"My picture of the present in AI" by ryan_greenblatt
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What is the main topic discussed in this episode?
My Picture of the Present in AI by Ryan Greenblatt Published on April 7, 2026 In this post, I'll go through some of my best guesses for the current situation in AI as of the start of April 2026.
You can think of this as a scenario forecast, but for the present, which is already uncertain, rather than the future. I will generally state my best guess without argumentation and without explaining my level of confidence. Some of these claims are highly speculative while others are better grounded, certainly some will be wrong. I tried to make it clear which claims are relatively speculative by saying something like I guess, I expect, etc., but I may have missed some. You can think of this post as more like a list of my current views rather than a structured post with a thesis, but I think it may be informative nonetheless. In a future post, I'll go beyond the present and talk about my predictions for the future.
What is the current state of AI R&D and software acceleration?
I was originally working on writing up some predictions, but the predictions about today ended up being extensive enough that a separate post seemed warranted. Heading AI R&D Acceleration and Software Acceleration More Generally Right now, AI companies are heavily integrating and deploying AI tools in their work and getting significant, but not insane, speed-ups from this. At the start of 2026, the serial research engineering speed-up was around 1.4x, but it's now reached more like 1.6x at OpenAI and Anthropic with more capable models, better tooling, adaptation, humans learning how to use models better, workflow changes, people shifting what they work on to areas that benefit more from AI assistance, etc., and some diffusion.
As in, using AI tools provides as much of an engineering productivity increase as if people operated 1.6x faster when doing engineering, in addition to literal coding, engineering includes less central activities, like determining what features to implement, deciding how to architect code, and coordinating a meeting with other engineers. For many specific engineering and research tasks, people can now leverage AIs to do that task with much less of their time, for example 3-10x less human time, but other tasks see much smaller speed-ups. People are shifting their work toward two kinds of tasks, lower value, tasks where AIs are particularly helpful, and tasks they wouldn't have been able to do without AI due to insufficient skills in knowledge.
When people think about AI uplift, they naturally think about something like how much longer would it take me to do the work I'm currently doing without AI. But this isn't the right question, because people have adapted their workflows, completing more tasks where AI helps a lot and doing tasks they wouldn't otherwise have the skills for. This bias is the answer upward relative to how much productivity is actually increased. The question that better captures the actual productivity value is something like how much would we have to speed you up before you'd be indifferent between that speed up and having AI tools? I think the answer to this, the serial speed up I quoted above, is around 1.6x right now, while the answer to the prior question might be more like 3-20x.
the speed up is also lower than it might seem because the resulting code is generally sloppier, less reliable, and less well understood than if it was just written by human engineers. It's more common for no one, including the AIs themselves, to have a great understanding of how some code works or how exactly it fits into a broader system, and for example what assumptions it makes, making some issues more frequent. Other types of errors are made less frequent because AIs make testing less expensive. But for much of AI R&D, low reliability and poor understanding isn't catastrophic. Also, experimentation is typically done in smallish relatively self-contained projects where the AIs and the humans can get a decent understanding of what's going on.
This engineering speed-up isn't distributed evenly. I expect Anthropic is getting a larger speed up than OpenAI which is getting a substantially larger speed up than GDM.
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Chapters
7 chapters
1
What is the main topic discussed in this episode?
0:00–0:58
2
What is the current state of AI R&D and software acceleration?
0:58–5:07
3
How are AI engineering capabilities evolving?
5:07–10:33
4
What are the implications of AI misalignment?
10:33–15:31
5
How could AI impact cybersecurity in the near future?
15:31–16:54
6
What are the potential risks of AI in bioweapons?
16:54–21:00
7
How is AI affecting the economy today?
21:00–21:04
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