Brian Stever

Structural Equity advocate for a creator-first AI economy. | Showrunner | @OslerAI Snack Labs | Host @SickboyPodcast | Applied AI @UArizona

[Brian Stever @itsbrianstever]

We fucked up the last tech era. Let's not do it again. ( a thread 🧵)

Tech created massive value. Most people didn't see it. We worked more, costs went up, and the gains went to shareholders while we got convenience and surveillance.

[Brian Stever @itsbrianstever]

@amazon I finally figured out why you put the smiles on your packaging :)

[Brian Stever @itsbrianstever]

@icreatelife

[Brian Stever @itsbrianstever]

@sama Amen

[Andrej Karpathy @karpathy]

Judging by my tl there is a growing gap in understanding of AI capability.

The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.

But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along.

So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions.

TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.

[Brian Stever @itsbrianstever]

@orcdev Here’s mine http://localhost:3000

[Brian Stever @itsbrianstever]

@heyblake https://t.co/bz2mNdBkoq

[Brian Stever @itsbrianstever]

December saw a 5 year low in job openings...

This data was published just two days ago - will we see a rise in January or will the downward trend continue??
https://t.co/0YBCPqnxl1

[OpenAI @OpenAI]

We worked with @Ginkgo to connect GPT-5 to an autonomous lab, so it could propose experiments, run them at scale, learn from the results, and decide what to try next. That closed loop brought protein production cost down by 40%.

[Brian Stever @itsbrianstever]

@JoshLDomingues I’d love to DM you

[Brian Stever @itsbrianstever]

December data coming in hot in 2 days!

https://t.co/0YBCPqnxl1

[Brian Stever @itsbrianstever]

@ericw_ai Very interesting points raised. I feel this from my own experience as a programmer

[Brian Stever @itsbrianstever]

Are companies in the United States still hiring? We’re tracking job postings to find out.

[Brian Stever @itsbrianstever]

@Innovate_FB @DaveShapi Also the code is public :)

[Brian Stever @itsbrianstever]

@DaveShapi I made this project to track job openings and unemployment - I feel like this is the year we start to see a sharp decline in hiring. Repo is public and curious to get your thoughts: https://t.co/bz2mNdAMyS

[Brian Stever @itsbrianstever]

I used NextJs as my framework, convex for db, and the BLS api primarily. Funnily enough I’m actually Canadian but I chose to start with the US because 1. The data set was easy to access 2. There’s more data purely from a population basis. I’d love to add Canada and other countries though

[Brian Stever @itsbrianstever]

@igorzmitrovich @DaveShapi added chatgpt release date :)

[Brian Stever @itsbrianstever]

@igorzmitrovich @DaveShapi That's a great idea re: chatgpt release - I can easily add that in now. It will take a bit of time to source data globally. There is a db that covers most developed countries with standardized labor stats though (OECD). I'll definitely look into it

[Brian Stever @itsbrianstever]

@denicmarko https://t.co/bz2mNdAMyS

[Brian Stever @itsbrianstever]

@DaveShapi updated to include participation: https://t.co/0YBCPqmZvt

very interesting!

[Brian Stever @itsbrianstever]

@DaveShapi Definitely - the BLS dataset already has this. I'll just add another view. Will work on it now!