Tag: AI disruption

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AI Business Investing Technology

The Scarcity Portfolio: Navigating Sovereign Debt, Wafer Bottlenecks, and Orbital Compute

Today I was watching the interview of Gavin Baker by Patrick O’Shaughnessy on his Invest Like the Best podcast. Like prior conversations this was another fascinating excursion into the mind of a sophisticated and very successful tech venture investor.

During the conversation, Patrick asked Gavin what agents he was using that were especially helpful and he mentioned one which summarizes YouTube podcasts and videos for him. Like most of us Baker just doesn’t have the time to watch or listen to them himself so good summaries are really helpful.

Turns out I’ve been working on a Google Gemini Gem that does this for me. When Baker mentioned his I fired up the new Gemini 3.5 Flash model and asked it to summarize the Baker interview.

Later in the conversation Baker used the term “battlefield AI” which caused me to go back to Gemini again to learn more about that. The results were so interesting that I asked Gemini to create a syllabus for a semester class on these subjects. After that I asked it to convert our whole conversation into a Markdown file so I could share it. You’ll find it below.

I found this whole experience pretty stunning. I came away very impressed with Gemini 3.5 Flash both for the quality of the responses but also the sheer speed. Wow!

Anyway I hope you enjoy the following!

Continue reading “The Scarcity Portfolio: Navigating Sovereign Debt, Wafer Bottlenecks, and Orbital Compute”
Categories
AI Consulting

The Judgment Layer

An analyst’s note about the CEO of one of the largest consulting companies making comments at an investor conference includes a line that deserves more attention than it got: “token volume used on a project isn’t a proxy for AI maturity.”

Translation — clients are burning money on frontier models for problems that don’t need frontier models, and they’re not getting the outcomes they expected.

This firm’s CEO offered this as a business opportunity. I read it as a confession.

The old consulting model was simple: client has a technology problem, firm deploys humans to solve it. Billing followed effort. The new problem is different in kind — clients have an AI strategy problem. They know they’re supposed to be using AI. They’ve heard the word “frontier.” They’re spending accordingly. They just don’t know why, and the outcomes are showing it.

So the CEO is right that there’s an opportunity here. The value proposition shifts from implementation to judgment — not deploying AI, but knowing when not to deploy the expensive one. Matching capability to problem. Being trusted enough to tell a client that their $50M frontier model contract is solving a $500K problem.

Here’s the irony that the comment skates past: that advice is structurally difficult for a large consultancy to give.

The business model that built consulting firms was billing for doing. The more you deploy, the more you bill. Helping a client spend less, or choose the cheaper model, or run a narrower project, is genuinely good advice that the incentive structure actively works against. You don’t grow a $70 billion professional services firm by talking clients out of scope.

The judgment layer, if it becomes the real value, requires something closer to a doctor’s relationship with a patient than a contractor’s relationship with a client. Doctors get paid whether they prescribe or not. The value of the visit is the diagnosis — including the diagnosis that says you don’t need the expensive intervention. Consultants, historically, get paid to prescribe, and paid more when the prescription is larger.

There’s a reason we trust doctors with that asymmetry and not contractors. Licensing, malpractice, professional norms built over centuries — all of it exists to align the incentive. Consulting has none of that infrastructure. What it has instead is reputation, which is slower-acting and easier to game.

Whether the large firms can actually make the shift — rather than just reframe the same billable-hours model in the language of AI optimization — is the real question the market is wrestling with. The CEO’s comment is genuinely perceptive about where client value lies. It’s less clear that consulting firms are currently built to capture it honestly.

Categories
AI

The Thousandfold Door

There is a pattern hiding in the history of human progress that we almost always miss in the moment — and almost always recognize, with some embarrassment, in hindsight.

Richard Koch and Greg Lockwood called it price-simplifying. The insight, drawn from decades of studying transformative businesses, is deceptively simple: when you cut the price of something dramatically, demand doesn’t respond proportionally. It responds exponentially. Halve the price, and you don’t double the market. You might multiply it by ten, or a hundred, or a thousand. Reduce the price to a tenth of what it was, and you may unlock a market a hundred thousand times larger than the one that existed before.

The math sounds implausible until you start listing the examples. Henry Ford didn’t just make cars cheaper — he conjured an entirely new civilization of mobility. Ikea didn’t discount furniture — it democratized the designed home. Southwest Airlines didn’t offer cheaper seats — it invented the era of the spontaneous trip, transforming flying from an executive luxury into something a college student books on a whim.

In every case, the price drop didn’t just serve existing demand more cheaply. It revealed latent demand that nobody knew existed — desire that had been sitting dormant, waiting for the door to open.

I keep returning to this framework when I think about what is happening with intelligence right now.

For most of human history, access to high-quality thinking — legal analysis, financial modeling, medical reasoning, strategic advice, elegant writing — has been extraordinarily expensive. Not just in money, but in time. You needed years of specialized education, or the budget to hire someone who had it. The price of cognition was high enough that vast swaths of human need simply went unmet. Problems went unsolved not because solutions didn’t exist, but because the expertise required to find them was priced out of reach.

AI is a price-simplifying event for intelligence itself.

“If the price is halved, demand does not double. It increases fivefold, tenfold, a hundredfold, a thousandfold or more.”

We are currently debating AI as though the primary story is substitution — one form of labor replacing another. But Koch and Lockwood’s framework suggests the more consequential story is what happens on the other side of the price collapse. When the cost of a legal opinion drops from $500 an hour to nearly zero, the question isn’t just “what happens to lawyers?” It’s “how many people who never could afford a lawyer now get access to one?” When the cost of a business plan drops from a consultant’s retainer to an afternoon conversation, the question isn’t just “what happens to consultants?” It’s “how many ideas that never got funded now have a fighting chance?”

The thousandfold door is opening. We can see it in the aggregate usage numbers, in the explosion of one-person companies, in the PhD-level tutoring now available to a student in a country that couldn’t previously afford it. What we cannot yet see is the full shape of what walks through.

That’s the thing about exponential demand. It doesn’t announce itself. It just accumulates quietly, and then one day someone looks at the numbers and realizes the world has changed.

Questions to Consider

  1. The Latent Demand Question: What human needs — currently unmet because expert help is too expensive — do you think AI will unlock first? Where is the largest reservoir of suppressed demand?
  2. The Ford Parallel: Henry Ford’s price simplification didn’t just create a new industry — it reshaped cities, suburbs, culture, and geopolitics in ways he never anticipated. What are the second and third-order consequences of dramatically cheaper intelligence that we’re not yet taking seriously?
  3. The Distribution Problem: Price-simplifying events historically don’t distribute their benefits evenly — early advantages tend to compound. Who is best positioned to walk through the thousandfold door first, and does that concern you?
  4. The Demand We Can’t Imagine: Koch and Lockwood’s most unsettling point is that the new demand often didn’t previously exist in any visible form — it was created by the price drop itself. What entirely new human behaviors, industries, or creative forms might AI’s price simplification call into existence that we currently have no framework to anticipate?
Categories
AI Business

The Moat Drains

There is an old metaphor in investing — the “moat.” Warren Buffett popularized it: the idea that the best businesses are castles surrounded by deep, wide moats that keep competitors at bay.

For the past two decades, enterprise software companies built some of the most impressive moats in the history of capitalism. Sticky customers. Multi-year contracts. Switching costs so high that even dissatisfied clients stayed put. The moat wasn’t just deep — it was filled with concrete.

This morning, JP Morgan’s equity research team quietly suggested the concrete may be cracking. See also this recent Substack post by Jordi Visser.

In a note lowering price targets across their software coverage, the bank cited a striking phrase: “the exponential pace of AI proliferation raises doubts about competitive moats and the defensibility of software companies.”

They’re not alone in thinking this. But there’s something significant about seeing it written in the careful, hedged language of a major Wall Street research report.

When the analysts who model ten-year discounted cash flows start abandoning that framework — replacing it with simpler one- and two-year profitability multiples — it’s a signal worth decoding.

The shift in valuation methodology is itself the story. DCF analysis — the gold standard of software valuation for a generation — requires confidence in a company’s earnings trajectory over many years.

JP Morgan is saying, plainly, that they no longer have that confidence. The window of visibility has collapsed. When you can’t see more than a year or two out, you stop pretending you can.

“Investors are less comfortable underwriting defensive growth over multi-year periods.”

What’s driving this?

The suspicion — increasingly well-founded — that AI is not just a feature to be added to existing software products, but a force that restructures the value chain entirely.

If an AI agent can perform the function that previously required a $50,000-per-year SaaS subscription, the moat doesn’t just shrink. It evaporates. The castle becomes a historical curiosity.

Vertical software stocks — the specialized platforms serving specific industries like healthcare, construction, or legal — currently trade at 10 to 25 times EBITDA, according to the note. The S&P 500 as a whole trades at 15 times. The message embedded in those numbers is sobering: many of these once-premium businesses are being re-rated toward commodity valuations, and some may not have found their floor yet.

JP Morgan’s preferred companies in this environment are those with upside to 2026 revenue estimates and those they view as “defensive to AI proliferation.” That second phrase is the one I find myself turning over. It implies a new taxonomy is forming in the market — not growth vs. value, not cyclical vs. defensive, but AI-vulnerable vs. AI-resistant. That’s a categorization that didn’t meaningfully exist three years ago.

The moat metaphor may need an update. In the age of AI, the question is no longer how wide the moat is. It’s whether the castle itself still needs to exist.

Questions to Consider

  1. The Moat Inventory: If you were a software CEO this morning, which parts of your product would you genuinely consider defensible against AI substitution — and which would you privately admit are vulnerable?
  2. The Valuation Signal: When Wall Street abandons long-term DCF models in favor of near-term multiples, is that a temporary adjustment to uncertainty — or a permanent reset in how software businesses will be valued going forward?
  3. The New Taxonomy: JP Morgan implicitly divides the software world into AI-vulnerable and AI-resistant. What characteristics do you think actually define that divide — and can a company move from one category to the other?
  4. The Buffett Test: Buffett’s moat metaphor was built for a world of slow-moving competitive forces. Is the concept still useful in an era of exponential technology change, or do we need a new mental model entirely?
  5. The Timing Question: Is this re-rating of software companies a rational early response to a real structural shift — or is Wall Street, as it often does, overcorrecting in the short term for a change that will take much longer to fully materialize?
Categories
AI Work

The Rungs We Leave Behind

“Companies, too, must prepare. To thrive they need not only to make the best use of ai, but also to find and nurture the best people to work with it. Some back-office workers will lose their jobs. But others with tacit knowledge of the business may be trained for new roles. The biggest mistake would be to stop hiring young people altogether. That would not only choke off the pipeline for future talent, it would rob businesses of AI natives. Instead, companies should rethink the type of work they offer young people—less grunt labour, more judgment and analysis; speedier rotations across the business so they gain insight that ai cannot have; piloting new roles and trying new approaches.”
— The Economist

There is a specific kind of quiet panic in boardrooms today. It isn’t just about the bottom line; it’s about the lineage of knowledge. For decades, the “entry-level” role served a hidden purpose. It wasn’t just about getting the spreadsheets done; it was about osmosis. By doing the “grunt labor,” a young professional absorbed the culture, the politics, and the subtle, unwritten rhythms of an industry—what we call “tacit knowledge.”

We often view AI as a replacement for the “boring stuff,” but we forget that the boring stuff was the soil in which expertise grew. If we remove the bottom rungs of the ladder because a machine can climb them faster, how do we expect anyone to reach the top?

The shift from “labor” to “judgment” is a profound psychological leap. We are essentially asking 22-year-olds to skip the apprenticeship of execution and move straight into the apprenticeship of discernment. This requires a radical empathy from leadership. We cannot simply hand a junior employee a powerful AI tool and expect them to know what “good” looks like if they’ve never seen “bad” up close.

The “AI native” brings a fluidity with technology that my generation might never fully replicate, but they lack the scars of experience that inform intuition. To thrive, companies must become teaching hospitals rather than just production factories. We need to create “judgment-rich” roles where young people are encouraged to experiment, to fail safely, and to rotate through the business at a pace that keeps them ahead of the automation curve.

The disruption is here. It is unavoidable. But there is a soulful middle ground: using AI to strip away the drudgery while doubling down on the human mentorship that transforms a “worker” into a “leader.” The goal isn’t just to make the best use of AI; it’s to ensure that when the AI provides an answer, there is still a human in the room with the soul and the context to know if that answer is right.