CHAPTERS
What’s changed since “AI Eats the World”: product strategies diverge and coding hits PMF
Evans reflects on the last 12–18 months of generative AI and argues the big update is strategic divergence: not just “bigger models,” but clearer product focus. Agentic coding is the standout use case with undeniable product-market fit, while many other consumer and enterprise uses remain tentative or experimental.
Why coding was the first breakout use case—and why job implications are still unknowable
Coding’s success is partly path-dependent: developers were the earliest heavy users and naturally tried to apply LLMs to software creation. Evans cautions that conclusions about engineering org structure and career ladders are premature because this shift only recently started working well and pricing/capacity constraints distort outcomes.
OpenAI’s “everything all at once” phase vs. refocus—and the consumer usage gap
Evans describes OpenAI’s attempt to build value above the model via many adjacent product bets (ads, commerce, payments, browser, social). The core challenge persists: outside of coding and a few niches, most users aren’t daily—there’s a big gap between Valley power users and mainstream weekly dabblers.
AI adoption through the lens of past platform shifts: acceleration, friction, and pricing shocks
Comparing AI to PCs, the internet, and mobile, Evans argues adoption always accelerates because each wave stands on prior infrastructure. Early phases are messy and unreliable, and AI is also experiencing a familiar “pricing shock” moment similar to mobile data’s transition to sustainable pricing models.
Where value gets captured: infrastructure layers vs. platforms vs. apps
Evans frames a central uncertainty: do model providers capture lasting value like Windows/iOS, or do they become commoditized infrastructure like telcos/ISPs/chip layers? He notes many historic infrastructure builders transformed the world but failed to capture the bulk of profits, which migrated up the stack.
Why prediction is hard right now: multiple paths before the S-curve narrows
Evans resists deterministic forecasts, arguing history helps generate questions more than answers. At this stage, many outcomes are plausible, and clarity only arrives once adoption steepens and the market “narrows” toward a dominant structure.
“Foundation models are a commodity” argument: weak differentiation, chatbot limits, and abstraction
Evans lays out a chain of reasoning for commoditization: model differentiation seems hard to sustain, the chatbot UI is a limited V1, and the real productization requires tooling, data, workflows, and interfaces that model labs can’t build for every domain. He likens model choice to cloud choice in SaaS—often abstracted away from customers.
Next questions: on-device/cheaper models, and what AI means for specific industries
Evans highlights new focal points: when “good enough” models shift workloads off the most expensive frontier systems, and how AI changes pyramid-shaped professional services (law, consulting, banking). He argues the hardest, most important implications become industry-specific, not purely technical.
Automation, Jevons paradox, and the search for what becomes newly possible
Framing AI as automation, Evans explores elasticity: cheaper work can mean doing the same for less, more for the same, or entirely new categories of activity. The important frontier isn’t “old work, faster,” but new capabilities that were previously impossible or not even imagined.
Ads, shopping, and discovery agents: redefining how products are understood and sold
Evans argues commerce and advertising are promising because LLMs can interpret what products “are” and why people buy them, not just correlate SKUs. This enables richer shopping agents (visual search, comparisons, personalized style guidance) and helps explain improving ad conversion and recommendation performance.
Enterprise stack rewired: more software, new abstraction layers, and where probabilistic systems fit
Evans predicts “way more software,” not less, as AI becomes embedded both as a feature within systems of record and as a cross-system synthesis layer. The core design tension is placing probabilistic LLM behavior alongside deterministic databases and workflows—especially around exception handling and judgment calls.
Capex, scarcity, and the ‘magic’ problem: how much investment is sustainable, and what’s the ROI?
Evans notes current AI capex is approaching “global infrastructure scale,” with big tech spending levels far above traditional capital-intensive industries by revenue percentage. The present moment is extreme disequilibrium—pricing, usage, and ROI measurement are still unstable, and some benefits look like consumer surplus that gets competed away.
Closing synthesis: AI becomes invisible infrastructure—then ‘of course computers can do that’
Evans ends with a historical reminder: every major computing wave felt magical and uncertain, then became mundane. His baseline expectation is that AI will embed everywhere, create winners/losers, and eventually fade into the background as an assumed capability of modern computing.
