Artificial intelligence is the hot new college major.

This semester, more than 3,000 students enrolled in a new college of artificial intelligence and cybersecurity at the University of South Florida in Tampa.

At the University of California, San Diego, 150 first-year students signed up for a new AI major. And the State University of New York at Buffalo created a stand-alone “department of AI and society,” which is offering new interdisciplinary degrees in fields like “AI and policy analysis.”

The fast popularisation of products such as ChatGPT, along with skyrocketing valuations of tech giants such as chipmaker Nvidia, is helping to drive the campus AI boom. — Read More

In economics, deflation is the opposite of inflation—it’s what we call it when prices go down instead of up. It is generally considered harmful: both because it is usually brought on by something really bad (like a severe economic contraction), and because in and of itself, it has knock-on effects on consumer behavior that can lead to a death spiral. One of the main problems is that if people expect prices to keep going down, they’ll delay purchases and save more, because they expect that they’ll be able to get the stuff for less later. Less spending means less demand means less revenue means fewer jobs which means less spending and then whoops you’re in a deflationary spiral.

… This isn’t really an economics blog post, though. I’m thinking about deflation because it parallels a recent pattern I’m seeing in startups. (So I guess you could call it a micro-economics blog post?) The basic mechanism is: (1) it’s easier and cheaper to build software now than ever before; (2) it seems like it probably will keep getting easier and cheaper for the forseeable future; so (3) why bother building anything now, just build it later when it’s cheaper and easier. — Read More

… You will never be able to detect the use of AI in homework. Full stop. All “detectors” of AI imo don’t really work, can be defeated in various ways, and are in principle doomed to fail. You have to assume that any work done outside classroom has used AI.

…[T]he goal is that the students are proficient in the use of AI, but can also exist without it, and imo the only way to get there is to flip classes around and move the majority of testing to in class settings. — Read More

Artificial Intelligence is reshaping America’s $9.4 trillion labor market, with cascading effects that extend far beyond visible technology sectors. When AI transforms quality control tasks in automotive plants, consequences spread through logistics networks, supply chains, and local service economies. Yet traditional workforce metrics cannot capture these ripple effects: they measure employment outcomes after disruption occurs, not where AI capabilities overlap with human skills before adoption crystallizes. Project Iceberg addresses this gap using Large Population Models to simulate the human-AI labor market, representing 151 million workers as autonomous agents executing over 32,000 skills and interacting with thousands of AI tools. It introduces the Iceberg Index, a skills-centered metric that measures the wage value of skills AI systems can perform within each occupation. The Index captures technical exposure, where AI can perform occupational tasks, not displacement outcomes or adoption timelines. Analysis shows that visible AI adoption concentrated in computing and technology (2.2% of wage value, approx $211 billion) represents only the tip of the iceberg. Technical capability extends far below the surface through cognitive automation spanning administrative, financial, and professional services (11.7%, approx $1.2 trillion). This exposure is fivefold larger and geographically distributed across all states rather than confined to coastal hubs. Traditional indicators such as GDP, income, and unemployment explain less than 5% of this skills-based variation, underscoring why new indices are needed to capture exposure in the AI economy. By simulating how these capabilities may spread under scenarios, Iceberg enables policymakers and business leaders to identify exposure hotspots, prioritize investments, and test interventions before committing billions to implementation. — Read More

#strategy

Ilya Sutskever, in a rare interview with Dwarkesh Patel, laid out his sharp critique of the AI industry. He argues that reliance on brute-force “scaling” has hit a wall. While AI models may be brilliant on tests, they are fragile in terms of real-world applications. He believes the pursuit of general intelligence must now shift from simply gathering more data to discovering a new, more efficient scientific principles. — Read More

… One [theory] is that integrating all of this data together is extremely valuable, and that the rush to do it—according to The Information, every major enterprise software company is building an “enterprise search” agent—is a very sensible war for a very strategic space. Google became the fourth biggest company in the world by being the front door for the internet; of course everyone wants to be the front door for work. And this messiness is just an intermediate state, until someone wins or we all run out of money.

A second theory, however, is that platforms aren’t as valuable as we think they are. For a decade now, Silicon Valley has come to accept, nearly as a matter of law, that the aggregators are the internet’s biggest winners. But aggregation theory5 assumes that production flows cleanly from left to right: From producers, to distributors, to consumers, with the potential for gatekeepers along the way. “Context”—especially if MCP succeeds in making it easy for one tool to talk to another—is not like that. Slack aggregates what Notion knows; Notion aggregates what Slack knows; ChatGPT aggregates what everyone knows, and everyone uses ChatGPT to aggregate everything. Producers are consumers, consumers become producers, and everyone is a distributor. There aren’t people orderly walking into one big front door; there are agents crisscrossing through hundreds of side doors.

In that telling, the right analogy for context isn’t content, but knowledge. — Read More

As AI models become as common as electricity, the real competitive advantage shifts. Discover the five critical second-order effects of AI commoditization and learn how industries are preparing for a transformed business landscape in 2026.

For the last several years, the conversation around artificial intelligence has been dominated by a narrative of scarcity and exclusive power. Having access to a state-of-the-art AI model was a golden ticket, a competitive moat that only a handful of tech behemoths could afford to build. That era is rapidly coming to a close. We are now entering the age of AI commoditization, where powerful models are becoming a standardized, widely accessible utility—much like cloud computing or electricity before them

This seismic shift is being accelerated by fierce market competition, the proliferation of high-performance open-source models, and aggressive pricing from major cloud providers. The first-order effects are already visible and dramatic. We’re seeing a race to the bottom on pricing, with some analyses showing that the cost of using top-tier models dropped by over 80% in just one year. This democratization of access is just the beginning. — Read More

It’s cheaper to cover a hole in the wall with a flat screen TV than fill it. Stuff is cheap, services are expensive. AI is about to fix that.

You either believe AI will displace jobs or you think its hype. I think that’s the wrong question. The right question is how does AI reshape the economy. 

AI will force us to reconsider commerce, consumerism and the norms of our economy. We will enter a world where consumers buy less stuff, but with much higher conversion. Middle-income consumer populations will have less disposable income as their jobs come under pressure from AI. Meaning consumerism ceases to be the driver of economic growth. — Read More

OpenAI CEO Sam Altman has conceded that the company is facing “rough vibes” and “economic headwinds” just days after Google reclaimed the AI performance crown with Google’s Gemini 3 Pro launch. According to The Information, a leaked memo from last month starkly contrasts with Altman’s public trillion-dollar ambitions and he reportedly warned employees that revenue growth could plummet to single digits by 2026. — Read More

#strategy

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’.

This post includes the slides for that presentation as well as videos for Evans’ presentations on YouTube. — Read More