A rational conversation on where AI is actually going | Benedict Evans

Benedict Evans framed the conversation around a clear, repeatable thesis: AI is a fundamental platform shift on the order of the internet or mobile, but not obviously larger. From that starting point he broke the topic into three lenses he uses in his six‑monthly presentation: capital (who pays and who builds the models), deployment (how software and workflows change), and change (how jobs, value and industries evolve). He repeatedly returned to an analogy of being in "1997" for the internet — the technology is transformative but immature, adoption is jagged and most meaningful use cases haven’t been discovered yet.

Evans pushed back against simple apocalypse narratives. He argued historical patterns since the 1800s show automation removes tasks but also unlocks new work; the hard, practical reality is enterprise change is slow and political. He pointed to enterprise sales cycles (~18 months), long transformation projects requiring multi‑person teams for months, and the recent phenomenon of AI labs increasing headcount (OpenAI and Anthropic among those mentioned) and hiring consultancies or forward‑deployed engineers to help customers integrate models into workflows. That, he said, explains why consultancies are booming rather than disappearing despite AI: companies need people to design processes, untangle politics, and operate hybrids of legacy systems and new AI components.

A central theme was task vs. job. Evans used VisiCalc/spreadsheets and accounting as a historical example: automation often makes previously expensive tasks cheap, which can either compress costs or expand the market (the Jevons paradox / price elasticity). Some jobs are largely a single task (elevator attendant → pressing a button) and are easily automated; others have a surrounding set of human judgments — politics, customer insight, product design — that resist simple automation. He also contrasted two economic architectures: if foundation models become undifferentiated, commodity infrastructure (think AWS or mobile networks), pricing power sits up‑stack with app builders and distributors; if models remain differentiated and vertically integrated, model labs could capture more value. He compared that debate to telecoms (global mobile revenue ≈ $1T, CAPEX ≈ $200B) to illustrate how impressive technical complexity doesn't guarantee high margins at the infrastructure layer.

Evans emphasized measurement gaps and definitional drift. He criticized the lack of transparent usage metrics from model labs (e.g., no public ChatGPT DAU), which makes econometric assessment of impact difficult. He cited a Livermore Lab study (end of 2024) estimating U.S. data‑center water use at ~0.017% of national water consumption to argue some headline environmental claims are overstated in aggregate, though local impacts are real. He also said AGI and superintelligence remain fuzzy concepts: we have no complete theory of intelligence or of why current models scale so well, so long‑term forecasts are speculative.

On politics and culture, Evans described rising anti‑AI sentiment as a "big, fuzzy mess" — a mix of legitimate harms (e.g., deepfakes, platform harms) and exaggerated or misdirected fears. He recounted the UK Post Office scandal as a cautionary example of how buggy systems can ruin lives, showing technology’s harms can be operational and legal as well as theoretical. Finally, his practical advice for listeners was concrete: don’t stick your head in the sand; learn the tools, experiment, and understand what AI makes possible in your domain. He closed by acknowledging uncertainty — "it depends" and "presume radical uncertainty" — while urging people to submerge themselves in the technology so they can identify the new jobs, workflows, and products that will follow the current wave of automation. He pointed listeners to his deck and newsletter for further reading and pledged continued, skeptical tracking of how value and adoption evolve.