Where are you?” and then immediately going to see them. There’s a great story in Elon’s biography about him doing the exact same thing. It makes you think. Is there anyone in your life you need to go see right now?

→ Why does it matter? An example of a skill called cost-estimate that I really like. An easy one for anyone to replicate. And a great way to convey the ROI on investing in these tools.

→ Why does it matter? This was one of the most discussed tweets on the internet this week, and it captures an important idea: recursive learning. A human writes the strategy document. An agent runs experiments autonomously. A clear metric determines what stays and what goes. Then the loop runs again and again, compounding while you sleep.

→ Why does it matter? I cannot wait for Claude to nail generating graphics and animations. It’s a tough use case for AI right now. Everybody is trying to get AI to generate slides. There isn’t really a clear leader. This is progress.

Worth a watch or listen at 1x:

→ Why does it matter? To me, Coatue is saying this is a regime change, and they are investing as if it were. They do not assume the Magnificent 7 will remain the Magnificent 7; they keep their own roster, the “Fantastic 40,” a curated list of companies they believe can lead in an AI-driven world. That mindset, in their opinion, makes the SaaS repricing feel inevitable.

→ Why does it matter? What I take from this conversation is a very practical theory of where agents actually work. Code repos are unusually agent-friendly, they are text-heavy, versioned, and full of built-in feedback like tests, which is why coding agents have sprinted ahead while most “knowledge work” still feels squishier. Taylor’s “harness” idea is the missing layer, turn a workflow into something repo-like with documentation, rules, tools, and even a blunt memory system, sometimes as simple as a directory of markdown files.

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Quotes & eyewash:

→ Why does it matter? Connect Claude Code to your Apple Vision Pro. Now we’re talking.

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The mission:

The Wall Street Journal once used “Read Ambitiously” as a slogan, but I took it as a personal challenge. Our mission is to give you a point of view in a noisy, changing world. To unpack big ideas that sharpen your edge and show why they matter. To fit ambition-sized insight into your busy life and channel the zeitgeist into the stories and signals that fuel your next move. Above all, we aim to give you power, the kind that comes from having the words, insight, and legitimacy to lead with confidence. Together, we read to grow, keep learning, and refine our lens to spot the best opportunities. As Jamie Dimon says, “Great leaders are readers.”

Disclaimer: This content is for informational purposes only and does not constitute financial, investment, or legal advice. Readers should do their own research and consult with a qualified professional before making any decisions.

Enjoy this week’s Big Idea read by me:

The big idea: AI Product Strategy for Model Consumers

Reading time: 9 minutes

Most of our readers are not training AI models. We’re building software that consumes them.

That sounds obvious, but it changes where you place your bets.

If you are not building the brain, your job is to build a system around the brain. The part customers pay for. The part that compounds into durable value. The part that stays defensible when the underlying LLM makes meaningful improvements.

One way to make sense of this world is to split it into two camps: Model Producers and Model Consumers.

Model Producers train frontier models, and increasingly, they are shipping products on top of them. In the American closed-source world, that center of gravity sits with OpenAI, Anthropic, and Google. You can make a case that xAI belongs in the conversation, too.

Model Consumers are the rest of us. Mostly software and services companies that rent intelligence and build products around it.

If the brain is rented, what is the most defensible way to build the product around it?

Let’s talk about the brain first.

The Big 3

You know their apps: ChatGPT, Claude, Gemini.

These labs are no longer just building large language models. They are building across three layers: models, apps, and harnesses.

Models are the brains. They determine reasoning, writing, coding, and multimodal perception. They are what benchmarks measure and where labs are in a close race.

Apps are the front door. Apps are the products you use to talk to a model, and how models increasingly complete work for you. More and more, they sit inside productivity suites, developer tooling, and operating systems.

Harnesses are what turn intelligence into work. Apps come with a harness. They give models tools, permissions, file access, logging, memory, guardrails, and the ability to execute multi-step tasks. A harness is the difference between answering a question and completing a job.

If “harness” feels old-fashioned, think of an exoskeleton. You are not adding intelligence. You are channeling the raw cognitive power of an LLM. The harness, or exoskeleton, directs that power toward output you can rely on.

Claude in a chat window is one experience. Claude inside a coding harness is another, because that environment is designed to read a repo, edit files, and run commands. Claude, as a desktop co-worker, is another, because it has local file access and the autonomy to complete tasks.

This matters because Model Producers are not stopping at models. They cannot, because models are quickly becoming commoditized.

They are building apps and harnesses, too, because that is where distribution, retention, and margin live. To quote Jeff Bezos, “Your margin is my opportunity.”

That is why the AI labs are moving into territory that used to be exclusively the domain of software companies.

And if producers are climbing into the app and harness layers, model consumers need to know what they can still own.

Five places defensibility lives

If you are a software company, you are a Model Consumer. You rent the brain. You build around it. This is typically referred to as “wrapping” an LLM.

Here is the simplest stress test.

If your model API disappears, what is your IP?

Here is what I’m seeing.

1. Integrations: The unglamorous work. Connectors, schema mapping, permissions, and data normalization. In the enterprise, connectors are a product. If your AI cannot reliably access the right data with the right permissions, you have a demo, not a deployable system.

2. Proprietary data and modeling: LLMs train on public data. What dataset do you own that is not public, and how do you make it legible to the model? This is ontology in practice. A representation of how the business works that the model can reason over safely.

3. Tools and plugins: These are the proprietary actions you expose to the model, APIs, workflows, and system updates inside your product. Instead of just generating language, the LLM can execute real tasks within your software.

4. Skills: Reusable workflows, instructions, playbooks. Enterprises do not have one workflow. They have hundreds. A skill library that starts small compounds institutional knowledge over time.

5. Memory: If the system learns how your team works, replacement gets expensive. Memory turns a generic assistant into something closer to an institutional colleague.

If you want one organizing principle, it is this: everything a Model Consumer builds should ladder to one or more of these five.

Once you’ve made that call, the product strategy gets clearer.

Four product strategy plays for model consumers

You do not need to be a Model Producer to win. You need a playbook that maps to what you can own. Here are four repeatable, product-level plays. Each is a different way to own an app, a harness, or both, while renting the model.

1. Universal AI Assistants: The front-door play, owning the app layer. The product wants to sit where users already spend time. Microsoft Copilot is the canonical example because it lives inside Microsoft 365 and draws on organizational context. Glean is another, built around permission-aware connectors and enterprise search. Their defensibility is distribution, integration, and grounding.

2. AI Agents: The outcome play, owning app plus harness. Agents take responsibility for slices of work. They have tools, entitlements, audit trails, and multi-step execution. Convert services into software. The closer you get to outcomes, the more pricing shifts from seats to results. Maybe #6 IP opportunity here, training your own small language model.

3. Enterprise-Grade Controls: Permissions, governance, compliance, observability, auditability. Enterprises will not deploy AI at scale without trust and transparency they can measure and defend. Build it for them.

4. Embedded AI Features: The compounding play, owning workflow depth inside your existing product. AI becomes a feature set inside existing workflows. Summarization, triage, autofill, exception handling, and natural language query. Many categories will be won by the product that becomes meaningfully easier to use thanks to AI.

Why this matters

Frontier models will keep getting better. At the same time, they will converge and become harder to tell apart.

The labs know this. That is why they are investing so heavily in apps and harnesses. They are moving up the stack into territory that used to be exclusively the domain of software companies.

If you are not building one of these brains, but building around one, you must be explicit about where your differentiation lives.

Decide where your intellectual property sits. Integrations. Proprietary data and ontology. Tools. Skills. Memory. Pick a primary bet(s) and build around it.

Because here is the uncomfortable truth.

As base models improve, some products will wake up and discover that what they thought was a moat was simply proximity to the frontier model. When the underlying intelligence gets meaningfully better, parts of the value proposition compress overnight.

I’m told Larry Ellison once said something to the effect of, “Your company is a feature on my platform.”

In an AI world, that is the risk.

If what you built can be absorbed into someone else’s app or harness the moment the brain improves, you were never building a platform. You were building a feature. And borrowing the intelligence that powered it.

Make sure you own something that survives when the brain gets smarter next week.

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Best of the rest:

🏦 Howard Marks No Longer Sitting on the Fence – The Oaktree founder went looking for an AI bubble, used Claude to build his own tutorial, and came back convinced this is real. – Robert Dee ACSI

🤖 There is exactly one way that SaaS can be saved — Eoghan McCabe argues that survival in the age of agents requires radical creative destruction, detailing how Intercom cannibalized its own legacy business, bet the company on AI, and reignited growth by rebuilding around agents from the ground up. – Eoghan McCabe

⚖️ Anthropic Picks a Fight It Cannot Win – Ben Thompson argues Anthropic’s standoff with the Department of War is a principled but ultimately untenable position, disconnected from how power actually works. – Stratechery by Ben Thompson

👨‍💻 We stress-tested GPT 5.4 on the hardest UI on the internet – After months inside legacy insurance portals, Pace found GPT-5.4 can finally click accurately, reason across hundred-step workflows, move fast enough for production, and retain UI memory, signaling that computer-use agents are crossing from demo to deployment. – Jamie Cuffe

Charts that caught my eye:

→ Why does it matter? This is a wild chart if true. Anthropic is becoming the dominant player in business AI spend in just 18 months, with Claude Team alone now accounting for nearly half of all enterprise AI chat dollars.

→ Why does it matter? The Financial Times distilled the entire AI debate into a single chart, and the punchline: the most likely outcome is not singularity or extinction. It is a modest 0.2 percentage point bump to trend growth, the same steady line that has held since 1870. That is the base case. The dramatic curves, up toward abundance or down toward human extinction, are the tails. Most of the AI conversation lives in those tails. Most of the evidence points to the middle.

→ Why does it matter? An interesting chat from Anthropic here. The gap between blue and red is the most important story in AI adoption right now. Across nearly every occupational category, AI can theoretically handle a substantial share of job tasks, but observed usage barely registers. Even in the fields leading adoption, legal, software, business, and finance, actual usage sits at a fraction of what is already possible today.

Tweets that stopped my scroll:

→ Why does it matter? On the evening of the Super Bowl, a rumor circulated about a leaked OpenAI commercial featuring a mysterious new device. I referenced it in that week’s edition of Reading Ambitiously, only to discover there were no confirmed reports of such a spot. For the first time, I felt like I had been pulled into fake news, so I removed the reference before publishing. Then, last week, that same device appeared in Joe Gebbia's hands. Call it conspiracy theory corner if you’d like, but when Jony Ive was at Apple, he prized secrecy and understood that the ecosystem feeds on leaks. The anticipation was part of the product. It is hard not to wonder whether this, too, is part of a similar playbook.

→ Why does it matter? Could a MacBook drop from over $1,000 to a projected $80 by 2081, while a McDonald’s burger climbs toward the same number? The things we build with silicon get cheaper every decade, while the things we grow, staff, and serve in person get more expensive.

→ Why does it matter? Qwen 3.5 running on-device on an iPhone 17 Pro is a big deal. When frontier-class reasoning runs locally, without a cloud call, without latency, without a subscription, the economics of AI applications change completely. This would finally mean Apple could fix Apple Intelligence! The edge is becoming the new data center, and the phone in your pocket is the new inference engine.

→ Why does it matter? Google just shipped a CLI for its entire Workspace suite. Instead of clicking through polished dashboards, the most productive users are dropping back to the terminal with Claude Code. 2026 feels like the year of CLI’s and skills.

Worth a watch or listen at 1x:

→ Why does it matter? Chris Degnan was employee #13 at Snowflake and the only person to hold the CRO seat across all four CEOs and $3.5B in ARR. He refuses to sugarcoat what scaling a sales org actually requires: relentless travel, paranoia about competition, and staying close enough to the front lines that you could close a deal yourself on any given Tuesday. He warns against becoming a “spreadsheet manager,” a CRO who hides behind dashboards until the org quietly stops trusting them.

→ Why does it matter? John Arnold built one of the most successful trading operations in history, then stepped away to focus on philanthropy. In this conversation, he applies that same systems-level thinking to China’s manufacturing velocity, the hard economics of the energy transition, and what AI-driven demand means for physical infrastructure. His observations on China’s industrial capacity alone are worth the time.

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Quotes & eyewash:

→ Why does it matter? has become one of my favorite recent follows. She is an entrepreneur who is now using AI to homeschool her kids, and the way she applies the most advanced tools to personalize their education is so cool and hands-on. This week, my six-year-old and I walked through a lightweight software design session together. A few prompts later, we had built a spelling test game in Claude Code, complete with Trolls. Thanks for the inspiration, Jesse.

Subscribe now

The mission:

The Wall Street Journal once used “Read Ambitiously” as a slogan, but I took it as a personal challenge. Our mission is to give you a point of view in a noisy, changing world. To unpack big ideas that sharpen your edge and show why they matter. To fit ambition-sized insight into your busy life and channel the zeitgeist into the stories and signals that fuel your next move. Above all, we aim to give you power, the kind that comes from having the words, insight, and legitimacy to lead with confidence. Together, we read to grow, keep learning, and refine our lens to spot the best opportunities. As Jamie Dimon says, “Great leaders are readers.”

Disclaimer: This content is for informational purposes only and does not constitute financial, investment, or legal advice. Readers should do their own research and consult with a qualified professional before making any decisions.

The big idea: Top 5 lessons learned vibe coding AmbitiousOS

Reading time: 6 minutes

Fourteen days ago, AmbitiousOS was a Friday-night idea. Today, it is live in production, running a private beta with about 20 subscribers. Two weeks from idea to production would have sounded unrealistic not long ago. With Claude Code, it is the new normal.

Here are the top five lessons I learned building AmbitiousOS with Claude Code over the last two weeks.

The AI is a shadow of its creator

“An institution is the lengthened shadow of one man.” - Ralph Waldo Emerson

AI output is downstream of a dataset, and datasets are curated by humans first.

The intelligence is not new, at least not yet. It is relocated, packaged, and distributed through LLMs.

AmbitiousOS is constrained exclusively to my personal research and writing. Over the last two years, I’ve curated thousands of pieces of material that make up the dataset the model retrieves from. Before a single token is spent, the dataset has already been filtered and weighted by my human brain.

This matters because the “artificial” intelligence is an extension of the curator. It is both surprising and not surprising that AmbitiousOS ended up resembling its creator. Growing up, I was the person family members asked to set up their computers. I’ve always gotten joy out of explaining technology to others. Reading Ambitiously is that impulse in newsletter form, and AmbitiousOS is what it feels like when you turn that same impulse into an AI.

This is a lesson for anyone building or investing in AI. “AI for [insert your best idea]” is exciting, but you should look even harder at the team behind it. The best systems are built in the image of creators who care deeply about why the system exists in the first place.

The full power of a database, and I love it

I am not a trained database analyst. Claude built AmbitiousOS on Postgres, and it is astonishingly powerful. If Excel is one way to interact with data, this is a different category entirely.

Query, transform, validate, and refactor schema. I can do it all by talking to Claude with my voice. Databricks reported that AI agents now create 80% of new databases in Neon (their Postgres offering) and 97% of database branches. No surprise to me.

Working in the terminal, while intimidating at first, might be the most pleasurable UX I’ve ever used. Claude Code with direct database access, programmatic access via APIs and MCP to other systems, and the ability to speak in English and build highly personalized software. That is awesome.

Vibe coding a dashboard is one thing, getting to production is another

It is one thing to vibe code an HTML front end. It is another thing to ship software.

At one point, Claude said, “Let’s check the logs.” I went looking and realized I didn’t really have logging infrastructure. There is a lot of work that goes into manufacturing software. Yes, I could ask Claude to invent something, wire it up, and ship it. But there is a big difference between a prototype and a product.

And once you get to a product in production, it is a lot of work. I spent 2 days on the sexy features and 12 on security, deployment, maintenance, and bugs.

If you’ve spent your career in enterprise software, you already know the iceberg. AI compresses the visible work, but it does not eliminate the invisible work.

A lot of what we see AI producing is incredible demos, parlor tricks, and the “wow” moments.

What most people do not see is the manufacturing line required to get to production: security and permissions, secrets management, environments, deployments and pipelines, observability, auditability, regression testing, incident response, rollback, and the daily maintenance that starts the moment real users show up.

Most of my feature development happened last week. This week has been dominated by ensuring a safe transition to production. Claude has been a coding partner, but also something closer to a CISO and SDLC partner, pushing on infrastructure assumptions, reviewing flows, and helping test the deployment, not just the sexy features that’ll drive sign-ups.

35,000 feet

Earlier this week, one of our private beta testers wrote in to say they couldn’t log in via our authentication workflow. They sent an email with details, plus a screenshot. I happened to be on an airplane, and I fired up Claude Code at 35,000 feet on United’s Wi-Fi. I gave Claude the email and the bug.

It took a couple of iterations, but Claude quickly identified the changes we needed to make. We wrote a fix, pushed it to GitHub, and redeployed the application in a matter of minutes, all from 35,000 feet.

Then Claude drafted the reply, and I sent it to the user to let them know the fix was in. They were able to log back into AmbitiousOS.

It’s a wild time to be alive. At the same time, now that we’re live, we have an obligation to our users.

The roadmap is more AI-native, without getting sloppy

What comes next for AmbitiousOS is more AI-native capability.

I want to give it a voice. I want to give it a phone number. I want you to talk to it on WhatsApp without logging into a browser. On the roadmap are things like an MCP server and a more conversational interface, plus the less glamorous work that makes those experiences safe: persistence infrastructure, chat history, stronger security controls, and a lot more hardening.

There is an old trap in software. People assume all progress is feature progress. But once you ship, you cannot spend 100 percent of your budget on features. You are now running a software company. Focus on the visible work that excites users, while also focusing on the invisible work that keeps them safe, in a reliable, available way that brings them back.

It’s a new world. Be safe out there.

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Best of the rest:

🙈 Anthropic Quietly Dropped Its Hardest Safety Commitment – The most safety-conscious AI lab just scrapped its core pledge to never release models without guaranteed risk mitigations in place. – TIME

🎙️ Robert Caro on Power, Process, and the Man Who Reshaped New York – The author of The Power Broker joins 99PI’s epic mini-series breakdown of his masterpiece. Caro on Robert Moses is worth every minute. – 99% Invisible

🔗 Block shares soar as much as 24% as company slashes workforce by nearly half – Jack Dorsey cut nearly half his workforce, 4,000 people, because “AI” can now do the work. Or maybe he just way overhired during COVID. Shares soared 24%. – CNBC

💾 AI Is Bringing the Thin Client Back, and This Time It Might Stick – The PC and mobile era favored thick clients, but AI flips the logic: intelligence lives in the cloud, not the device. – Stratechery by Ben Thompson

👨‍💻 The Future of Software Engineering – A look at how AI is redrawing the boundaries of what it means to build software, and who gets to do it. – ThoughtWorks

Charts that caught my eye:

→ Why does it matter? Vertical Industry Software is the only category breaking above its 8-quarter average on both quota attainment and inbound lead sentiment. At the same time, everyone is debating whether vertical software has a future. The numbers say the opposite of the bear case: reps are hitting quota at rates not seen since early 2022, and inbound lead flow is at a multi-year high heading into Q4 2025. This is what a category looks like when the market starts to believe the moat is real, not theoretical.

→ Why does it matter? The top decile of AI-native software companies now generates roughly $650K in ARR per employee, up from under $400K just a year ago. Productivity gains from AI?!

Tweets that stopped my scroll:

→ Why does it matter? Bill Gurley is flagging the same trap that burned investors in 1999: revenue growth that looks spectacular until you ask what it actually costs to generate. If AI companies are buying compute from Nvidia and reselling it at a loss to win customers, the top-line numbers are a mirage.

→ Why does it matter? Perplexity Computer orchestrates multiple agents from OpenAI & Anthropic. GPT-5.2, Claude Opus, Claude Sonnet, running in parallel on a single financial workflow, each assigned to the task it handles best.

→ Why does it matter? As we wrote in RA (Feb 6, 2026), I think Konstanine is right: we will see consolidation and the biggest players will get bigger. But at the other end of the barbell, a key group of domain experts will matter even more, especially in fields where 90% accuracy is not good enough.

→ Why does it matter? This is Salesforce acknowledging something important. And sharing a glimpse into SalesForce, Slack & Claude working together. But where is AgentForce?!

Worth a watch or listen at 1x:

→ Why does it matter? Dan Sundheim is one of the all time greats in investing today. In this episode of ILTB, he lays with Patrick O’Shaughnessy exactly why he is making concentrated bets on Anthropic, OpenAI, and SpaceX. He talks about how his firm effectively invests across both public and private markets.

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Quotes & eyewash:

→ Why does it matter? Silicon Valley was so far ahead of its time. Must watch.

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The mission:

The Wall Street Journal once used “Read Ambitiously” as a slogan, but I took it as a personal challenge. Our mission is to give you a point of view in a noisy, changing world. To unpack big ideas that sharpen your edge and show why they matter. To fit ambition-sized insight into your busy life and channel the zeitgeist into the stories and signals that fuel your next move. Above all, we aim to give you power, the kind that comes from having the words, insight, and legitimacy to lead with confidence. Together, we read to grow, keep learning, and refine our lens to spot the best opportunities. As Jamie Dimon says, “Great leaders are readers.”

Disclaimer: This content is for informational purposes only and does not constitute financial, investment, or legal advice. Readers should do their own research and consult with a qualified professional before making any decisions.

The big idea: Building AmbitiousOS v2 + Total Recall & Memory + AI Agents w/ Claude Code

Reading time: 8 minutes

On Friday night, I pulled a stack of journals off the shelf.

Throughout my career, I have taken many notes. Spiral notebooks. Hardbound journals. iPad note-taking apps. Evernote. The notebook I took everywhere in the first 200 days of Ridgeline. Every person we met. Their advice. Our goals.

Reading Ambitiously is an extension of that habit. One hundred and one editions of how technology, markets, and ambition are evolving. In many ways, it is my public journal.

And on Friday night, I had an idea.

What if subscribers could talk to it? Not search it. Talk to it. What is Reading Ambitiously? Could it become a part of their team?

In about 30 minutes, Claude and I had a plan. We built it, and we’re going to make it available to you.

Memory

Memory is a funny thing. The human brain is extraordinary at pattern recognition and terrible at retrieval.

Every technology era believes it has solved the knowledge problem. In the early 2000s, we built document management systems. Taxonomies. Keyword indexing. Then came the “second brain” movement.

All useful. All still dependent on one thing: you had to remember the path. And spend a lot of time gardening to keep it clean.

Search assumes you know what you are looking for. Memory allows you to ask what you have forgotten.

The latter of which AI is very good at.

From Newsletters to AmbitiousOS v2

There are tools that claim to do this (Glean, Notion AI, various “chat with your docs” products). I have tried a few. They feel heavy. You do not quite understand what they are retrieving or why. Plus, I wanted to do this to understand what’s going on technically here.

The first step was building a vector database. If you are new to that term, think of it as a way to store meaning instead of keywords. Each paragraph becomes a mathematical representation (an embedding) that captures its semantic shape. Similar ideas sit near each other in that space, even if the words are completely different.

Every edition of Reading Ambitiously was pulled from Substack. Not easily, I should add. But we wrote code to fetch the full archive, then chunked each edition into smaller sections: roughly 300 to 900 tokens at a time, preserving headings so the structure remained intact. Each chunk was embedded and stored with metadata (title, date, tags, entity references).

In our first load, we went with all editions available on Substack, which is 59. About 5,000 chunks. Indexed and stored.

From there, we layered on more intelligence and tooling:

• A retrieval system that searches by meaning.

• A temporality layer that detects when you are asking how your thinking has evolved, and sorts results chronologically.

• A knowledge graph that maps 460 entities (companies, people, concepts, frameworks) and how they connect across editions. Claude read every piece of content, extracted the entities, and built the graph automatically.

• An AI agent that does not just search once and respond. It decides which tools to use, searches the archive multiple times, checks the knowledge graph for connections, and synthesizes everything into a grounded answer with citations.

• Persistent memory so the system does not reset every session.

If I’ve lost you in the technical weeds, I’m sorry! This is technical. But what mattered was what happened next.

The Moment It Clicked

I asked a simple question: “Tell me every time I’ve mentioned AI pricing.”

The system did not just return quotes. It synthesized across years. It cited the original editions. It highlighted the first mention and recent usage. It showed how the framing shifted. In the UI, I could see the agent working: “Ambitiously thinking...” with a timeline of each step (searching the archive, looking up entities, finding connections) leading up to the answer.

A few seconds. Doing this manually across 100+ editions would have been an afternoon.

“I have a meeting about AI agent pricing tomorrow. Refresh my thinking: what have I written, what frameworks apply, and what is my current position?”

It pulled relevant chunks from the archive, cross-referenced the knowledge graph, and gave me talking points. Not generic AI-generated ones.

Then I asked one more: “How has the thinking on enterprise SaaS moats evolved over time?”

It detected the temporal nature of the question, sorted everything chronologically, and showed me how my position developed. Early editions versus recent ones. Where I changed my mind. Where I doubled down.

It felt less like a search and more like total recall. Search finds a file. Memory has a conversation with your own thinking.

Near-Perfect Memory, With Limits

Software is moving from tools you open to systems that respond to intent. This is the personal version of that shift. You do not open a folder. You ask. And because the corpus is yours, because the embeddings are legible, because you can see exactly which editions it pulled and why, you understand how it works. Feels like magic, but it’s AI!

The implication is this: near-perfect memory will belong to those who choose to document.

If you want leverage over your own thinking five years from now, you need to give your future self a corpus. Journals. Investment memos. Product specs. Friday notes. The format matters less than the habit. Write it down.

The model is not the asset.

The archive is.

We’re launching in private beta today. The plan is to make AmbitiousOS generally available to Reading Ambitiously subscribers next week. If you’re interested, please reach out.

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Best of the rest:

🤖 Introducing Agentic Wallets: Give Your Agents the Power of Autonomy - Coinbase is productizing the missing piece for real agent autonomy, wallets with built-in skills, spending guardrails, and the x402 rails so agents can pay, trade, and operate without waiting on humans. - Coinbase Developer Platform

🔋 The AI Productivity Paradox – An engineer who builds AI agent infrastructure shipped more code than ever last quarter. He was also more exhausted than ever. The two facts are connected. – Siddhant Khare

🐝 Anthropic’s Talent Density Is NFL-Level – Steve Yegge talked to 40 Anthropic employees and came away convinced they’re building a “hive mind” for software development. Harder to get hired there than making the NFL. – Medium

Charts that caught my eye:

→ Why does it matter? This is the fastest revenue ramp in enterprise software history. Going from $0 to $14B in annualized revenue in roughly two years, with 10x-plus growth each year, puts Anthropic in rare company. For context: it took Salesforce ~20 years to hit $14B in annual revenue.

→ Why does it matter? How many years did it take for human beings to create fire, build shelter, and hunt with spears? About 2.2 million years. We tend to think humanity progressed like the red line, but it actually looked more like the green line. The gap between those lines is where most organizations lose patience and quit. The ones who stay in the game during that flat period? They’re positioned at the elbow when everything compounds at once.

→ Why does it matter? New research from Anthropic on how Agents are being deployed, how much autonomy people are granting, and where they’re deployed. The full study can be found here.

Tweets that stopped my scroll:

→ Why does it matter? I’m sure this has hit most of your feeds by now. Sam Altman and Dario Amodei refusing to hold hands is priceless.

→ Why does it matter? This is an entirely AI-generated 3.5-minute movie. Wow.

→ Why does it matter? Many readers are setting up Claude Code as their Chief of Staff. If last year was the year of AI Agents, perhaps this year is that of Personal Operating Systems. Check out this short video for some great inspiration.

Worth a watch or listen at 1x:

→ Why does it matter? Dario Amodei doesn’t do many long-form interviews. When he does, he’s remarkably precise with language. This conversation covers the end of the scaling exponential, what “a country of geniuses in a data center” actually means for labor markets, and Anthropic’s positioning against OpenAI. The insight density is high. Amodei’s careful phrasing reveals as much as his answers. You’ll want to catch the pauses.

→ Why does it matter? Great to be with Marley Kayden to talk all things AI for Asset & Wealth managers, as well as what we’re seeing in the overall software market! Thank you for having Ridgeline on the show!

→ Why does it matter? Bret Taylor is the founder and CEO of Sierra, an AI agent company transforming customer service. Bret is one of the brightest minds on AI-native company building.

→ Why does it matter? Boris Cherny built Claude Code as a simple terminal-based prototype just a year ago, and it has already begun reshaping how software is built and how knowledge work is done. The details are the point: Claude Code’s growth to 4% of public GitHub commits, daily active users doubling last month, and the counterintuitive product principles behind it. When Boris says coding is “solved,” the claim is really about where work is moving next, from writing lines to directing systems.

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Quotes & eyewash:

→ Why does it matter? Very excited for Bill Gurley’s first book, Runnin’ Down a Dream. It comes out next week!

Subscribe now

The mission:

The Wall Street Journal once used “Read Ambitiously” as a slogan, but I took it as a personal challenge. Our mission is to give you a point of view in a noisy, changing world. To unpack big ideas that sharpen your edge and show why they matter. To fit ambition-sized insight into your busy life and channel the zeitgeist into the stories and signals that fuel your next move. Above all, we aim to give you power, the kind that comes from having the words, insight, and legitimacy to lead with confidence. Together, we read to grow, keep learning, and refine our lens to spot the best opportunities. As Jamie Dimon says, “Great leaders are readers.”

Disclaimer: This content is for informational purposes only and does not constitute financial, investment, or legal advice. Readers should do their own research and consult with a qualified professional before making any decisions.

The big idea: You can just build things

Reading time: 4 minutes

The best marketing introduces a human truth.

Jeff Goodby, the advertising legend behind “got milk?,” spent a career arguing that the work starts with something real about people. That is why I like watching Super Bowl ads. When a 30-second slot can cost $8 million to $10 million, companies are forced to compress their worldview into the essentials.

OpenAI compressed theirs into six words: “You can just build things.”

They are claiming the world is more buildable. And they are exactly right.

The gap between idea and reality is compressing fast. Once you have your first “weekend with Claude,” (a couple of hours building software with AI) the feeling of awe snaps in. You stop thinking of software as a permissioned activity, something that requires the right resume, the right team, the right sprint plan. You start thinking of it as an extension of your best ideas.

The hard part is no longer getting to execution. It is choosing what deserves execution. And it’s a moment where the workforce begins to recognize this shift.

Because now that you can just build things, are you going to be the person who does? That is the real question.

A lever, not a ladder

Most productivity tools behave like levers. A lever multiplies force, but it does not distribute force. It rewards the person already applying pressure in the right direction.

AI will not replace workers in the terminator-style scenarios we see in cinema. But workers who use AI will replace workers who do not. Not because they are better people, but because their unit of output compounds.

We are already seeing it in software.

A project that used to require eight engineers and one product manager is now down to two engineers and one product manager. In some cases, it collapses into a single high-level agency person who can blend product judgment with enough engineering competence to ship, test, and iterate. Bret Taylor, founder and CEO at Sierra, said it well: “I think it’s very important to have a very loose attachment to the way we do our jobs.”

In practice, it means the old boundaries are dissolving. Designers are doing PM work. Engineers are doing design work. PMs and designers are coding.

On one hand, that is empowering. The distance between idea and impact shrinks. Individuals get more agency. People who have spent years operating inside large machines can finally move more of the machine themselves.

On the other hand, it is dividing. Because the same compression that gives agency also squeezes the middle.

The K-shape workforce

It is starting to show up in the data.

A Harvard working paper tracks seniority inside nearly 285,000 U.S. firms and asks a simple question: does GenAI adoption tilt employment away from junior roles and toward senior ones? Yes. But “junior vs. senior” is a confusing label. What the chart really shows is a shift away from execution-heavy work and toward judgment-heavy work once execution becomes cheaper.

Inside companies, that looks like a K-shaped distribution.

On one side are people who treat AI as a multiplier. They compress the loop: prototype, test, debug, ship. They do not just produce more, they learn faster. Over time, that compounds, because tight feedback loops are the closest thing business has to free money.

On the other side are roles built around the old scarcity: translation, coordination, status, and handoffs. Those roles rarely disappear overnight, but they erode. The work gets unbundled, then absorbed by end-to-end owners and their tooling. Even when a role survives, it often does so with less leverage, because the firm now has a credible alternative: fewer people with broader scope.

This is not a “no juniors” world. It is a “different juniors” world. The opportunity is to hire AI-native early-career builders and train them in the new way of working. Bret Taylor is doing exactly that at Sierra with APX, an early-career program modeled after the Google Associate PM program started by Marissa Mayer, designed to turn new grads into Agent Engineers and Agent Product Managers who can build and ship end-to-end.

In other words, the aggregate chart reads junior down, senior steady. But the red thread is simpler: as the gap between idea and execution narrows, companies hire fewer people to do narrow tasks and more people at every level who can decide and do.

Why it matters

When AI makes more of the “doing” abundant, value migrates upstream toward judgment, context, and people with high agency and a bias for action.

This is the part most companies will learn the hard way. If execution cost drops, winners do not stop building. They raise ambition. They pursue adjacent markets. They revisit legacy systems. They launch experiments that were previously uneconomical.

When firms can do more with the same number of people, they protect the scarce input. The scarce input is no longer engineering capacity and institutional knowledge. It’s judgment, the ability to choose the right thing, align people around it, and see it through.

That is why hiring shifts away from routine execution and toward high-agency, end-to-end owners. Not everyone will build, but the people who do will pull away, and org charts will follow.

A decade from now, the story will not be that AI took everyone’s job. It will be that AI changed what a job could include: fewer handoffs, more ownership, smaller teams, wider scopes.

Which pulls us back to the human truth OpenAI smuggled into six words: “You can just build things.”

But now that you can, are you going to be the person who does?

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Best of the rest:

🚨 Something Big Is Happening – Matt Shumer compares where we are now to February 2020: the moment before everything changed. – X

⚔️ Peacetime CEO vs. Wartime CEO – Ben Horowitz’s classic framework on why the skills that build empires don’t save them. Growth mode and survival mode require completely different leadership DNA. – Andreessen Horowitz

💰 Alphabet Sells 100-Year Bonds to Fund AI – Google’s parent drew 7x oversubscription on a £750 million century bond. Investors are betting big that AI dominance pays off across multiple generations. – Bloomberg

💻 Your Software Has Become a Backseat Driver – Modern apps have shifted from tools you operate to channels that operate on you. One reasonable step at a time, we normalized constant interruption. – Mike Swanson’s Blog

Charts that caught my eye:

→ Why does it matter? OnlyFans has become one of the most durable consumer subscription businesses in tech history, now generating roughly 4x the consumer spend of OpenAI and NYT combined.

→ Why does it matter? J.P. Morgan just published its 19 AI-resilient software stocks after the SaaS crash, and the thesis is clear: companies with deep workflow integration, proprietary data moats, and security-critical positioning are the ones AI will augment rather than replace.

Tweets that stopped my scroll:

→ Why does it matter? This is the right framework for thinking about AI disruption. When software becomes cheap and abundant, the moats that matter are the ones AI can’t replicate: regulatory complexity, physical constraints, proprietary data, network effects, and operational excellence. The “bad” businesses (messy, people-intensive, hard to scale) suddenly look like the “safe” businesses.

→ Why does it matter? This is the logistics of modern media dominance in one image. NBC isn’t just covering two of the biggest events in sports: they’re treating the Super Bowl and Winter Olympics as a single, continuous broadcast operation. Two Bombardier Global 7500s (roughly $150K+ in charter costs) to move talent 6,000 miles overnight. That’s the cost of being everywhere at once. It’s also a reminder that “live” sports remain the last defensible moat in television. You can’t time-shift the Super Bowl. You can’t skip the opening ceremony. And NBC is betting that having Mike Tirico’s face on both events, back-to-back, is worth the private jet fuel.

→ Why does it matter? Ferrari partnering with Jony Ive signals something bigger than a luxury EV launch: it’s a bet that the next generation of high-end vehicles will compete on software and interface design as much as horsepower. The man who made aluminum feel like a status symbol is now shaping what it feels like to sit in a $400k electric car. That’s Ferrari acknowledging the cockpit is becoming the product.

→ Why does it matter? This is the clearest signal yet that we’ve crossed a threshold. Spotify’s best engineers aren’t writing code anymore: they’re directing AI systems that write code for them. The company shipped 50+ features in 2025 while their top talent focused entirely on architecture, review, and strategy. If Spotify (not exactly a bleeding-edge AI lab) is already there, the rest of the industry is about 18 months behind at most.

→ Why does it matter? An AI model to emulate human behavior and simulate the world we live in. A flight sim for humans.

→ Why does it matter? Sam Blond was CRO at Brex during its hypergrowth phase, so when he launches an AI sales platform, it’s worth paying attention. Monaco is betting that the traditional SDR model (hire bodies, run playbooks, grind through lists) is about to get automated away. If Monaco delivers, it compresses the go-to-market timeline for early-stage startups from “hire your first sales rep” to “configure your AI sales engine.”

Worth a watch or listen at 1x:

→ Why does it matter? We had a blast representing Ridgeline at WSJ’s Invest Live last week! Fun to share the Ridgeline story and where we’re planning to take the industry.

→ Why does it matter? Dwarkesh and John get Elon to connect dots he rarely connects publicly: Starlink’s bandwidth economics, Tesla’s energy storage buildout, xAI’s compute demands, and Optimus manufacturing. The thesis here is that these aren’t separate companies. They’re a vertically integrated system designed to solve the same bottleneck (power, data, physical labor) from multiple angles. At 2x you’ll miss the moments where Elon pauses to think through constraints in real time.

→ Why does it matter? Friend of Reading Ambitiously, Scott McNealy joined CNBC earlier this week to talk all things AI and share his perspective on the moment. Having lived and led Sun through the dot-com in a big way, Scott’s accrued wisdom is always insightful.

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Quotes & eyewash:

→ Why does it matter? One of the most amazing shots of live TV ever captured!

Subscribe now

The mission:

The Wall Street Journal once used “Read Ambitiously” as a slogan, but I took it as a personal challenge. Our mission is to give you a point of view in a noisy, changing world. To unpack big ideas that sharpen your edge and show why they matter. To fit ambition-sized insight into your busy life and channel the zeitgeist into the stories and signals that fuel your next move. Above all, we aim to give you power, the kind that comes from having the words, insight, and legitimacy to lead with confidence. Together, we read to grow, keep learning, and refine our lens to spot the best opportunities. As Jamie Dimon says, “Great leaders are readers.”

Disclaimer: This content is for informational purposes only and does not constitute financial, investment, or legal advice. Readers should do their own research and consult with a qualified professional before making any decisions.

The big idea: Not all software is created equally

Reading time: 4 minutes

The market is trying to re-price roughly $3T of software as if it were.

The market’s shorthand is “vibe coding kills software.” There is truth inside the fear. Vibe coding compresses software creation into natural language. Code is cheaper to generate, and substitution gets easier.

But not all software is created equally.

Some software is nice to have. It makes life a little easier. It has that one feature the other system doesn’t. Other software is needed. It runs the business. We rely on it.

So the question is: how do you tell the difference?

Moats with sharks

If the cost of writing code approaches zero, where are the moats? Here are some good questions to ask.

• Where does the company keep the “official truth”? When finance, legal, compliance, or a customer challenges something, which system is the final answer? Where is the ground truth?

• Which system is trusted to take action? What has the keys to do real work: approve, release, provision, pay, book, ship, or deprovision?

• What happens if you try to rip it out? Not “can you migrate the data,” but “does the business still run after it is gone,” and “what breaks on day two?”

• Where does work actually start? What is the default place teams go in the first 15 minutes of their day: the queue, the workflow, the command center, the system they live inside?

• When it’s wrong, how expensive is wrong? Does failure show up fast, get contained, and get reversed, or does it pollute the business and surface weeks later?

That checklist, when applied, helps separate software that is easy to recreate from software that is hard to replace.

The barbell

Industries tend to move in cycles. First, you win by owning the whole stack, because coordination is hard.

Then the stack breaks into layers, and specialists flourish.

Then coordination becomes hard again. The “middle” gets commoditized, and value concentrates at the ends: big platforms on one side, deep specialists on the other.

A barbell forms.

SaaS is approaching this phase in the cycle, and something similar happened right before the cloud.

Oracle ran the consolidation playbook, buying up all the pre-Cloud market leaders, including PeopleSoft in ERP and HR, Sun Microsystems, and Siebel in CRM.

It was a moat construction through getting big. Oracle pulled more truth into a single footprint. Once it owned the main systems of record and the workflow, it centralized procurement and renewals, bundled them into enterprise agreements, and let compounding handle the rest.

AI accelerates the same shape. It floods the middle with plausible substitutes, because code is cheap and interfaces are easy to copy.

And value concentrates at the ends.

On one end, systems that hold ground truth and are trusted to take action, and on the other, specialists that handle the messy parts of a domain where “mostly right” is not enough.

The middle will be a tough place to be. More clones, bundling, consolidation, and churn. Less pricing power.

The dangerous place is the middle. You don’t want to be in the middle. Software that is neither the source of truth nor trusted to act, and not protected by hard-earned expertise.

Why it matters

AI is not killing software. It is forcing a natural evolution we’ve seen before.

The debate is no longer whether something can be built. It is going to get built.

And if you’re a software company building it or you’re thinking about vibe coding an alternative to what you have today, ask the moat questions:

Is this the system that owns truth? Where we decide who’s allowed to act? And take that action? What breaks if we switch? What happens if the data in here is wrong? If our regulators call, is this the system we’re looking at?

In a software-abundant world, those answers decide who compounds, who commoditizes, and who gets acquired.

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Best of the rest:

🦀 OpenClaw Shows What Personal AI Assistants Should Be – Federico Viticci burned through 180 million tokens testing an open-source assistant that controls his smart home, apps, and can even improve itself. Runs on a Mac mini. – MacStories

💰 Nvidia’s $100B OpenAI Bet Gets Complicated – Jensen Huang called the deal “nonbinding” and criticized OpenAI’s business discipline, though he’s now walking it back. The stock dipped 1.1% on the uncertainty. – CNBC

💸 Anthropic Eyes $350B Valuation – The Claude maker is planning an employee tender offer at a staggering valuation, signaling just how hot the AI race remains – Bloomberg

📈 Time's up for SaaS, Grow Faster or Die , A blunt reminder that in a slower, pickier market, “good retention” is not a strategy, SaaS winners are redesigning go to market, pricing, and product velocity to earn growth again. – Meritech

🚀 Musk Merges SpaceX and xAI in $1.25T Deal – The combined company will build data centers in space to solve AI’s massive power demands. xAI is currently burning $1B per month. – TechCrunch

Charts that caught my eye:

→ Why does it matter? An LLM census that was outdated the second it was printed. We’re looking at 150+ foundation models released in 2025 alone, spanning 30+ organizations from Google and OpenAI to Alibaba and DeepSeek. The sheer density tells you everything: we’ve moved from a world of scarce AI capabilities to abundant, commoditized intelligence. Notice the mix of open-weight (yellow) and closed (orange) models. That’s the real story. The open-source ecosystem (Kimi, Qwen, Llama, DeepSeek, Mistral) is now matching proprietary players release-for-release. For builders, this means unprecedented choice. For incumbents, it means defensibility lives in distribution and data, not model weights.

→ Why does it matter? Claude Code now accounts for roughly 4% of all public GitHub commits. That’s AI becoming a meaningful fraction of global code production. The inflection point around October 2025 and the near-vertical climb since suggest we’re watching the adoption curve go exponential, not linear. Where will we be in 1 year? 20%? 30%?

→ Why does it matter? This Bain framework maps AI’s threat to SaaS. The existential risk isn’t Scenario 5 (total compression). It’s Scenario 4: AI captures the lion’s share of value while SaaS becomes commoditized plumbing. Notice the bar heights in Scenarios 3 and 4: total spending stays robust, but AI keeps growing while SaaS shrinks. That’s the barbell. Value flows to the AI layer and the systems of record underneath. The middle gets squeezed.

→ Why does it matter? That near-vertical orange line is OpenClaw (formerly Clawdbot and Moltbot), growing from zero to ~100K stars in a matter of months. For context: React, Vue, and the Linux kernel took a decade to reach similar numbers. The established frameworks show consistent, organic growth curves. OpenClaw’s trajectory looks nothing like that. We’re going to need a new name for hockey stick growth!

→ Why does it matter? The AI pricing model is in flux: 37% of companies plan to change their approach in the next year, and the top choice is consumption-based pricing (pay for what you use). That’s a direct response to the cost unpredictability of AI workloads. Traditional seat-based SaaS pricing assumed predictable per-user costs. AI breaks that model. A single power user can burn through 100x the compute of a casual one. Companies are waking up to this mismatch, and the shift to consumption and outcome-based models signals something bigger: we’re moving from “how many people use it” to “how much value does it create.”

Tweets that stopped my scroll:

→ Why does it matter? Moonshot AI’s founder doing his first on-camera appearance to announce Kimi K2.5 signals the Chinese AI labs are shifting from “build in silence” to “compete for global attention.” And they’re doing it with open source. Kimi K2 is already one of the best open-source models available, competitive with closed-source alternatives from major American labs. This is a strategic choice: open source builds developer ecosystems, accelerates adoption, and sidesteps the trust deficit Chinese companies face abroad. The playbook OpenAI wrote was founder-led narratives. The Chinese labs are adding a twist: founder-led narratives plus open weights.

→ Why does it matter? Anthropic is going all-in on brand marketing. These are cinematic, emotionally resonant ads showing real humans using Claude. And they had me laughing out loud. It’s a signal that the AI race is shifting from “best benchmarks” to “best brand.” When your product is a conversation, the story you tell about it becomes the product.

→ Why does it matter? This leaked email from Microsoft’s CTO reveals the internal theory at the time: Ilya Sutskever’s ouster of Sam Altman wasn’t about safety concerns or board governance. It was about resource allocation. Research wanted more GPUs for training frontier models. Applied (ChatGPT, the API) was eating them all. The “AI safety” framing that dominated headlines may have been a cover for a much older conflict: who gets the compute.

Worth a watch or listen at 1x:

→ Why does it matter? Peter Steinberger is the creator of OpenClaw, the open-source robotic claw machine that’s been taking over your feed. But before that, he built PSPDFKit into a $100M+ company. This talk captures the mindset behind both: you can just do things. Want to build a claw machine strangers can control over the internet? You can just do that. Want to cold-email a Fortune 500 executive? You can just do that. Most barriers are imaginary, and most gatekeepers aren’t actually guarding anything. OpenClaw is the latest proof the philosophy works.

→ Why does it matter? Gokul Rajaram built the ad products that turned Google, Facebook, and Square into revenue machines. This wide-ranging conversation covers how AI is fundamentally changing product development: faster cycles, smaller teams, and the death of the traditional PM role. Patrick is so good at getting to the topics that matter. Rajaram’s mental models are worth the full listen.

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Quotes & eyewash:

→ Why does it matter? Choose wisely!

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The mission:

The Wall Street Journal once used “Read Ambitiously” as a slogan, but I took it as a personal challenge. Our mission is to give you a point of view in a noisy, changing world. To unpack big ideas that sharpen your edge and show why they matter. To fit ambition-sized insight into your busy life and channel the zeitgeist into the stories and signals that fuel your next move. Above all, we aim to give you power, the kind that comes from having the words, insight, and legitimacy to lead with confidence. Together, we read to grow, keep learning, and refine our lens to spot the best opportunities. As Jamie Dimon says, “Great leaders are readers.”

Disclaimer: This content is for informational purposes only and does not constitute financial, investment, or legal advice. Readers should do their own research and consult with a qualified professional before making any decisions.

The big idea: Building AmbitiousOS & AI Agents w/ Claude Code

Reading time: 7 minutes

Every Friday, Reading Ambitiously appears finished. What you don’t see is the system behind it, and how much work goes into it before a single sentence makes it into the final draft.

This edition is different.

I want to walk through how I’m actually using Claude and AI agents to build what I now think of as AmbitiousOS (the operating system behind this newsletter). Not conceptually, but practically, and in the same order I built it.

Why is this important? As a team of one, every minute I can get back to what I consider the most valuable part of what I do (giving you the power of these ideas, tastefully selecting what makes it in here, thinking, and creating) is time well spent. It’s time to get back what translates directly into more value for you. Let the robots do the rest.

Getting Set Up: Claude Code and Visual Context

On my desktop, I run Claude Code in the terminal and VS Code side by side.

Claude can feel intimidating in a terminal window. VS Code makes the system legible. On the left, Claude understands the project. On the right, I can see the file structure as a real, assembled application.

As you become more comfortable with Claude, you may find yourself minimizing VS Code and “feeling the force,” as a close friend put it. Top engineers are now writing 100% of the code with Claude. VS Code is a nice stepping stone to getting there, so you can see the structure and have it help inform how you think about what you are asking the model to do.

Context Is King

Before writing any code, spend time on inputs.

Claude loosely knows what a newsletter is, just as it loosely knows what a car is. That is not enough. If you want a system that works, you have to explain the features that make the thing the thing.

Feature one. Feature two. Feature three.

This is where planning mode matters. I use Claude’s planning workflow, not to generate code, but to think out loud with the model. PRD, spec-led design, and planning: they are all the same idea. Good inputs equal good outputs.

I often let Claude interview me. It asks follow-up questions until the ambiguity disappears: parallel or sequential processing, file limits, UX expectations, and failure cases.

This stage takes time. Spend as much time as you can in planning. It always pays off.

System Design Is Not Optional

If you are not a software engineer, this part can feel uncomfortable.

It shouldn’t.

You are the architect now, whether you like it or not. AI does not remove the need for system design. It makes it explicit.

I’m basically the “Systems Architect” of Reading Ambitiously. It started as a simple internal post 2 years ago. Today, it includes video, charts, tweets, podcasts, and curated sections that some readers go to first. Each of those is a feature set with its own logic.

The system underneath had to evolve to support that complexity.

Understanding the primitives of what you are building is no longer a nice-to-have. It is the most important work.

Understanding the system drives design

Before building anything, you have to fully understand the system. It helps to draw it out. From there, you can derive the product requirements document (PRD).

That PRD includes the vision, mission, audience, personas, architectural decisions, and the agents themselves. It is the instruction manual for the model. The current master_PRD for RA OS is 2,268 lines. 🤯.

The more precise this document became, the better Claude’s output got. Not incrementally, but step-function better.

Version One: The Universal Inbox

The first system I built mirrored how I already worked.

A universal inbox. Content flowed in. I manually categorized it. Best of the Rest. Charts. Tweets. Podcasts. Each item could be processed by an agent to generate draft content.

It worked. It was UI-heavy. It was human-driven. It looked familiar.

Then I realized something obvious in hindsight, which reminded me of a favorite quote: “You’re always driving into the future in the rearview mirror.”

V1 looked a lot like what’s been possible pre-AI, not where we are today.

Let the Agents Do the Sorting

The AI was better at classification than I was.

So we built an auto-assembly system using an LLM with “vision,” so it can not only read text but also interpret images.

Now, content is classified first. Agents determine what something is, where it belongs, and how confident they are in their assessment. Then they process it and generate an initial “why it matters.”

This was the inflection point.

The more I built, the more agentic the system became. What started as folders became orchestration.

AmbitiousOS Today (really just a few hours later)

Today, when I log in, I do not see an inbox.

I see an update from my editor-in-chief telling me where the edition stands, what is missing, and where I should focus. I can talk to it. It coordinates the other agents in the background.

Each agent has narrow instructions and specific skills. Best of the Rest. Charts. Tweets. Podcasts. All orchestrated by the core system.

This will save hours every week.

What the System Does Not Do

AmbitiousOS does not decide what matters or why.

It does not replace taste. It does not judge how to explain why a certain piece of content matters. That’s my job!

What it does is remove the drudgery that competes with time for judgment. It protects my own context window, so I can spend my time on what matters most: explaining why something is important. So I can do what I love to do, give our readers the power!

That tradeoff is the entire point of most Agents you’re going to want to have.

Where This Is Headed

As one friend told me after seeing an early version, there may not be a UI at the end of this arc.

I think they are right.

The system started hierarchical, then became AI-driven, and now feels AI-native. That progression was not planned. It emerged.

Last year may have been the year of agents. This feels like the year of personal operating systems.

Software shaped around how you think, not how software has always worked.

This is just the beginning.

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Best of the rest:

💾 Microsoft Flexes Custom Silicon – Maia 200 chip claims 3x performance over Amazon’s Trainium, will run GPT-5.2 and power Microsoft 365 Copilot at 30% better cost efficiency – The Verge

📊 The Man Who Almost Ran Berkshire – David Sokol was Buffett’s heir apparent until personal trades derailed everything. Now Greg Abel has the job Sokol lost. – WSJ

💰 Brex’s $5.15B Exit and the Hubris Tax – Capital One acquires Brex for less than half its peak valuation. A massive win by any normal standard, but a cautionary tale about raising at 100x+ revenue multiples. – X

🌅 Amodei’s “Technological Adolescence” Essay – Anthropic’s CEO lays out a battle plan for humanity’s most dangerous transition: the moment we hand near-infinite power to systems we barely understand – Dario Amodei

🎖️ Title Arbitrage – Palantir turned “solutions engineer” into “forward-deployed engineer” and changed how the role was valued. AI companies should do the same. – a16z

Charts that caught my eye:

→ Why does it matter? After three flat years, iOS app releases are now running 60% higher YoY in December and 24% higher on a trailing twelve-month basis, a pattern that lines up well with the arrival of tools built for agentic coding!

→ Why does it matter? OpenAI is betting that conversational AI commands a premium: at $60 CPM, ChatGPT ads cost 38% more than broadcast TV and 12x more than YouTube. That’s not a typo. OpenAI believes attention inside a chat interface is fundamentally different (and more valuable) than passive scrolling. The question is whether advertisers agree, or whether this pricing becomes the ceiling that forces OpenAI to build a real ads business from scratch.

→ Why does it matter? Software costs followed a predictable pattern for 25 years: open source dropped the floor, cloud made it accessible, then complexity crept back in (more tools, more integrations, more headcount to manage it all). AI agents break that cycle entirely. They don’t just reduce costs. They collapse the labor component of software delivery. The companies that figure this out will operate at cost structures their competitors can’t match.

Tweets that stopped my scroll:

→ Why does it matter? This is a real-time map of enterprise software moats. When the two hottest AI companies are explicitly hiring for Salesforce, Workday, and NetSuite experience, they’re telling you which platforms have become so deeply embedded in how businesses operate that even frontier AI labs can’t ignore them. The long tail is equally revealing: 25+ different vendors mentioned means the modern enterprise stack is fragmented, specialized, and sticky.

→ Why does it matter? Claude is now embedded directly where knowledge workers actually live: inside Excel. This is Anthropic’s enterprise play in action. Instead of asking users to copy data into a chatbot, they’re bringing the AI to the spreadsheet. This turns Claude from a “clever assistant” into a genuine co-pilot. Google has Gemini in Sheets. Microsoft has Copilot in Excel. Anthropic just made its move.

→ Why does it matter? Anthropic projects it could hit $148B in revenue by 2029 while spending $100B+ on model training before going cash-flow positive. For context, Google’s entire 2024 revenue was ~$350B. Anthropic is projecting nearly half that in five years. The AI race increasingly isn’t just about who builds the best model—it’s about who can sustain the spend long enough to reach profitability.

→ Why does it matter? This is emergent agent behavior in the wild. The AI wasn’t programmed to handle voice messages. It encountered an obstacle, found tools in the environment, and chained together a solution on its own. That’s not following instructions. That’s problem-solving. Feel the AGI. We’re watching the shift from “AI that does what you tell it” to “AI that figures out what needs doing.”

Worth a watch or listen at 1x:

→ Why does it matter? Honestly, with all the content from Davos/WEF over the past week, I’ve declared podcast bankruptcy. Here is a 15-minute set of timeless lessons on how to show up like some of the very best leaders. As I’ve heard Dave Duffield say, “You have two ears and one mouth, use them proportionately.”

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Quotes & eyewash:

→ Why does it matter? How my friends who are real software engineers feel when I tell them about the application I just built with Claude.

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The mission:

The Wall Street Journal once used “Read Ambitiously” as a slogan, but I took it as a personal challenge. Our mission is to give you a point of view in a noisy, changing world. To unpack big ideas that sharpen your edge and show why they matter. To fit ambition-sized insight into your busy life and channel the zeitgeist into the stories and signals that fuel your next move. Above all, we aim to give you power, the kind that comes from having the words, insight, and legitimacy to lead with confidence. Together, we read to grow, keep learning, and refine our lens to spot the best opportunities. As Jamie Dimon says, “Great leaders are readers.”

Disclaimer: This content is for informational purposes only and does not constitute financial, investment, or legal advice. Readers should do their own research and consult with a qualified professional before making any decisions.

The big idea: Context is king, protect it at all costs

Reading time: 6 minutes

Every AI demo you have ever seen has the same hidden dependency: a box of text that the underlying LLM can hold in its “mind” at once.

That box is the context window.

Think of it as the model’s working memory at run time. Models learn enormous amounts during training, but at runtime, they can only condition on what fits within the current window: the system prompt, your message, tool outputs, documents, and custom instructions. All of it competes for the same space. If it’s not in the window, it’s not reliably in play.

If you feel the pace of AI development is dizzying, you’re not alone. Agents, skills libraries, sub agents, MCPs, tools and plugins, Ralph Wiggum loops, it’s a lot to keep up with. Until you realize most of it converges on the same problem: protecting and compounding context.

That pressure is turning into a discipline. Software development is getting easier. The harder work, the new software engineering, is protecting and compounding context across time, tools, and workflows, without sacrificing reliability, security, or clarity.

The long-context arms race

For the past two years, model providers have competed on context window size the way cloud companies once competed on storage limits.

The state of the art now supports 1M-plus token context windows. In plain English, you can hand a model a small library, not just a prompt. Long context expands what is possible.

But a bigger memory does not eliminate the hard part: deciding what matters.

Meet “context rot,” or why more can lead to worse performance

Researchers have documented a frustrating pattern in long-context models: performance can degrade just by moving the relevant information around inside a long context. Models often do best when the crucial detail sits near the beginning or the end, and they do worse when it is buried in the middle.

It’s referred to as context rot, a catch-all term for performance degradation as prompts grow longer, more crowded, and harder to search.

Long context is necessary for real work. But long context can still produce brittle behavior if the model cannot reliably use what you provided.

It is no longer, “How many tokens can I shove in.” It becomes, “How do I curate the right tokens, at the right time.”

Enter context engineering, the iterative practice of selecting what should go into the limited context window from an expanding universe of possible information.

The 2026 story: protect the context window at all costs

Once you see the context window as a scarce resource, many recent “agent innovations” stop looking like separate ideas. Tools, MCP, skills libraries, orchestration loops, retrieval, they are all variations on the same theme: preserve the window for judgment, and push everything else out.

Here is the simple mental model:

• Tools and plugins: reach. The model does not try to be a browser or a database. It calls existing systems, then uses the results. This keeps the conversation lighter and the work more reliable.

• MCP: plumbing. A standard way to connect agents to external tools and data without turning every integration into custom prompt glue.

• Skills libraries: know-how. Reusable playbooks packaged for an agent. Instead of pasting your entire SOP into the prompt, the agent loads the right instructions only when needed.

• Orchestration loops (the Ralph Wiggum idea): reset and re-anchor. Long-running work stays grounded in artifacts, files, diffs, tests, not an endlessly growing chat transcript, because the system keeps refreshing the context window and re-centering on the current source of truth.

• Retrieval and summarization: select, then compress. Pull the few documents that matter, summarize them into something the model can use, and move on.

Different names, same objective: treat context like a budget, and spend it on the decisions that matter.

Why it matters

When context is high-quality and well-structured, the AI produces excellent output. When context is noisy or inconsistent, less so. Garbage in, garbage out.

Software development may be getting easier. Software engineering is being redefined. The bottleneck is no longer writing code, it is assembling systems that preserve intent across time, tools, agents, and workflows, systems that are reliable, secure, composable, and understandable. It’s context engineering.

So the real craft of building AI Agents in 2026 looks like:

• Deciding what must be in the window.

• Deciding what should live outside it in tools, files, and skills.

• Building mechanisms that load the right context at the right time.

• Having the discipline to keep the working set clean.

In other words, the context window is becoming what RAM was to early computing. It is the scarce resource around which the whole system organizes, at least for now.

This feels like the future of software engineering. And I don’t think we’re going back.

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Best of the rest:

🛰️ The Palantirization of Everything – Palantir’s real edge is not the model, it’s the hard-won ability to wire AI into real workflows, data, and decision loops, and that playbook is about to spread everywhere. – a16z News

🤖 My agents are working. Are yours? – Jack Clark captures the real shift in agentic AI: not better chat, but a managed fleet of synthetic coworkers that multiplies your output while you hike, sleep, and live. – Jack Clark on X

🏢 The future of enterprise software; Aaron Levie makes the case that agents will not replace SaaS, they will ride on top of systems of record as guardrails, forcing a shift from seats to consumption pricing and expanding enterprise software markets. – Aaron Levie on X

🛠️ Compound Engineering: How Every Codes With Agents; A practical four-step loop that turns agent-written code into a flywheel, where each change teaches the system and speeds up the next one.; Every

🤖 2026: This is AGI. Pat Grady and Sonya Huang argue that long horizon agents can iteratively figure things out in messy, ambiguous environments, which is the practical shift from AI that talks to AI that does, and it pulls a surprising amount of the future into 2026; Pat Grady on X.

Charts that caught my eye:

→ Why does it matter? It’s an interesting time for SaaS. One clean way to see the scale is the BVP Nasdaq Emerging Cloud Index, which still represents about $2.0T in market cap and trades at an average 7.3x revenue. Cloud software used to be the default public-market compounder, but it keeps trading down under the logic that AI will replicate big chunks of what these companies do. I’m not sure that story holds up in the places that matter. AI will pressure the thin layers, but it also raises the value of the hard parts, distribution, integrations, and the domain data sitting underneath systems of record. Like most mature markets, that feels like a barbell, the giants get even bigger, and the specialists get even more valuable. Perhaps you won’t want to be in the middle.

→ Why does it matter? Additional data from Meritech on the above.

→ Why does it matter? AI won’t make most human skills obsolete, but it will change how they’re used. According to McKinsey, Negotiation, problem-solving, and leadership will matter more than ever as people work alongside agents and robots.

→ Why does it matter? Enterprise market LLM share. Interesting to see Anthropic up and to the right as OpenAI focuses heavily on consumer.

→ Why does it matter? Have always enjoyed Avenir’s state of SaaS; this year doesn’t disappoint. Check it out here.

Tweets that stopped my scroll:

→ Why does it matter? Watch the exact moment that led to winning a Nobel Prize. Full documentary is here (bookmark for your next plane ride).

→ Why does it matter? Own monopolies. You can check out TCI’s holdings on Whale Wisdom here.

Worth a watch or listen at 1x:

→ Why does it matter? Had a blast earlier this week with Diane King Hall on Schwab’s Morning Movers discussing a wide range of topics in AI and investment management! Check it out here.

→ Why does it matter? There is a lot coming out of Davos this week, including this conversation with Anthropic’s founder Dario Amodei. Dario is a great thinker, and it’s worth gaining his perspective on all of the most pressing topics in AI.

→ Why does it matter? This McKinsey & Company presentation explores how AI impacts software development, moving beyond Agile methodologies. Worth a watch to see what could be beyond agile.

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Quotes & eyewash:

→ Why does it matter? Wow! More than $520 million in contributions from Dave Duffield, including a new pledge of $371.5 million and a 2025 commitment of $100 million, combined with previous gifts, will establish the Cornell David A. Duffield College of Engineering. Go Big Red!

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The mission:

The Wall Street Journal once used “Read Ambitiously” as a slogan, but I took it as a personal challenge. Our mission is to give you a point of view in a noisy, changing world. To unpack big ideas that sharpen your edge and show why they matter. To fit ambition-sized insight into your busy life and channel the zeitgeist into the stories and signals that fuel your next move. Above all, we aim to give you power, the kind that comes from having the words, insight, and legitimacy to lead with confidence. Together, we read to grow, keep learning, and refine our lens to spot the best opportunities. As Jamie Dimon says, “Great leaders are readers.”

Disclaimer: This content is for informational purposes only and does not constitute financial, investment, or legal advice. Readers should do their own research and consult with a qualified professional before making any decisions.

The big idea: It’s a new world with Claude Code. My 2026 AI powered workspace.

Reading time: 1 minute (watch our video edition)

Software is instructions for a computer.

When those instructions run on someone else’s computer, we call it the cloud.

Cloud made software easier to ship, secure, and operate.

AI makes software easier to create. And English is becoming the most powerful programming language. That is a big deal.

In my personal use, I’m shocked by how Claude Code and Cursor shrink the distance between ideas and executing them.

For this week’s Reading Ambitiously, Claude and I built an AI-enabled Voice CRM, a highly personalized tool that uses my voice and AI to help me manage relationships better. Claude looked at the requirements and told me it would take 16 days. I asked, “Are you sure?” Claude replied, “Oh, I thought you meant human developer time. We’ll be done in 30 minutes.”

Software can now be highly personalized at near-zero marginal cost. Claude estimates this entire project (3,500 lines of code) cost ~$45 to build.

And the best way to understand it is to watch it happen.

So today’s Reading Ambitiously is a special video edition. You’re about to watch me build and use that PersonalCRM directly inside my 2026 AI-powered workspace.

It’s a new world.

See you in it.

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Best of the rest:

🤝 Apple, Google strike Gemini deal for revamped Siri in major win for Alphabet – Apple putting Gemini under the hood of Siri makes Google the default intelligence layer across Apple’s massive device base, a distribution win that reshapes the consumer AI platform race. – Reuters

🦷 An Updated Dentist Office Software Story – A parable for how “vibecoding” with AI can finish the job on vertical SaaS moats by letting end users build bespoke workflows faster than vendors can ship roadmaps. – AVC

🧰 Claude Code and What Comes Next – Agentic coding tools are crossing from “helpful autocomplete” to “do real work for an hour unsupervised,” which is the quiet threshold that makes AI feel less like software and more like labor. – One Useful Thing

Charts that caught my eye:

→ Why does it matter? A class divide might be forming inside companies. Some % are becoming AI-native, using ChatGPT daily and steadily upgrading their workflows. Another % are still skeptical, barely use LLMs, and dismiss the tech as slop after a quick test. If that pattern holds, the productivity gap could be big, and it will be on companies and individuals to close it.

Tweets that stopped my scroll:

→ Why does it matter? If AirPods were their own company, they'd be a colossal tech giant, generating billions in annual revenue (around $12-18B+ in recent years), boasting massive profit margins, and achieving a valuation potentially exceeding $190 billion, placing them above major brands like Netflix, Pepsi, and Nike in market cap. A good place for Sam and Jony to get started with their first product.

→ Why does it matter? What Anthropic launched here goes beyond “Claude versus ChatGPT.” It feels like a new interface category. As the underlying models mature, the tooling will get simpler, the setup will fade into the background, and the user won’t need to think like an engineer to get real leverage. Cowork is a preview of that arc, a world where Claude Code, which still rewards a fairly technical skillset today, becomes accessible to the average knowledge worker.

Worth a watch or listen at 1x:

→ Why does it matter? This is not a highlight reel, it is a case study in second acts. Steve Young spent years trying to earn credibility in investing without leaning on his NFL fame, then went on to co-found HGGC, which has raised roughly $7B across its funds, including a $2.5B latest fund, and has completed about $50B in deals. You see how elite intensity transfers from the field to building a firm, how networks compound when you design them on purpose, and how leadership gets tested when you plan succession and share ownership. The best takeaway is simple: give away the playbook so others can win.

→ Why does it matter? Ryan Holiday has written a few of my favorites, including “Ego Is the Enemy” and “The Obstacle Is the Way.” He’s also the voice behind The Daily Stoic, and in this short video he shares 26 quick reminders for how the Stoics would approach the year ahead.

→ Why does it matter? This episode of Lenny’s Podcast with Molly Graham is jam-packed with practical career advice and an operator’s view of what it actually feels like to build inside a company that’s scaling. Graham helped lead through hypergrowth at Google and Facebook, later ran operations as COO at Quip and at the Chan Zuckerberg Initiative, and is best known for her “give away your Legos” framework for growing as fast as your company grows. She also publishes her writing on Substack here: mollyg.substack.com.

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Quotes & eyewash:

→ Why does it matter? Starting Reading Ambitiously forced me to see my writing clearly. Every sentence either works or it doesn’t. Writing well is a lifelong pursuit. Scott Adams has simple, timeless rules that make you better fast. H/t to Sheel Mohnot.

Subscribe now

The mission:

The Wall Street Journal once used “Read Ambitiously” as a slogan, but I took it as a personal challenge. Our mission is to give you a point of view in a noisy, changing world. To unpack big ideas that sharpen your edge and show why they matter. To fit ambition-sized insight into your busy life and channel the zeitgeist into the stories and signals that fuel your next move. Above all, we aim to give you power, the kind that comes from having the words, insight, and legitimacy to lead with confidence. Together, we read to grow, keep learning, and refine our lens to spot the best opportunities. As Jamie Dimon says, “Great leaders are readers.”

Disclaimer: This content is for informational purposes only and does not constitute financial, investment, or legal advice. Readers should do their own research and consult with a qualified professional before making any decisions.

The big idea: High-Agency teams win the AI era

Reading time: 5 minutes

In the agent era, the bottleneck is not intelligence. That will be abundant. The bottleneck is an orientation toward acting on it.

It’s a crisp January 9, 2026 morning. Company A and Company B sit down to map out the year ahead. They face the same market pressures, similar opportunities, and the same question on the table.

Can we leverage AI to achieve a real competitive advantage this year?

Both have the same frontier models from OpenAI, Anthropic, and Google. Both have committed real resources and budget. Both are aiming for meaningful wins.

Fast forward to the end of 2026. The outcomes have diverged sharply.

Company A treated agents like new co-workers, and they didn’t want to see their teammates fail. They selected a handful of key revenue-generating workflows, assigned a single owner to each, deployed them into production, and iterated quickly.

One example: business development. Agents monitored prospect account signals, automatically segmented markets, generated briefs for sales executives, and teed up the next best action. They provided the agents with data from Salesforce, ZoomInfo, Perplexity, and other sources. They also updated the agents’ instruction set and skills catalog every time they learned something new. Humans weren’t replaced. They gained leverage.

Company B treated agents like experiments. It launched pilot programs across functions, built impressive prototypes, and generated artifacts, decks, and internal excitement. But ownership stayed diffuse. Decisions waited for alignment. Nobody wanted to be the person who shipped the wrong thing into production. Each pilot ended with a debrief instead of a deployment. By year’s end, there was activity, but little achievement.

At Company B, AI was allowed to assist rather than be authoritative. It could draft, summarize, and suggest, but it could not own outcomes from end to end. It was a move to play it safe, and it kept progress slow. Work moved at the speed of human review.

What was the primary difference between Company A and Company B? It wasn’t the technology. For practical purposes, the AI tools available today (ChatGPT, Gemini, Claude, Grok) are increasingly interchangeable. What they provide, intelligence, is becoming abundant.

What differed was agency, the root word of AI agent.

An agent is, at its core, something that acts. My favorite definition of agency is the capacity to direct action, to choose an outcome, and move toward it without waiting for conditions to be perfect or blaming the circumstances. When agency is high, intelligence stops being merely impressive and starts becoming leverage.

LLMs are making intelligence abundant. The prize for high-agency teams that deploy it is leverage: a small team producing the output of a much larger one because the distance between intent and execution collapses.

That’s what Company A earns, and Company B does not.

Leverage emerges when software is empowered to act, not just suggest or summarize, and when humans transition from doing the work to overseeing the exceptions.

If you want the Company A outcome in 2026, start where the compounding begins: build a high-agency team, give them AI superpowers, and have them build the workflows that drive growth.

What it means to be high agency

Back to my favorite definition: getting what you want without waiting for conditions to be perfect or blaming the circumstances.

It’s easy to mistake high agency for a personality trait. It isn’t. It’s closer to an operating system, and in my view, one of the best predictors of success in hiring.

George Mack, who writes the High Agency blog, frames it as a blend of three skills: clear thinking, a bias for action, and a willingness to dissent when the default path is wrong.

High-agency teams start with outcomes and define “done” in plain English. Ambiguity is normal. When requirements are fuzzy, they don’t freeze. Being wrong is acceptable, the goal is to reduce uncertainty. They default to ownership. You can hear it in their language: “I will,” “we will,” “here’s the plan,” “here’s the deal.”

Now the uncomfortable part. Most organizations train the opposite. The good news is it can be fixed.

Low agency is often an incentive response, not a talent problem. If promotions go to the people who play it safe, if leaders punish a deployment that breaks something, if every decision requires a parade of approvals, if the safest move is to wait until consensus is reached, then waiting becomes rational.

Over time, a culture develops that appears thoughtful, aligned, and busy, while quietly bleeding momentum and creativity.

We all likely recognize our own environments in this story. If projects die through committee rather than failing fast in production, we are not looking at a motivation gap. We are looking at a system that rewards low agency.

Adjust the incentives and you can adjust the agency. A few suggestions:

• Make ownership safe.

• Give clear decision rights, with explicit thresholds and discretion.

• Reward speed with accountability.

• Treat failures as data, and focus on what was learned.

• Acknowledge fear and reward good judgment. Encourage teams to trust their instincts.

This is the biggest difference between Company A and Company B. AI can act today, but it needs intent. A low-agency organization will use AI to generate artifacts and email summaries. A high-agency organization will use AI to own outcomes and create leverage.

The historical bottleneck was intelligence and technical skill. That is no longer the constraint. The constraint is willingness to act on intelligence that is now abundant.

Choose to be Company A

This is an easy story to tell, and a harder one to live.

But it’s worth saying plainly: technology isn’t the differentiator. The models are strong, the platforms feel interchangeable, and access is increasingly widespread. Thanks to AI, intelligence is becoming abundant.

Build for high agency. Ownership under uncertainty. The ability to pick an outcome, move toward it without waiting for conditions to be perfect or blaming the circumstances, and stay accountable when the work gets real.

Do that and you’ll earn the leverage that follows: a small team producing the output of a much larger one. Faster cycles from intent to execution. Work that used to require coordination across five different people handled in one directed loop, with humans focused on judgment, discernment, communication, trade-offs, and decision-making.

Company A did not win because it had better tools. It won because it built a high-agency team that learned how to use them.

Choose to be Company A.

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Best of the rest:

📖 Companies Are Desperately Seeking ‘Storytellers’ , Corporate America is racing to hire narrative builders as earned media shrinks and AI-generated content floods the zone, forcing brands to own their voice, credibility, and human connection end to end. , The Wall Street Journal

🔮 What Will Happen In 2026 – Fred Wilson drops ten concrete predictions across AI, politics, venture, and markets that are easy to track through the year and useful for calibrating your own worldview. – AVC

🚀 Shipping at Inference-Speed – A visceral report on what software building looks like when you stop “writing code” and start running a factory of agents, with the real constraint shifting from keystrokes to model throughput, verification loops, and architectural taste. – Peter Steinberger

✨ Standing Out in 2026 – Lulu Cheng Meservey’s call is simple and sharp: in a world drowning in synthetic slop, the only durable edge is reality, real craft, real proof, real relationships, and real-world artifacts that compound into narrative trust over 6 to 18 months. – Lulu Cheng Meservey on X

Charts that caught my eye:

→ Why does it matter? Inference is the compute burned at runtime, after a model is trained; training creates the weights, inference is the ongoing cost that scales with usage. Reasoning models add a new lever: more test-time compute can improve output, so “thinking time” becomes a metered input. As agentic apps come online, most of the compute will be consumed at runtime, by systems that plan and execute workflows end-to-end.

→ Why does it matter? The 2025 IPO class reads like a stress test of private-market narratives, particularly the belief that venture backing and brand recognition can offset durable cash flows once the lockups expire. Even the winners, Circle, CoreWeave, and Figma, demonstrated how challenging it is to maintain momentum when public investors are evaluating fundamentals.

→ Why does it matter? Speaking of IPO, the window is open, and it could swing wide in 2026 if SpaceX leads the way!

→ Why does it matter? Stack Overflow was the internet’s help desk for programmers, a public place to paste an error message and get a fix that stayed searchable for the next person. That loop snapped once developers could ask ChatGPT and coding assistants in private, and the volume of new questions has sunk to roughly where it was when Stack Overflow launched in 2009. Prosus paid $1.8B to acquire Stack Overflow in 2021, closing that summer, which in hindsight looks like selling the tollbooth right before traffic rerouted.

→ Why does it matter? The 2025 news cycle in one chart via Axios!

Tweets that stopped my scroll:

→ Why does it matter? The world of software development is changing fast. Rahul from Ramp captures well some of the changes they’re making on the engineering team. Impressive. Worth clicking through and reading the whole Tweet if you are on one of these teams!

→ Why does it matter? It truly is a once-in-a-lifetime opportunity to build right now. Greg Isenberg captures a solid list of reasons why.

Worth a watch or listen at 1x:

→ Why does it matter? If you’re looking for a shot of intensity in the veins as we kick off 2026, look no further than this wide-ranging conversation with Matt MacInnis, CPO at Rippling. I admire Matt’s drive and ambition. I particularly like what he calls SPOTAK - people are generally Smart, Passionate, Optimistic, Tenacious, Adaptable, and Kind. If a candidate doesn’t click in a hiring process, chances are one of these traits is off.

→ Why does it matter? Make sure you’re in the right headspace before you fire this one up. It’s worth having real focus going in, especially if you’re feeling reflective as we kick off 2026. There are so many key insights in here. Including this mantra Dr. Brooks says every morning with his class at Harvard. “I am truly grateful for the pleasant things that will happen today. I am truly grateful for the troubles I face because my learnings and growth will come from those troubles. Bring them on”

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Quotes & eyewash:

→ Why does it matter? 💯

→ Why does it matter? As Mr. Ford said, “If I had asked people what they wanted, they would have said faster horses.”

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The mission:

The Wall Street Journal once used “Read Ambitiously” as a slogan, but I took it as a personal challenge. Our mission is to give you a point of view in a noisy, changing world. To unpack big ideas that sharpen your edge and show why they matter. To fit ambition-sized insight into your busy life and channel the zeitgeist into the stories and signals that fuel your next move. Above all, we aim to give you power, the kind that comes from having the words, insight, and legitimacy to lead with confidence. Together, we read to grow, keep learning, and refine our lens to spot the best opportunities. As Jamie Dimon says, “Great leaders are readers.”

Disclaimer: This content is for informational purposes only and does not constitute financial, investment, or legal advice. Readers should do their own research and consult with a qualified professional before making any decisions.

The big idea: There is no going back. A year in vibes.

Reading time: 5 minutes

This is the final Reading Ambitiously of 2025, and it closes our first full year of writing to you every week.

Every technology era has a clean line you only recognize once you’ve crossed it. This past twelve months, I believe we’ve crossed it with AI. I believe we’ll look back at 2025 and say this year was the clear before and after. I encourage you to watch our video edition this week to understand why I hold that view.

Starting Reading Ambitiously had its own before and after in my life. Writing every week sounds simple enough until you try to do it. What I’ve learned is that the work is not the writing itself. It is defending a block of time, every week, long enough to actually think.

And in an ever-busy world where we are all in a hurry, that thinking time feels like one of the scarcest resources for us all. Writing is how I personally get it back.

When you give someone words for an idea they already felt but could not articulate, you give them power. Reading Ambitiously is a weekly endeavor to foster language and community for what we are all experiencing, to empower you to utilize it and lead with it.

In pursuit of that mission, we wrote a lot this year. A few numbers tell the story better than I can.

Today is our 48th edition of 2025. In total, that added up to 100k+ words. The word counter has its own sense of humor. “ai” was the most common word, with 1,020 whole-word mentions, and “openai” and “software” weren’t far behind.

The themes reveal where the gravity was: AI & Models dominated, followed by Enterprise Software & SaaS, Markets & Investing, and Infrastructure & Semiconductors, with a notable strand of Leadership & Culture.

Over the past year, we have reached more than 1.7 million eyeballs on LinkedIn, Substack, and X.

Most importantly, at our first full year since launch, we have 905 readers who read very closely, share thoughtfully, rarely miss a week, and want this work to continue. These are the folks who will text me at ten minutes past the hour on Friday morning, asking if I’m “OK” because we haven’t published yet.

Kevin Kelly talks about your first 1,000 true fans. This newsletter is not designed for the widest possible audience. It’s designed for the most ambitiously dense. If that is you in the “905”, thank you for being here. You are why we do this every week.

Your notes and testimonials fire me up. They also keep me humble and remind me to keep innovating every week.

If Reading Ambitiously earned a slot in your weekly routine, I have one ask: help me improve it, help me share it, and help me reach our first 1,000 true fans by forwarding this to one person as ambitious as you.

Thank you, truly. Happy Holidays and Happy New Year. We look forward to being back with you on January 9, 2026.

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Best of the rest:

💸 Amazon reportedly in talks to invest $10B in OpenAI as circular deals stay popular – If this lands, it signals OpenAI is moving from a Microsoft-centric compute stack to a multi-cloud, multi-chip world where capital, chips, and cloud credits blur into one giant strategic bargain. – TechCrunch

🧠 Portable Memory & Behavioral Signatures: the missing layer for AI personalisation – The real leap in personalization is not better in-model memory tricks, it is making your “behavioral signature” portable across apps so “sign in” becomes “import how you work,” killing onboarding and turning identity providers into memory providers. – Venture Bystander

⚙️ TPU Mania – A sharp tour of Google’s TPU resurgence that frames the real fight as ecosystem and software moat versus NVIDIA’s CUDA, and asks if this is the CISC vs RISC moment for modern AI accelerators. – The Chip Letter

🚀 SpaceX Targets $1.5 trillion IPO – Reports say SpaceX is lining up a 2026 public debut, and the deeper “why” is Starlink plus Starship turning space into scalable infrastructure for orbital compute, with an IPO-sized war chest to accelerate the plan. – Ars Technica

🧾 Clouded Judgement 12.12.25 - Long Live Systems of Record – A needed corrective to “agents replace systems of record” hype, arguing automation makes canonical truth more valuable, not less, and that the winners will be the platforms that turn warehouses plus semantic layers into an explicit “truth registry” agents can safely read and write. – Clouded Judgement

Charts that caught my eye:

→ Why does it matter? A new cohort of AI-native companies is hitting $100M in annual recurring revenue faster than any software generation before it. The real test is quality. Is this durable, expanding revenue, or a surge of buyers sampling a shiny new tool before moving on?

→ Why does it matter? CEO’s are embracing AI and suggesting it’s going to change the shape of your workforce.

→ Why does it matter? Strong list to watch in 2026. I’m paying closest attention to #27, Vercel. I’ve also enjoyed testing a few others here, including Cursor, ElevenLabs, and Wispr.

→ Why does it matter? Valuations circa 1999 vs. today.

Tweets that stopped my scroll:

→ Why does it matter? Incredible execution by the team at Notion in growing to their $10B valuation. After the 2021 froth, the reset did not come from financial engineering, it came from revenue catching up, plus smart packaging like bundling AI into Business and Enterprise tiers.

→ Why does it matter? We used Opus 4.5 in today’s vibe coding demonstration!

→ Why does it matter? Google is on the offensive with Gemini. As it gets woven deeper into Google Workspace, formerly G Suite, the in-app integrations across Gmail, Docs, Sheets, and Slides are starting to feel like a real workflow shift. Exciting example here.

Worth a watch or listen at 1x:

→ Why does it matter? No podcast has stayed in my rotation longer than Tim Ferriss, one of the originals of the format. Bill Gurley is a strong guest, and they cover a lot of ground. This is an easy one to add next time you’re multitasking to catch up on multiple topics.

→ Why does it matter? I wouldn’t multi-task through this one. Henry Ellenbogen, founder and managing partner of Durable Capital Partners, and formerly the lead manager of T. Rowe Price’s New Horizons Fund, is thoughtful in a way that rewards attention. The episode, “Man Versus Machine,” is a long-form tour through how he thinks about people, change, and the small handful of companies that drive most long-term returns, worth hearing on 1x.

→ Why does it matter? Twice a year, Benedict Evans produces a big presentation exploring macro and strategic trends in the tech industry. New in November 2025, AI Eats the World. Here he is on The a16z Show with Erik Torenberg to break the deck down, and to connect it to the broader shift some people call “Software 2.0,” where more capability moves from hand-written code into models trained on data.

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Quotes & eyewash:

→ Why does it matter? New Toy Story 5 is coming next summer. Guess who the villain is? An iPad. Perhaps this will be the turning point to fight back against too much screen time. I seriously hope so.

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The mission:

The Wall Street Journal once used “Read Ambitiously” as a slogan, but I took it as a personal challenge. Our mission is to give you a point of view in a noisy, changing world. To unpack big ideas that sharpen your edge and show why they matter. To fit ambition-sized insight into your busy life and channel the zeitgeist into the stories and signals that fuel your next move. Above all, we aim to give you power, the kind that comes from having the words, insight, and legitimacy to lead with confidence. Together, we read to grow, keep learning, and refine our lens to spot the best opportunities. As Jamie Dimon says, “Great leaders are readers.”

Disclaimer: This content is for informational purposes only and does not constitute financial, investment, or legal advice. Readers should do their own research and consult with a qualified professional before making any decisions.

The big idea: AI will not save you. Change management might.

Reading time: 8 minutes

A line often credited to Charles Darwin captures it: survival belongs not to the strongest or the smartest, but to the most responsive to change. And in 2026, every company will have an AI component to their strategy, thus moving that line from biology to the boardroom.

Many conversations about AI focus on the technology and its providers, such as OpenAI, Anthropic, and Google. However, the real story belongs to the organizations that are trying to change how their people work on a Tuesday afternoon.

That is why leaders like Tobi Lütke at Shopify are asking employees to make AI use reflexive, not optional. The expectation is to reach for AI by default in every task where it can help. The question is how you turn that expectation into reality, in the grain of daily work.

Most people use AI today like a better version of Google. It is capable of much more. It can be an operating system, an invisible layer that rewires how work gets done. Once it is part of the daily routine, going without it feels like stepping back in time. The organizations that win will not be the ones who talk about AI the most. They will be the ones who reconfigure how work gets done.

At Ridgeline, AI has been implemented with this in mind, as a disciplined change management program touching company values, tooling, incentives, and career paths. This week, I’m thrilled to be joined by Erica Lied, Ridgeline’s Head of Human Resources and one of the key architects behind our endeavor to make AI reflexive at Ridgeline.

Erica and the team recently collaborated with an outside firm, Superintelligent, which rated our AI agent readiness at 71 out of 100. We were initially disappointed, but then we learned that it was the highest score they had ever given, and it was still labeling us in a “pilot” phase.

What’s valuable to our readers is what went into achieving that score. What follows are parts of the playbook that can be adapted and improved upon inside your organization. We hope some of what has worked for us can inform your AI program.

A simple map for difficult change

The first thing we suggest is to give yourselves a map for change. We like John Kotter. His central insight is that lasting change is a social process that follows a sequence. You build urgency, enroll the right people, make the future concrete, clear the path, and only then does behavior change.

We used Kotter’s model as the backbone for our own program at Ridgeline. From there, we translated each phase into specific decisions, routines, and experiments. We learned a lot!

How we ran this playbook at Ridgeline

Here is what we tried, what we learned, and what is working so far.

1. Start with urgency and curiosity, not fear (Create a sense of urgency)

• We licensed ChatGPT for every employee at Ridgeline. We opted to give everyone the tools and not restrict AI to a small group. That way we could all learn by doing.

• We did not lead with “AI will replace you.” We led with “AI will remove the grind from your day.”

• Innovation is a core value at Ridgeline, and so we tied our AI implementation to living that value.

• Our CEO also set a clear expectation and gave permission: use AI reflexively, in any task where it can help.

2. Build a coalition that people actually trust (Build a guiding coalition)

• We started with a champions group of twenty plus early adopters. Their job was not to write strategy. It was to run experiments in their own functions and share with best practices. Quickly, we saw the limits. Champions have day jobs. They can evangelize, but they cannot architect the whole system.

• So we added more structure. We created a Ridgeline AI group with four critical sub teams focused on being in the business and imagining the future state of work and reskilling the team.

• Senior leaders signaled that AI was part of their remit, not someone else’s project. The tiger team kept the trains moving.