Benedict Evans

Agentic coding is AI’s first unmistakable product-market fit, while nearly every broader market-structure question remains unanswered. Evans says it went from “kind of useful to really changing everything,” with customers effectively pulling products out of vendors’ hands. But because this barely worked six months ago, predicting engineering-team design, junior hiring, or software careers three years out would be “insane.”

Foundation models appear structurally headed toward commodity economics unless their providers can prove durable differentiation or move up-stack. Evans sees no clear network effect, little differentiation beyond spending, and customers unlikely to care which model powers a SaaS product—just as they rarely ask which cloud hosts it. His deliberately hedged challenge is: the argument “deterministically looks like these things will be commodities,” so “explain to me why they won’t be.”

Today’s token economics are a transitory scarcity regime, not evidence of permanent pricing power. Users can receive “10 grand worth of tokens” for $20 or accidentally incur a $10,000 bill, echoing mobile data circa 2009–10. With perhaps $1 trillion–$2 trillion of capex arriving and models becoming “100x, 200x” more efficient annually, supply, usage, pricing, and ROI must find a different equilibrium.

The mobile-network precedent warns that vast usage and infrastructure spending need not translate into attractive returns. Mobile traffic rose roughly 1,500–2,000 times; networks collectively have about $1 trillion in revenue and spend around $200 billion annually on capex, yet their stocks have been flat for 20 years while “all the cool stuff got built by somebody else.” The central investor question is whether models become low-margin infrastructure or gain operating-system-like leverage—something Evans notes models currently lack.

AI is likely to create “way more software,” but that does not reveal which incumbent SaaS companies survive. Cheaper development, previously impossible functionality, and new combinations of probabilistic models with deterministic systems should expand supply and competition. Evans expects some percentage of SaaS companies to be wiped out, yet argues investors cannot identify them confidently enough to justify indiscriminately derating the entire sector by 50%.

The largest opportunities will come from making previously impossible products, not merely rebuilding old software with AI. Evans’s examples progress from finding a coat in an image to recommending alternatives and finally choosing one from a user’s Instagram that changes their look “but not too much”; enterprise systems might synthesize calls, emails, telemetry, and analytics to recommend pricing changes that improve churn. “The important stuff is not doing the old thing but more. It’s doing something new that you couldn’t have done with the old thing.”

Financial gravity will slow AI capex before technical possibility does, while much of the resulting value may be competed away as consumer surplus. Microsoft, Meta, and Google are each on course to spend more than 50% of revenue on capex, while the big four guide to roughly $700 billion collectively; Evans says the world simply cannot sustain $10 trillion a year of AI infrastructure. Even when AI turns a week-long DCF into a ten-second task, firms may perform 50 analyses without charging more—until today’s “magic” becomes something computers have seemingly always done.

🔗 Original source & video: The Economics of AI Usage and What’s Next For SaaS | Benedict Evans on a16z

Generative AI may be a platform shift, but Evans sees no evidence yet that it exceeds the internet or smartphones. His “centrist” position is that AI is “as big a deal as the internet or smartphones, but only as big a deal as the internet or smartphones.” The upside remains unusually unknowable because neither intelligence nor why these models work so well has a usable theory.

AI demand is enormous yet sharply uneven, making workflow adoption the central commercial question. ChatGPT has “800 or 900 million” weekly active users, but only about 5% pay; Evans cites roughly 10–15% of developed-world users engaging daily and another 20–30% weekly. The investor question is why five times more people understand the product yet “can’t think of anything to do with it this week or next week.”

A bubble is likely, but neither timing nor ultimate infrastructure demand can be modeled with confidence. “If we’re not in a bubble now, we will be,” Evans argues, while distinguishing 1997, 1998 and 1999-style conditions is impossible in real time. Compute requirements may fall 20, 30 or 40 times a year even as usage explodes, reproducing the impossible bandwidth forecasts of the late 1990s.

The product opportunity above the models is likely to center on specialized products that encode workflows, validation and institutional knowledge. “People buy solutions, they don’t buy technologies”: law firms want legal-discovery software, not translation and sentiment-analysis API calls. The discussion points toward purpose-built applications around general models rather than raw model access alone.

OpenAI’s 800–900 million weekly users constitute distribution, not yet a durable moat. Evans sees brand and default status but no clear network effect, feature lock-in, proprietary infrastructure or cost advantage: “You get a bill every month from Satya.” OpenAI must race simultaneously toward a defensible product ecosystem and infrastructure involving NVIDIA, Broadcom, AMD, Oracle and new pools of capital.

The incumbent impact is asymmetric: Google can absorb AI, Meta and Amazon face deeper product questions, while Apple may remain insulated unless computing itself changes. Google can fund frontier models and make AI a feature of search and ads; Amazon could finally improve discovery beyond commodity retail. Apple is endangered if apps disappear, but even an LLM-first world may still demand “a nice big color screen,” camera and battery—in other words, something much like an iPhone.

The deepest disruption will expose businesses whose profits depend on routing, bundling or friction rather than their stated product. Evans’s progression is feature adoption, new capabilities, then potentially “pull the whole industry inside out”; newspapers discovered they were partly light-manufacturing, local-distribution and trucking companies, while an LLM might erase defenses built on tedious administration. To call AI bigger than the internet, however, he would need to see something “actually a person” outside narrow guardrails: “What we have right now isn’t that.”

🔗 Original source & video: AI Eats the World: Benedict Evans on the Next Platform Shift