At a typical AI conference, there's usually much discussion of "the future of software", but it usually ends with agents producing traditional deterministic code in increasingly convoluted ways. Agents are contained to customer support popups on a website, or made multiplayer in the 67th clone of Conductor/Slack. Not very futuristic.

Returning to AIE to keynote the System One aka "Jev Day" track, Diogo Almeida of TypeSafe AI talked about the MISSION of Jev — not the 100 clones of the API, but the point of why it was necessary to establish the entire category of System One models, as a reaction against the entire category of Instruct-tuned LLMs that became ChatGPT.

In his words, "Where is all the automation?!?" Where is the GDP per capita impact of AI? Why is everyone working longer hours despite having Millennium-Prize-solving AI?

The simple answer is we overtuned for RLHF, and unsurprisingly tuning for more human feedback only serves to keep the human in the critical path of work rather than remove them. But there's deeper cost/latency/reliability issues with using unspecialized general purpose LLMs for high volume work, for obvious reasons.

You can watch the talk for the rest of the pitch, but I really, truly want to draw your attention to this one slide:

This visually contrasts "smart software" with traditional software/LLMs-in-agent-harnesses. As Diogo puts it, "people still have LLM stockholm syndrome". This is why TypeSafe disagrees with all the clones that fixate on the "Decision Model" framing - 1) because you want smart LLMs to make decisions too, but 2) you are missing the message on being able to liberally sprinkle "System One" model calls everywhere INSIDE application code to make smarter software instead of increasingly convoluted forms of "concat strings together and hope for the best" AI engineering. This is both the simplicity and the genius of the Jev breakthrough - not just the speed, not just the cost, not just the calibration, but all these things working in concert to make it a promptable alternative to the switch statement, with noul, score, and choice being composable primitives that you can rely on to "just work".

What applications do System One models have, and what -other- kinds of System One models might we need? This is the realm of "Software Research", what Diogo calls the age of Smart Software experimentation we are entering. "Research" vs "Software" are usually in opposition because they represent unknowns vs knowns, unproven theories vs proven techniques, but here it is appropriate because we are researching and rethinking how we think about what Software can do, and what we have to manually write edge cases/robustness for because normal code is "dumb".

He gave many examples - one to do with smarter memory (sharing state between agents, making memory log(N)), and another to do with intent alignment ("do what I meant, not what I said"), realtime intelligent security, new voice interfaces, and even "cursed software" (shoutout /r/BadUIBattles)... but the overall takeaway is that Smart Software allows software to do what you mean to do instead of do only what was preprogrammed, even if you aren't a "token billionaire" or "AI native engineer".

Coming from the AI Engineer side, this unlocks an entire age of automation that we originally anticipated with the Software 3.0 essay, but have spent the last 9 AIE conferences waiting for. I'm glad that it's here and I hope you find the next big Smart Software opportunity.