I've found myself replying to different people about the early 2025 METR study kind of often. So I thought I'd try posting a top level thread, consider it an unsolicitied public service announcement.

You might be familiar with the study because it has been showing up alongside discussions about AI and coding for about a year. It found that LLMs actually decreased developer productivity and so people love to use it to suggest that the whole AI coding thing is really a big lie and the people who think it makes them more productive are hallucinating.

Here's the thing about that study... No one seems to have even glanced at it!

First, it's from early 2025, they used Claude Sonnet 3.5 or 3.7. Those models are no way comparable to current gen coding agents. The commonly cited inflection point didn't happen until later in 2025 with, depending on who you ask, Sonnet 4.5 or Opus 4.5

The study was comprised of 16 people! If those 16 were even vaguely representative of the developer population at the time most of them wouldn't have had significant experience with LLMs for coding.

These are not tools that just work out of the box, especially back then. It takes time and experimentation, or instruction, to use them well.

It was cool that they did the study, trying to understand LLMs was a good idea. But it's not what anyone would consider a representative, or even well thought out, study. 16 people!

But wait! They did a follow up study later in 2025.

This time with about 60 people and newer models and tools. In that study they found the opposite effect, AI tools sped developers up (which is a shock to no one who has used these tools long enough to get a feel for them). They also mentioned:

However the true speedup could be much higher among the developers and tasks which are selected out of the experiment.

In addition they had some, kind of entertaining, issues:

Due to the severity of these selection effects, we are working on changes to the design of our study.

Back to the drawing board, because:

Recruitment and retention of developers has become more difficult. An increased share of developers say they would not want to do 50% of their work without AI, even though our study pays them $50/hour to work on tasks of their own choosing. Our study is thus systematically missing developers who have the most optimistic expectations about AI’s value.

And...

Developers have become more selective in which tasks they submit. When surveyed, 30% to 50% of developers told us that they were choosing not to submit some tasks because they did not want to do them without AI. This implies we are systematically missing tasks which have high expected uplift from AI.

And so...

Together, these effects make it likely that our estimate reported above is a lower-bound on the true productivity effects of AI on these developers.

[...]

Some developers were less likely to complete tasks that they submitted if they were assigned to the AI-disallowed condition. One developer did not complete any of the tasks that were assigned to the AI-disallowed condition.

[...]

Altogether, these issues make it challenging to interpret our central estimate, and we believe it is likely a bad proxy for the real productivity impact of AI tools on these developers.

So to summarize, the new study showed a productivity increase and they estimate it's larger than the ~20% increase the study found. Cheers to them for being honest about the issues they encountered. For my part I know for sure that the increase is significantly more than 20%. The caveat, though, is that is only true after you've had some experience with the tools.

The truth is that we don't need a study for this, any experienced engineer can readily see it for themselves and you can find them talking about it pretty much everywhere. It would be interesting, though, to see a well designed study that attempted to quantify how big the average productivity increase actually is.

For that the participants using AI would need to be experienced with it and allowed to use their existing setups.

I want to add that this is not an attempt to evangelize for AI. I find the tools useful but I'm not selling anything. I'm interested in them and I stay up to date on the conversations surrounding them and the underlying technology. I use them frequently both for my own projects and to help less technical people improve their business productivity.

Whether AI agents are a good thing or not, from a larger perspective, is a very different, and complicated, conversation. The important thing is that utility and impact are two different conversations. There isn't a debate anymore about utility.

I know this probably won't stop people from continuing to derail conversations with the claim that developers are wrong about utility, but I had to try. It's just hard to let it pass by when someone claims the sky is green.

I understand that AI makes people angry and I think they have good reason to be angry. There are a lot of aspects of the AI revolution that I'm not thrilled about. The hype foremost, the FOMO as part of the hype, the potential for increased wealth consolidation really sucks, though I lay that at the feet of systems that existed before LLMs came along.

It's messy, but let's consider giving the benefit of the doubt to professionals who say a tool works instead of claiming they're wrong. Let them enjoy it. We can still be angry at AI at the same time.

Let me start off by saying that I'm exhausted by AI hype. Being interested in LLM agent technology (AI agent hereafter for brevity) means skimming over a lot of hype for one or two useful, semi reality based, bits of information. Maybe the part that I find the most frustrating is how effective the hype is. I don't know if there's ever been a hype cycle like this. Probably a big part of the reason for that is the internet has already proven, within living memory for most people, that technological revolutions really can change everything. Or mess everything up. Either way they generate a lot of economic activity.

So this post is not that. I'm not going to tell you about how AI agents are the second coming for Christ. I'm not selling anything.

Fairly early into learning about AI agents I wanted a way to connect to the agent remotely without hosting it somewhere or exposing ports to the internet. I settled on tailscale and a remote terminal and moved on, I rarely used it. Somehow the tiny friction of "Turn on tailscale, open terminal app, connect, run agent" was enough to make it not feel worth it.

I know I'm far from the only person who had the same "I want it remote" thought, the best evidence: OpenClaw. It's just one of those things that everyone naturally converges on.

If you're not familiar with OpenClaw, the TLDR is: Former founder with more money than he'll ever need vibecodes a bridge between instant messenger apps and LLM APIs. Nothing about it is technically challenging or requires solving any particularly hard problems. It almost immediately becomes the fastest growing GitHub repo of all time and is currently at number 14 for number of stars. It blew up the (tech) internet like very few things ever have. Within months he was hired by Open AI.

OpenClaw now does more than just connect messaging and agents, but I believe that one piece is the killer feature. My tailscale terminal solution, combined with a scheduled task or a cron job and some context files could already do all of the things that OpenClaw can do, and countless people had already implemented similar solutions. But I think it was the tiny bit of friction OpenClaw removed that was responsible for a lot its popularity.

I thought that was interesting but I have no interest in the security nightmare that is OpenClaw, or the "sentience" vibe for that matter, so I built my own tool.

Essentially it's just a light secondary harness combined with a bridge between Signal and Claude Code. It does some other things too, things I wished existing harnesses did, some memory and guidelines, automated prompts and reminders to wake the agent up and have it do stuff, some context to give the agent some level of persistence, make it less LLMy, less annoying. None of that is particularly interesting though.

Once I got it working (MVP took less than a day) and started playing with it, the OpenClaw phenomenon made a lot more sense. Somehow having the agent in a chat interface, with almost zero friction (just open the chat and send something) was cooler than it had any reason to be.

I can't explain it any better than that at the moment. Not only was it kinda fun, it lent itself to a whole range of "what ifs". What if it could do X? What if I wrote a tool that gave it Y capability? I've been experiencing that for some time, but somehow agent in your pocket has a different feeling.

Here's an example of a "what if". What if it could do our grocery shopping? I definitely want that. I already had a custom browser tool that I built for agent coding assistance so I was most of the way there. It was just a matter of teaching the agent to login and navigate a website, something they're already trained to do. Some hand holding, a few helper scripts, and an evening's worth of hours later and I had it working. The agent can respond to a shopping request by building a shopping list based on our most recent orders, presenting it to us for approval/edits in a Signal group chat, doing searches for any additional product requests and adding the finalized order to the cart. It could also checkout the order and schedule the delivery time but I'm doing the last 2 clicks manually for the time being. It's an idiot savant, it seems like a bad idea to give it access to my credit card. Maybe eventually.

The fact that I can handle shopping with a couple of signal messages feels effortless in a way that handling shopping by connecting to my PC terminal remotely via tailscale terminal wouldn't have. Especially when I can include people in the loop who have no interest in tailscaling anywhere. Everyone can use messaging apps.

I imagine before long solutions like this will be built in, either in the grocery websites and apps, or into the frontier harnesses themselves. There will probably be agents everywhere, for better or worse. Probably I'll wish that the agents would all fuck off. In the meantime it's exciting how easy it is to get these tools to do useful things.

I am currently reading my way thru Ted Chiang's guest column in the New Yorker, about why the predicted AI/Tech Singularity will probably never happen (https://www.newyorker.com/culture/annals-of-inquiry/why-computers-wont-make-themselves-smarter). ETA: I just noticed that article is almost 5 years old; the piece is still relevant, but worth noting.

Good read. Still reading, but so far, I find I disagree with his explicit arguments, but at the same time, he is also brushing up very closely to my own reasoning for why "it" might never happen. Regardless, it is thought-provoking.

But, I had a passing thought during the reading.

People who actually use LLMs like Claude Code to help write software, and/or, who pay close attention to LLMs' coding capabilities ... has anyone actually started experimenting with asking Claude Code or other LLMs that are designed for programming, to look at their own source code and help to improve it?

In other words, are we (the humans) already starting to use LLMs to improve their code faster than we humans alone could do?

Wouldn't this be the actual start of the predicted "intelligence explosion"?

Edit to add: To clarify, I am not (necessarily) suggesting that LLMs -- this particular round of AI -- will actually advance to become some kind of true supra-human AGI ... I am only suggesting that they may be the first real tool we've built (beyond Moore's Law itself) that might legitimately speed up the rate at which we approach the Singularity (whatever that ends up meaning).

The last couple of years have been a wild ride. The biggest parts of the conversation around AI for most of that time have been dominated by absurd levels of hype. To go along with the cringe levels of hype, a lot of people have felt the pain of dealing with the results of rushed and forced AI implementation.

As a result the pushback against AI is loud and passionate. A lot of people are pissed, for good reasons.

Because of that it would be understandable for people casually watching from a distance to get the impression that AI is mostly an investor fueled shitshow with very little real value.

The first part of the sentiment is true, it's definitely a shitshow. Big companies are FOMOing hard, everyone is shoehorning AI into everything they can in hopes of capturing some of that hype money. It feels like crypto, or Web 3.0. The result is a mess and we're nowhere near peak mess yet.

Meanwhile in software engineering the conversation is extremely polarized. There is a large, but shrinking, contingent of people who are absolutely sure that AI is something like a scam. It only looks like a valid tool and in reality it creates more problems than it solves. And until recently that was largely true. The reason that contingent is shrinking, though, is that the latest generation of SOTA models are an undeniable step change. Every day countless developers try using AI for something that it's actually good at and they have the, as yet nameless but novel, realization that "holy shit this changes everything". It's just like every other revolutionary tech tool, you have to know how to use it, and when not to use it.

The reason I bring up software engineering is that code is deterministic. You can objectively measure the results. The incredible language fluency of LLMs can't gloss over code issues. It either identified the bug or it didn't. It either wrote a thorough, valid test or it didn't. It's either good code or it isn't. And here's the thing: It is. Not automatically, or in all cases, and definitely not without careful management and scaffolding. But used well it is undeniably a game changing tool.

But it's not just game changing in software. As in software if it's used badly, or for the wrong things, it's more trouble than it's worth. But used well it's remarkable. I'll give you an example:

A friend was recently using AI to help create the necessary documents for a state government certification process for his business. If you've ever worked with government you've already imagined the mountain of forms, policies and other documentation that were required. I got involved because he ran into some issues getting the AI to deliver.

Going through his session the thing that blew my mind was how little prompting it took to get most of the way there. He essentially said "I need help with X application process for X certification" and then he pasted in a block of relevant requirements from the state. The LLM agent then immediately knew what to do, which documents would be required and which regulations were relevant. It then proceeded to run him through a short Q and A to get the necessary specifics for his business and then it just did it. The entire stack of required documentation was done in under an hour versus the days it would have taken him to do it himself. It didn't require detailed instructions or .md files or MCP servers or artifacts, it just did it.

And he's familiar with this process, he has the expertise to look at the resulting documents and say "yeah this is exactly what the state is looking for". It's not surprising that the model had a lot of government documentation in its training data, it shouldn't even really be mind blowing at this point how effective it was, but it blew my mind anyway. Probably because not having to deal with boring, repetitive paperwork is a miraculous thing from my perspective.

This kind of win is now available in a lot of areas of work and business. It's not hype, it's objectively verifiable utility.

This is not to say that it's not still a mess. I could write an overly long essay on the dangers of AI in software, business and to society at large. We thought social media was bad, that the digital revolution happened too fast for society to adapt... AI is a whole new category of problematic. One that's happening far faster than anything else has. There's no precedent.

But my public service message is this: Don't let the passionate hatred of AI give you the wrong idea: There is real value there. I don't mean this is a FOMO way, you don't have to "use AI or get left behind". The truth is that 6 months from now the combination of new generations of models and improved tooling, scaffolding and workflows will likely make the current iteration of AI look quaint by comparison. There's no rush to figure out a technology that's advancing and changing this quickly because most of what you learn right now will be about solving problems that will be solved by default in the near future.

That being said, AI is the biggest technological leap since the beginning of the public, consumer facing, internet. And I was there for that. Like the internet it will prove to be both good and bad, corporate consolidation will make the bad worse. And, like the internet, the people who are saying it's not revolutionary are going to look silly in the context of history.

I say this from the perspective of someone who has spent the past year casually (and in recent months intensively) learning how to use AI in practical ways, with quantifiable results, both in my own projects and to help other people solve problems in various domains. If I were to distill my career into one concept, it would be: solving problems. So I feel like I'm in a position to speak about problem solving technology with expertise. If you have a use for LLM powered AI, you'll be surprised how useful it is.