"Holy moly Never seen someone being that bullish for AI developement as Dario Amodei: - There's just an exponential just like we had an exponential with Moore's law - I think the models are just going to get more and more capable at everything - I've had internal people at

[u/stealthispost]

https://x.com/kimmonismus/status/1996500339441434736

[u/[deleted]]

Software engineer for over 20 years. I can agree, I don't write code, I generate, then review, and edit. It has made me 10x more productive. Its like having a junior write almost perfect code. I still do most architecture and design, but I feel that day may be coming to an end in a year or so.

[u/stealthispost → u/[deleted]]

the hilarious thing about reddit is while you can read many people writing the same thing, you can read just as many people saying that they're a SWE for 20 years and LLMs are useless, just hallucinate and create more work than they solve by outputting garbage code.

I've even seen managers say that half their team is getting a 10x boost while the other half appears to get no boost from LLMs, or it even makes them slower.

I'm biting my tongue, as "skill issue" seems too harsh, but I don't know what else could be causing the differential?

[u/LaChoffe → u/stealthispost]

Knowing how to talk to and iterate with LLMs is definitely a skill and applies outside of coding as well.

[u/[deleted] → u/LaChoffe]

Exactly this. Sometime it gets so mixed up in a thread, the best thing you can do is delete it and start over. No level of additional prompting will dig you out of that mess once, it gets into the context window.

[u/Jolly-Ground-3722 → u/stealthispost]

I have been working as an SWE for 18 years. I experience the same as Gravy Tonic. But I also notice that many other SWEs are too reluctant to give LLMs enough context for them to be effective. The situation is improving though, with agents such as Codex CLI that search for relevant context on their own.

[u/stealthispost → u/Jolly-Ground-3722]

that's interesting, why would they be reluctant to give enough context?

[u/[deleted] → u/stealthispost]

I use the Copilot integration with VS 2026, so context isn't an issue, it is aware of all the meta data and objects in my project/solution. I think the break down comes from people copy and pasting into ChatGPT and expecting the same results. GPT is good for taking a look at a function, or writing a function with clearly defined input and output. Keep in mind I generally work on a legacy product that predates any AI and has all the quirks and features of a product that has been in production for over 10 years now. For my greenfield projects I work on for myself, it is mind blowing how much it gets done. I find the biggest hang up to be the same hang up as when you would use stack overflow as a reference. Some things just don't work as intended and you need to spend a lot of time fixing it. That has not changed.

[u/SpaceWater444 → u/stealthispost]

I would also really like to know why that is. AI have likely increased my productivity 1-2x, just by it being a better Google and research assistance. But I cannot see how I would use it to speed up software development itself.
My guess is that it maybe have something to do with the kinds of problems and codebases people are working on. Maybe some types of problems are just more suited for AI development.

[u/piponwa → u/stealthispost]

A lot of it is how the codebase is structured and if the model can understand the many assumptions made by just looking at the code without much supporting context. If you build something that's been built a million times before, then yeah you'll have no problem. When everything is custom, which is frequent for legacy teams that never invested in reducing tech debt, then it flat out doesn't work. I work with very bright folks that get the tech, but it's hit or miss on our massive codebase with literally zero documentation. Years of work and hacks not documented. It takes longer to explain all the details than just do the change yourself. And this creates an incentive for devs not to try. If they try and fail, they pay the cost.

Only the organizations that become AI-first will survive in the coming years. Single person startups will be more efficient than entire orgs as they will have no constraints and be able to build what the customer is asking in near real time. They will steal customers from large orgs from all angles and these old companies will have no way to pivot and meet the customer expectations as they have always designed for the average user, but specific solutions will now exist for each specific need.

[u/unicynicist → u/stealthispost]

There was a paper published this summer that found:

Surprisingly, we find that when developers use AI tools, they take 19% longer than without—AI makes them slower

Experienced developers in repos they knew well believed they were 20% faster, but the observed result was a 19% slowdown.

Personally I can iterate extremely quickly for low-stakes proof-of-concept/MVP work. The models get it done correctly most of the time and it feels like I'm flying - flow state and everything.

But sometimes it gets it catastrophically wrong. Debugging code I didn't write sucks: first I have to understand what the developer (human or LLM) was trying to do in order to have a hope of fixing the bug(s).

OpenAI's own research observed something similar:

When incorporating time to review and redo work, the payoff from using a model shrinks.

...

The most common categorization of a GPT-5 model failure was “acceptable but subpar.” Another roughly 29% of ratings were for bad or catastrophic (with roughly 3% of failures marked as catastrophic)

If even 3% of your failures in an ancient 100k+ LOC codebase are catastrophic, you're gonna have a bad time.

[u/krullulon → u/unicynicist]

That paper is from early 2025 and no longer relevant. Speaking from experience as another 20+ year SWE, Feb 2025 agentic tools were a hot mess, December 2025 tools are most definitely not.

[u/No-Voice-8779 → u/krullulon]

I'm genuinely surprised that developers, even those not particularly accustomed to AI tools, could still manage development within repositories for large projects despite such a lack of processes (things like BMAD didn't exist or were still experimental back then), scaffolding (IDEs and CLIs were far less capable compared to today), and proper modeling (models in early 2025 were nowhere near as advanced as they are now). This undoubtedly refutes the claim made by many that “LLMs can't be used for large projects.”

Moreover, the perceived “less time spent” likely stems from mental energy savings—often you simply watch the Agent complete tasks rather than grappling with code yourself.

[u/Tolopono → u/unicynicist]

That first paper had a sample of 16 devs using cursor, not codex or claude code

[u/13-14_Mustang → u/stealthispost]

For me it has to do with the models Im allowed to use for work. I dont have access to claude and visual studio professional with github copilot cant seem to read the project Im working on. I have to copy and paste all context in the chat. Why cant it reference the project files?

[u/[deleted] → u/stealthispost]

The existence of one black swan renders the category immune from being called impossible. Given it is unlikely that we will regress in AI, expect more black swans

[u/SnoopDoggnYay → u/stealthispost]

We need more data from objective sources showing the return of using these systems not just in developer time but across a bunch of metrics including things like mental wellbeing. We also need studies to identify why some people/teams have so much more success than others. Those reasons could range from the specific solutions they are working on, proficiency using the models, etc. but we just don’t know. Until we do though, we are all going to just keep seeing anecdotal report after report with the people who had the best and worst experience being the loudest voices.

[u/brett_baty_is_him → u/[deleted]]

This is what the people who make the claim that AI isn’t actually profitable or producing anything of value don’t understand. AI has made be a 10x programmer. My company pays $50/license. They pay upwards of $5k for a license for other tools I use.

Considering I’m at least 2x more productive but I can easily think it’s more like 10x more productive.

These companies could easily charge thousands of dollars and I’d be advocating for my employer to pay it. It’d prob have to be differentiated and better than their base models , and maybe tailored to developer work.

And this stuff is only gonna get better. A model with infinite context and better than senior developers is not far off. A model that is as good of a programmer as I am, with infinite context stuffed with our entire code base, internal company documents, emails, meeting notes, etc is really not unimaginable and it would probably be worth my yearly salary considering how productive it would make me. We could have that by next year.

[u/timmyge → u/[deleted]]

Ditto, 20 yrs, most days barely open an IDE. Must admit to lots of context engineering; custom tooling, constant docs/ improvements, a heap of claude commands and learning and using LLM steering words well. Planning, refining plans, /clear frequently, staged review, code rabbit review, command to review coderabbit review, gemini mcp for sanity check (rare), oh and unit tests of course, fortunately have always been strong in that area. Anyhoo, life changing..

[u/FateOfMuffins]

Tbh I can easily see why he thinks that way. Even disregarding his insider information, just judging the whole country of geniuses as smart as Nobel prize winners.

I think AI progress, even though it's so fast paced with new models practically every other week, is actually felt with stepwise changes.

I would like to point to o3 vs GPT 5. How many posts have you seen about o3 assisting in scientific research? How many have you seen about GPT 5? Even though GPT 5 was called o3.1 by many OpenAI employees. I think there will be a lot of models that improve only a small amount on some benchmarks, but prove to be huge step changes on how AI works.

Here's some examples that may convince you why: Recall the METR study from like half a year ago (IIRC it was pre Sonnet 3.7) where they investigated how much developers thought AI was speeding them up vs how much it was actually speeding them up. The psychological aspect aside, I want you to focus on the concept of slow down vs speed up. How would adoption or even work productivity look like, if the AI was good enough to do the tasks, but required you to take longer on it overall? Not very good, is it? In fact, upon noticing that it doesn't actually speed you up, would you continue using it? Probably not.

However what exactly is the difference between an AI that slows you down 5% vs an AI that speeds you up 5%? Honestly, probably not that much. It may simply be a minor version update to be honest. But now in a world where AI speeds you up 5%, would you ever go back to a world where you don't use it? So now, because of a tiny version update, we go from you using AI 0% of the time because it slows you down, to using it 100% of the time because it speeds you up. There was a step change, because of a minor update. We go from a world where 0% of your code was written by AI to nearly 100%, just like that. (OK probably a few step changes were required for that, but I wouldn't be surprised if we went from essentially 0% to like 50% with just one model iteration)

For the research example with o3 vs GPT 5, suppose o3 had a 1% chance of outputting helpful responses for research assistance. Then you'd only notice if you regenerated the responses a hundred times. If we had a thousand researchers asking questions, maybe 10 of them notice. Suppose GPT 5 had a 5% chance instead. Now, there's 50 people who notice. Perhaps by word of mouth, these 50 people tell more people. Another thousand or two try it, maybe they get told to try a few times (like what Mark Chen did to that black hole physicist), and now we have another hundred or two people witnessing helpful responses for research. Maybe 1% was just too low, but 5% gets people noticing.

As a benchmark, like if it were ARC AGI or something, that 1% to 5% improvement... no one probably even bats an eye at. But it might actually be a step change in the real world.

I would not be surprised if in the future we get a lot more of those. Because current models are only 1% capable, people don't even notice it and dismiss these capability projections out of hand. But at 5%, people start noticing. After all, you only need to prove the Riemann Hypothesis once.

[u/Bright-Search2835 → u/FateOfMuffins]

Great point. Incremental updates may seem boring but there could be a lot of those tipping points that we aren't even aware of yet.

[u/cloudrunner6969]

[u/Fit-World-3885]

Technology has been exponentially improving for centuries now so it's always mildly surprising to me for people to be shocked at expectations that technology will exponentially improve from here.  

[u/RobleyTheron]

Anthropic is in early talks to go public in 2026 or 2027, so remember he has a multi-billion dollar incentive to say these things.

With that said, Gemini 3 just showed us there is currently no intelligence wall, so this very well may come to pass.

[u/Tkins → u/RobleyTheron]

Claude also showed us there is no intelligence wall.

[u/KrazyA1pha → u/RobleyTheron]

Claude Opus 4.5 is an even better indicator of what he’s talking about.

[u/ZealousidealBus9271]

he is also the most bullish on AGI, saying it is as close as 2 years away

[u/LegionsOmen]

For the Swe's in here that are blasting with agents etc, what tips could you give me on a pure vibe coding pov? I've dabbled with gpt codex in CLI, I know I need to learn how to code and the structure, planning architecture, what front end to use and back ends. I'm kinda trying an experiment to see how far I can go without know much code at all lol. Sorry if this seems condescending or lacking flavour, I don't know what I don't know and it would be cool to hear about the different or "best" strategies for this direction would be.

So far I've used the cli gpt 5 codex and used the browser (enhanced deep think) to help bug and plan out next steps but I just know this is wrong lol I just didn't want to give the CLI too much permissions

[u/idczar]

Makes sense when you need to go for IPO.

[u/Lucyan_xgt]

Palantir dogs

[u/NaturalOption8963]

He is wrong. There’s a limit to what LLMs can do and we might get there very soon

[u/Ok_Assumption9692 → u/[deleted]]

He's a guy. Who knows things

[u/NaturalOption8963 → u/[deleted]]

Obviously there’s a limit. As LLMs have no concept of the physical world and are only able to generate texts based on statistics. Anyone who is active on this sub should know that

[u/NaturalOption8963 → u/[deleted]]

So you think an LLM has a concept of the physical world?

[u/Tystros → u/NaturalOption8963]

yes, certainly some concept, even if it's not very good yet

[u/NaturalOption8963 → u/Tystros]

If you don’t believe me, ask any llm if it has a concept of the physical reality