… 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

The Age of AI is a Crisis of Faith

The fundamental question facing online spaces in 2025 is no longer can AI pass as human? but rather can humans prove they’re not AI?

This represents a profound shift from technical doubt to existential uncertainty. It’s a crisis of faith where the bedrock assumption that we interact with other humans online has collapsed. And I’m not being hyperbolic. In 2024, bot traffic exceeded human traffic for the first time in a decade, hitting 51%. We’ve crossed the threshold. The internet is now majority non-human.

When I personally veer onto the Internet, particularly places like LinkedIn or Substack or any social media’s comment section, Dead Internet Theory truly shines as a valid hypothesis. This once-fringe conspiracy theory which speculates that the Internet is now mostly bots talking to bots is now many people’s lived experience — Read More

AI 2027 and AI as Normal Technology were both published in April of this year. Both were read much more widely than we, their authors, expected.

Some of us (Eli, Thomas, Daniel, the authors of AI 2027) expect AI to radically transform the world within the next decade, up to and including such sci-fi-sounding possibilities as superintelligence, nanofactories, and Dyson swarms. Progress will be continuous, but it will accelerate rapidly around the time that AIs automate AI research.

Others (Sayash and Arvind, the authors of AI as Normal Technology) think that the effects of AI will be much more, well, normal. Yes, we can expect economic growth, but it will be the gradual, year-on-year improvement that accompanied technological innovations like electricity or the internet, not a radical break in the arc of human history.

These are substantial disagreements, which have been partially hashed out here and here.

Nevertheless, we’ve found that all of us have more in common than you might expect. — Read More

In 1950, when computing was little more than automated arithmetic and simple logic, Alan Turing asked a question that still reverberates today: can machines think? It took remarkable imagination to see what he saw: that intelligence might someday be built rather than born. That insight later launched a relentless scientific quest called Artificial Intelligence (AI). Twenty-five years into my own career in AI, I still find myself inspired by Turing’s vision. But how close are we? The answer isn’t simple.

Today, leading AI technology such as large language models (LLMs) have begun to transform how we access and work with abstract knowledge. Yet they remain wordsmiths in the dark; eloquent but inexperienced, knowledgeable but ungrounded. Spatial intelligence will transform how we create and interact with real and virtual worlds—revolutionizing storytelling, creativity, robotics, scientific discovery, and beyond. This is AI’s next frontier. — Read More

Almost two decades ago, Hewlett-Packard (HP) was the first tech company to exceed $100 Billion annual revenue threshold in 2007. At that time, HP had 172k employees. The very next year, IBM joined the club, but IBM had almost 400k employees.

Today’s megacap tech companies all exhibit a common characteristics: their growth is pretty much decoupled from their headcount. Intuitively, this might not be a news to anyone, but when I sat down and carefully jotted the numbers, the extent of the decoupling even before Generative AI truly came to the scene was a bit astonishing to me. — Read More