> What happened the last time that everything changed?

* Hardware era (pre 1995s) -> IBM, Intel, Microsoft, Apple

* Internet era (1994-2001) -> Amazon, Google, Meta, Salesforce

* Mobile era (iPhone+ era) -> Uber, Mobile Games, Youtube, Snapchat, Tiktok, Airbnb

* Cloud era (AWS+ era) -> AWS, GCP, Azure, Snowflake, Databricks and bunch of other data & database startups

AI era (ChatGPT+ era) -> Change is inevitable

Didn't Ben Evans previously shill for bitcoin, which is now omitted in the graphs for "disruptive technologies"?

This is a marketing Gish Gallop talk that pretends to invalidate counterarguments with a couple of fantasy graphs.

You can find the 4 versions of Benedict's deck here: https://www.ben-evans.com/presentations I appreciate the temporal view into this thinking. My interpretation:

Nov 2024: Don’t dismiss this; it may be the next platform shift. But the actual questions are still unsettled: scaling, usefulness, deployment, and business model.

May 2025: The model layer is already showing signs of commoditization, so the important question shifts toward deployment: products, use cases, UX, errors, and enterprise adoption.

Nov 2025: The capital cycle has become the story: everyone is spending because missing the platform shift is worse than overbuilding, but there is still no clarity on product shape, moats, or value capture. That creates bubble-like dynamics.

May 2026: Provisional thesis: models look likely to become infrastructure, while value probably moves up-stack into apps, workflows, product, proprietary data/context, GTM, and new questions made possible by cheap automation. But he is still explicitly calling this provisional.

If coding is such a big of LLM agents at the moment, I do not understand how far the best models will continue to shine and take the largest chunk of revenue. I am far away from tech hubs but I think better harness will utilize smaller models for more constrained, efficient and reliable coding agents.

In a way this is like distilling (but it is not) but you can make better harness (tackle more edge cases, better tool/function definitions, sandbox handling, bash management, DB management, deployment management, etc.) but extracting what LLMs know into code.

Maybe I am wrong but I would like to see custom software for the last mile (tiny/small businesses) becoming a reality. AI would eat the world of software but costs would go down since you can extract value upstream from the LLMs and spread downstream through tighter coding agents.

I am building a coding agent that will not be small - it will be a lot of code, carefully mixed roles (mimic a software dev shop) with separate tools available to different roles. And all this code is generated by other coding agents. https://github.com/brainless/nocodo

I am a nobody from nowhere with 18 years of software engineering behind me. I do not care about revenue. I just want to see a regular business owner's workflow going live on their own VPS.

>>Companies report ‘annualised’ revenue, defined as sum of previous 4 weeks multiplied by 13.

why is it multiplied by 13?

It will literally eat the world. Just like we crowded out wild animals in a few reserved areas, so will AI data centers crowd us out.

To quite Ilya Sutskever:

> I think it’s pretty likely the entire surface of the earth will be covered with solar panels and data centers.