Dear Readers,

Every pitch deck this year still carries a slide titled "Our Moat." Very few of them survive contact with the current release cycle. Open-weight models now sit about four months behind the closed frontier and cost a fraction as much. Routing has almost no barriers to entry. And the app layer gets eaten one vertical at a time, every time a lab ingests enough of the digital traces your customers leave behind.

So what is actually left? Today's guest has spent his career answering that question with models rather than vibes. Christian Catalini co-founded Lightspark, co-created Libra at Meta, founded the MIT Cryptoeconomics Lab, and is a Research Associate and Senior Lecturer at MIT. His answer is uncomfortable and, I think, correct: most of the network effects we treat as permanent are far weaker than they look, and exactly one kind gets stronger every time the models improve. He calls it a verification-grade network effect, and if he is right about it, the money in this industry lands somewhere very different from where most of us are pointing.

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Kim Isenberg

The closer you get to the AI frontier, the more you may start wondering if there are any moats left. The pace and marvel of progress may make you believe that the top AI labs will soon eat software. And after software, AI labs will eat the world, as most work can be expressed as software. Or at least some believe it can.

But you will also quickly realize that competition from open-weight models is not trailing far behind, and has a good chance of democratizing the bulk of token volume, even if not the top of the market. While there will always be demand for SOTA, it might be fleeting enough and concentrated in domains where the premium truly gives you a massive advantage over everyone else. Think domains where slightly better intelligence translates quickly into wins, from cyber to patentable matter. Luckily, many fields still encounter friction with the real world, and that friction may be more significant, and have more economic leverage, than a few months' AI lead.

If you believe closed SOTA and open-weights will co-exist, you'll inevitably start thinking about routing as the obvious aggregation and value capture layer, only to realize that barriers to entry in routing are modest at best, and that every vertically specialized app, from coding to finance, will bring its own. And if all you are left selling is cheaper inference, you can't ignore the competition from the leading labs: all they need to do is release an extremely low-cost model that matches the open alternatives to make routing not worth it (at least if all your customers care about is costs).

Once you move beyond routing, you're inevitably narrowing on the app layer. And focusing on this level of the stack may even give you a temporary reprieve from the moatpocalypse whiplash. Plenty of historical examples support the idea that the last mile is where all the friction usually hides, and therefore a great place to plan for value capture once things stabilize. This was true for other general purpose technologies such as crypto, despite endless discussions about fat versus thin protocols capturing the rents from open networks. The irony in that case was that while the technology started as subversive, value accrued to the most boring intermediaries: the ones with a banking charter.

But the app layer isn't perfectly shielded from AI either. Often, what's standing between your customers switching en masse to a series of prompts or a new tool is just more and better digital traces feeding the labs' training. A model that is ok at design today, given the right data, can get exceptionally good at solving most of your customers' needs. It's the fruit of the Bitter Lesson, eating one industry vertical at a time. From design to legal, finance and accounting, it is unquestionable that each major model release further erodes the need for specialized tools for a meaningful share of users. Jevons' paradox may come to your rescue, or not.

With more capable models, startups and incumbents alike can do more with less, and SaaS tools that used to be procured are now cloned in-house at a fraction of the cost. Of course, better models cut both ways, as the best SaaS providers can expand their offering and out-iterate internal teams by observing evolving needs across a wider customer base. They can also use this historical opportunity to switch from selling seats to selling the end-to-end work—assuming they are willing to underwrite the risks and become liability-as-a-service (LaaS) providers.

In many cases, you'll have a mix of SaaS contracts being cancelled when the internal version recreates the 10% of specific features a company actually needs, and larger contracts being signed where SaaS has become robust LaaS. Just look at how rapidly Stripe and Ramp have turned from fintechs to foundation labs focused on finance and payments: there will be no SaaSpocalypse for the firms that leverage the tools to capture more of the value chain.

Ultimately, we have to accept that turbulent phases of technological change are both extremely exciting and confusing. Whenever you feel like you've found something to anchor your beliefs on, you are quickly humbled and reminded by the latest breakthroughs of just how shaky our current assumptions are.

Ironically, AI is exacerbating consensus and muting contrarians because it is accelerating idea recombination: everyone is (inadvertently) distilling everyone else in real time. In 1890, Alfred Marshall defined industrial clusters as places where "the mysteries of the trade become no mysteries; but are as it were in the air". For AI, that place today is San Francisco. As robotics advances, it may well be one of China's river deltas.

Regardless, much of the debate around the surviving moats is inevitably online, on X. Some of it is obvious and priced in, and some of it is plainly wrong. So rather than giving you a list of moats, this piece will propose a few economic first principles. By running them through your own accumulated experience, you'll be able to map them to what is and isn't a moat in your specific domain.

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About a year ago, with Jane Wu and Kevin Zhang, we wrote a piece that delivered a very simple insight: anything that can be measured will be automated.

As more and better data comes online and is fed to models, they will be able to replicate more of human thinking and behavior. Because better AI also leads to better measurement, the dividing line between what stays human and what companies can defend is a fast-moving frontier.

Of course, some domains and problems are harder to codify than others: this is the land of unknown unknowns where assigning probabilities to events is extremely difficult, if not impossible. Financial markets, frontier R&D, and domains where the right answer doesn't matter because what we care about is that others converge with us on the same result: think "status games", and any good or service where social consensus is the product, from art to cryptocurrency.

If you buy our thesis, the resulting first principle is basic: you can either compete on advancing measurement, or you can double down on domains that escape measurement to begin with.

The first is one of the most thriving areas of work right now, as everyone has realized that you cannot train more capable models without capturing more of what shapes the actual workflows that matter in the economy.

The second is the land of the "meaning makers", and is one where human connection may matter. Although often people overestimate just how much a lower-cost service may become a substitute for a high human-touch endeavor if the results are comparable, from mental health support to forms of care.

In a follow-on paper with Jane Wu and Xiang Hui on the economics of AGI, we went one step further. If you believe measurement is the key to understanding where this is all going, then you quickly realize that agentic labor is useless whenever the agents are missing key measurement that we, as humans, are using every day in performing the job. We bring that missing measurement in through our experience whenever we perform verification, which is essentially deciding if a model output is ready to be used in production.

Verification is the ultimate bottleneck to AI scaling. It is the human act of bringing in tacit knowledge that the machines haven't seen yet. It is also something fundamentally different than having AI verify AI. It is, by definition, the residual of that process. The part of the code review that you cannot automate away without running into issues later. The part of the software factory that still needs to ping a human.

Which leads to a second principle: if execution becomes free and abundant for anything that can be measured, value accrues to the verification last mile.

How do you build a moat in the verification last mile? Fundamentally, there are only two ways: you either collect unique, proprietary data following the first principle, or you hire and retain some of the world's best experts in the relevant core domains. Of course, building better verification tooling can also be very profitable in the short term—and is the reason why Cursor was worth so much to SpaceX—but ultimately tooling has to evolve into a data play to be sustainable.

How do you acquire better data? A range of options here, from sensors, to digitizing additional workflows or interactions with the real world, to looping in and capturing the insights from talented humans. Crucially, the "weights" in the world's top expert brains are some of the most realistic simulators of a particular slice of reality, and the market will price that accordingly.

Once you view the evolution of AI through the lenses of what's measurable vs. not, and what needs verification vs. not, you cannot unsee that many network effects that have made digital platforms so resilient over the last decades are weaker than they appear.

Yes, distribution is a moat, especially in domains where you may need a license to operate in the first place, but not all distribution is equal. In many cases, AI agents will be able to help consumers and businesses multi-home, switch and migrate to new providers, and constantly hunt for better deals. They will also be able to aggregate demand across marketplaces, undoing some of the market liquidity benefits of the leading venues.

While most network effects lose their value as models become more capable, there is one type that, if anything, becomes stronger. We called it a verification-grade network effect, and it has a very particular shape: it is a network effect that gets stronger as you process more transactions and are able to observe more out-of-distribution events. Why? Because you can feed those back to your experts, have them perform verification, and then use the resulting digital trails to automate things further.

Summarized as a third principle: scaling verification faster than your competitors establishes verification-grade network effects. These allow you to build better and more capable models, widening your lead. We've seen early glimpses of this with Tesla's Full Self-Driving, Stripe's AI Payment Model, and Palantir's AIP, as well as through the advancement of tools like Claude Code, Codex, Cursor, and Devin.

How do you survive the moatpocalypse? It's simple.

You obsess about what is currently measured vs. not. You use what is measured to automate your execution, and what is not measured as an opportunity to build new, unique, proprietary data in the domains that matter to your workflows. To do so, you inevitably need to hire the best global talent to perform verification, and set up systems that can feed back their decisions, evals and experience into the next training run. That loop is how you establish and defend the only type of network effect left, the verification-grade one.

Of course, compute and the physical infrastructure running all of this are also extremely valuable, and are obvious bottlenecks as our insatiable appetite for inference scales. Same with regulatory friction and licenses in heavily regulated industries. But if you zoom out, the main reason why those two are bottlenecks to begin with relates to how you make progress in those domains. You make it, respectively, through friction with atoms and physics, or with regulators and policymakers.

So in the end, if you commit the common sin of adding a layer of indirection, the only principle you should care about is maximizing your path towards meaningful friction with the real world. This is the actual reason why many are obsessing about "agency".

In a world where intelligence is cheap, agency is a proxy for individuals and companies that are willing to drive through the grunt work of facing reality and collecting better measurement as fast as possible.

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Catalini's starting point is a rule he first published in Harvard Business Review with Jane Wu and Kevin Zhang: anything that can be measured will be automated. Because better models also produce better measurement, the line between what stays human and what gets absorbed keeps moving. That leaves two honest strategies, not ten. You either compete on pushing measurement into territory nobody has digitized yet, or you retreat into the domains that resist measurement altogether, where the product is social consensus rather than a correct answer.

Run that logic forward and you arrive at verification. An AI agent fails not because it lacks intelligence but because it lacks the measurement a human quietly supplies from experience. Verification is the act of importing that tacit knowledge, the part of the code review you cannot hand to another model without paying for it later. If execution becomes free for everything measurable, the value migrates to that last mile.

The strategic payoff is the part worth arguing about. Most platform network effects get weaker as models improve, because agents will help customers multi-home, switch providers and aggregate demand across marketplaces. One type moves the other way. Process more transactions, observe more edge cases, route them to your experts, and feed their judgments back into training. Catalini calls that a verification-grade network effect, and it is the only moat in his framework that widens rather than erodes with each model release.

The uncomfortable version of this argument is that most of what founders currently call a moat is a lease. Routing, distribution, marketplace liquidity and the app-layer feature set all lose value as the models get better, and the boards betting on them are underwriting a depreciating asset. What appreciates is the loop: proprietary measurement nobody else has, world-class experts making the calls machines cannot yet make, and a pipeline that turns those calls into the next training run.

The examples he names all share one shape. Tesla's Full Self-Driving, Stripe's payment model, Palantir's AIP and the coding agents sit where the work actually happens, watch a human decide whether the output was good enough to ship, and feed that judgment back into the next version. The clearest price the market has put on that trace so far is the 60 billion dollars SpaceX paid for Cursor, and note what it bought: a tooling company, not a model lab.

Which makes his closing line the practical one to take into Monday. In a world where intelligence is cheap, agency is just a proxy for whoever is willing to do the grunt work of colliding with reality first.

Christian's research turns the questions founders usually argue about on X into models you can actually test: what gets automated, what stays human, and where the rents end up once execution is cheap. The framework in today's essay comes out of two of those papers, one with Jane Wu and Kevin Zhang in Harvard Business Review, and a follow-on with Jane Wu and Xiang Hui on the economics of AGI.

Before his current work he was the Theodore T. Miller Career Development Professor and Associate Professor at MIT Sloan, and he built the MIT Cryptoeconomics Lab into one of the earliest serious academic programs on digital money. At Meta he co-created Libra, later Diem, and served as Chief Economist of the Diem Association, which is about as close as anyone has come to designing a global payments network from a blank sheet inside a large platform.

He now co-founds and advises companies applying that same lens to payments and open networks, including Lightspark, and writes regularly on where the AI and money stacks collide.

Sources:

🔗 Christian Catalini, Xiang Hui, Jane Wu, Some Simple Economics of AGI (arXiv, February 2026) https://arxiv.org/abs/2602.20946

🔗 Christian Catalini, Jane Wu, Kevin Zhang, What Gets Measured, AI Will Automate (Harvard Business Review, June 2025) https://hbr.org/2025/06/what-gets-measured-ai-will-automate

🔗 Christian Catalini, Intelligence Wants To Be Free (June 2026) https://catalini.substack.com/p/intelligence-wants-to-be-free

🔗 Epoch AI, Open models lag state-of-the-art closed models by 4 months https://epoch.ai/data-insights/open-closed-eci-gap

🔗 Stripe launches the first AI foundation model for payments (Stripe Sessions 2026) https://stripe.com/newsroom

🔗 SpaceX to acquire Cursor parent Anysphere for $60 billion (CNBC, June 2026) https://www.cnbc.com/2026/06/16/spacex-spcx-cursor-acquisition-ipo.html