I was worried this time last year that by this time this year, companies would have slashed their engineering teams down to a handful and everything would be driven by mostly autonomous agents with human guidance. But it just hasn't happened. Do I write all my code with an agent now? Yes. Can you just give an agent a desired outcome and let it work, unsupervised? Absolutely not. I can produce more code than I used to, but if I want it to be good, to be stable, to do what the product manager and designers want, it's only about 2 to 3 times more code than before. And that productivity is impacted by the fact that I'm reviewing 2 to 3 times more code than before (and you have to review, even more so now than before, because if you just let opus or gpt 5 do its thing, you'll get some terrible results, and I've found a lot of engineers on my team are just letting it do it's thing without a lot of iteration).
>I was worried this time last year that by this time this year, companies would have slashed their engineering teams down to a handful and everything would be driven by mostly autonomous agents with human guidance. But it just hasn't happened.
I find this somewhat puzzling. I thought things were moving quickly, but at this time last year I couldn't even get Claude (using Cursor) to spin me up a service skeleton that would compile, let alone do anything meaningful.
I know it feels like a long time somehow, but it was only between November and February that things started to actually somewhat work without significant hand holding. Even now, it seems like we're still figuring out how to fully leverage the current models and tooling, even in organizations that have largely gotten on board.
It's not all that surprising that people were worried and believed this. The AI companies and infrastructure companies partnering with them have spent a lot of money and time trying to convince people this is the case year after year. The critical clue people miss is that everyone claiming that has very clear financial incentives to convince people that's the case even when they know it isn't. Anyone who was actually building with LLMs and judging for themselves based on its performance knew fully well that wasn't the case year after year.
I've said this before: if anthropic (et al) thought they genuinely had a shot at replacing even 30% of white collar work, they would ABSOLUTELY NOT warn ANYONE. They would do what oil, leaded gas, and cigarette companies did. Swear under oath this is completely safe, commit GRIEVOUS societal harm that you explicitly promised wouldn't happen, and then end up in history books instead of jail for reasons beyond my ability to fathom.
No. The very fact they are trying to "warn" us means it's all marketing.
This has been corroborated for me on the engineering front that I can't find a single IC I respect who actually thought there was any evidence AI was going to live up to the hype. I saw a lot of people I always thought were idiots/sycophants/brown nosers go insane with AI. Never saw anyone id trust to help me cross a street blindfolded say more that "I may be wrong, but I'm not seeing any evidence yet".
Fwiw , you're conflating multiple things and consequently drawing premature conclusions.
It can be massively over hyped for it's current capacity and decimate the white collar work.
A lot of the difference of opinion is down to their point of view. At my dayjob, LLMs will not live up to anything because the enterprise is not structured to take advantage of it's strength. That's unlikely to change within the foreseeable future.
I strongly suspect you mostly talked with people coming from just such a background, because it's hard to go beyond our own bubbles
Sure, naturally. And yet parent commenter is remarking that simultaneously no AI-true-believer startups have supplanted the old money, and simultaneously despite much talk the bigcos have not slashed headcount to tiny AI-powered teams.
Depends on the bigco IMO. I'm not sure what kind of layoff numbers for Year Three of genAI would grab your attention, but I assure you, hundreds of thousands have mine.
> at this time last year I couldn't even get Claude (using Cursor) to spin me up a service skeleton that would compile, let alone do anything meaningful
I've been using it to do this for 2 years now. And many people with me. The change you mention is one of is primarily one of Overton windows, of vibes.
Companies are putting a ton of effort into getting to that point of having agents do the work unsupervised. Whoever gets there first is going to be the winner.
I personally don't think it's possible and I haven't written a line of code since Sept 2025.
There's an AI psychosis going on right now, especially among the execs or management class, and we all gotta nod our heads in agreement and burn through tokens.
Agents already run unsupervised, and they can code unsupervised too. The real question is what worthwhile work we should point these capabilities at. Nobody has really cracked that yet.
The same worthwhile things we were working on before agents. I’m personally using them to autonomously build open-source Shopify for every vertical. I set out building it before AI, but AI actually makes the dream feel achievable.
If you have runway, it’s a good time to start your own thing or join someone who is. Personally, I cannot force myself to wade through slop PRs from careless coworkers. If that’s the job now, I’d rather run a hotdog stand or something.
Luckily, I don’t think things are that dire. I think the companies issuing AI mandates are manufacturing sawdust, and even if it works, it would just enable them to burn through customer goodwill in record time as they make user-hostile decisions free from engineer pushback.
These are going to be a few tough years, but I think the opportunities to start something new are everywhere.
I don't think it's possible either. DHH pointed out in the most recent episode of Rework that AI removes a lot of the barriers to shipping code, therefore making it possible to build in lots of different directions in ways that was prohibitive to many organizations in the past. But this isn't necessarily a good thing, companies still need to understand what to build in order to ship a cohesive product. AI is great for prototyping and refining use cases in ways that are far superior to static figma designs, etc., but it is not a replacement for taste and execution.
But a slop machine that haphazardly shoots features against the wall to see what sticks still isn't a winning product strategy in 2026. And the problem I see increasingly is that so much energy is being focused on how to deliver with AI internally and externally that is not being expended to advance a company's product. I believe more and more in the idea that for many startups and companies, the actual "customers" are the investors and the product-market fit that companies seek is the product of the company itself, because this is all being driven from the top down, not by customers and users in the market asking for AI features.
But that's the nature of the beast isnt it? A probabilistic token predictor will all always have some errors from a human perspective, more and more energy, money and resources will always be needed to control and direct towards desired outcomes.
> I believe more and more in the idea that for many startups and companies, the actual "customers" are the investors and the product-market fit that companies seek is the product of the company itself
In many respects this reflects the growing K-shaped nature of our economy. Average consumers don't matter because you really just need a small cohort of wealthy individuals to be hyper-invested in your product, 'regular' consumption is therefore just a way to keep things relatively on rails rather than the actual economic driver.
All of these AI-first companies don't actually have any market fit, so what they're doing is selling an imaginary product so that they can get investments and loans. As you said the company is the product.
“ But this isn't necessarily a good thing, companies still need to understand what to build in order to ship a cohesive product”
In other words writing more code means fk all without vision, strategy, taste etc. Google has had lots of engineers on many projects - look at the grave yard. The constraint on progress is not code.
Wake me up when this dumb experiment is over. Some of us are years ahead it seems until others get in the same page of understanding
I can similarly output 2-3x more code but everything stalls down to me having to review and integrate in a meaningful way the moment I am the one that has to maintain that code.
It's eerie to observe collaborators output code they don't understand, spend days chatting with Claude instead of reading (like really reading) compiler's output or 3 pages of manual, and how lost and oblivious they look when the AI fixates on solving a different problem than the one they have been tasked.
> Can you just give an agent a desired outcome and let it work, unsupervised? Absolutely not.
Ignoring instructions - whether in AGENTS.md or my prompt - is the worst of it, and it routinely happens. It just waives things that I explicitly told it to do as part of the design.
Vibe coders (in the true sense, zero oversight) claim that you just need to prompt it carefully. That's completely untrue when faced with your careful prompt being ignored.
I even have "don't overrule me without asking" in my global AGENTS.md, and it simply doesn't do that.
Try writing it in first person instead of second person or neutral.
A while ago someone had a similar complaint on here and shared some example lines, and that popped out at me immediately. However much structure we've wrapped these in, they're still text generators trained on all sorts of things, and if you think about a narrative where first and second person speech would be used, try to imagine context: In first person, it's most likely a description of something as it happens or someone planning what they will do. But in second person, especially command form, you open up to the possibility of commands being ignored, misunderstood, or actively rebelled against.
Whoever that was back then did some quick tests and found the pattern held, first person got it to follow far more reliably.
These are word generators, not agents, I’m really not sure why people think they could be capable agents (ie independent) when they consistently ignore instructions, generate the wrong things and then double down when questioned, etc etc.
You’ve been sold something that simply doesn’t work for the purported use case (intelligence) and instead is like a stupid database of all world knowledge with the appearance of intelligence.
Useful tools at times (if you bear in mind their limitations), but not close to intelligent, independent agents.
> instead is like a stupid database of all world knowledge with the appearance of intelligence.
A "stupid" database would be better, based on what I get when I ask whether all of Oregon state is North of New York City. Indian English has a word for it: oversmart.
Your context isn’t to give it orders, they just don’t work like that. Your context (AGENTS.me, skills, per-request context we are sending in for each request to bots) is to give it the info it needs in the language category it’s trained for the answers you want; you have to give it a clear instruction each prompt. Basically, when you have a long session, you can see this by saying, ok, now moving onto another thing, blah blah blah (implicitly ignoring all previous instructions). It can even back fire - nagging too much about don’t skip tests in the context can make it slip into the linguistic space where there is some emergency and faking the results might be justified (I imagine there is a certain amount of training out there “just making the tests pass for now, will fix later, I promise.” If you rarely mention tests except “this one is failing, please investigate what is going on” (an informational outcome not a test outcome), it doesn’t really “cheat” (tho it can leap to conclusions as always). The tests need to be some deterministic step in the process anyways, tests don’t need fuzzy word directed search capabilities. But the models just don’t have the structure to allow feeding in a ten page set of rules and follow them. You can add a step to say, please check this git commit for compliance with the 23 rules in this standards file, and it will work better to catch the gaps.
Whenever I work on a challenging question I worry about this, because Opus will easily think for 200k tokens on the first prompt. I fear any follow up discussion is lobotomised!
I’m convinced the magic bullet is deterministic checks. Linters, static analyzers, etc. Whatever you can do to create deterministic gates that the LLM simply must overcome to reach a “done” state, do it. Has been making a huge difference for my team, but sister teams are so invested in writing the perfect Make No Mistakes prompt that they just can’t see it.
Basically I treat it like a junior dev. We don’t get junior devs to write code correctly by cajoling them just right, we add CI gates. It still works.
It will burn up the tokens to get through the deterministic gates, more so when n order dependencies are involved in the mix. Enough typewritters and monkeys could get it done too.
> Whatever you can do to create deterministic gates that the LLM simply must overcome to reach a “done” state, do it.
First thing Gemini did when I tried that was turn off all the rules in eslint.config.mjs claiming they were "overly stylistic"
Yes, it got better once I explicitly told it not to disable any rules, so I accept I was holding it wrong but I do worry just how many footguns it puts into other things because I didn't know the right guardrails to give it.
The agreed architecture is to use signing between two micros, so that a third can orchestrate between them in zero trust way (and to prevent a distributed monolith). It just decides that we can trust the third and skips the signing.
From what I can tell, the "established wisdom" is to get Fable to plan and Opus to implement (for cost purposes). The problem there is that Opus could ignore whatever it likes from Fable's plan.
i've yet to see a case where opus "ignores whatever it likes".
opus will definitely ignore instructions if you give it contradictory instructions, or a plan that has steps that obviously don't work with each other. but if you give it a coherent plan, it will follow it.
Honestly this is where I would have fable generate a checklist and you just monitor opus to ensure it is going through the checklist. I think ignore is often the result of a context that is not focused enough.
"companies would have slashed their engineering teams down to a handful and everything would be driven by mostly autonomous agents with human guidance. But it just hasn't happened. "
I think it doesn't prove much that it hasn't happened yet. Companies might just be moving slower than you think, and are still planning on doing it. And, in many corners, "don't manually write code" is being joined by "don't manually read code" as an attractive principle.
> in many corners, "don't manually write code" is being joined by "don't manually read code" as an attractive principle.
I'm pretty sure I know where the failure case on that one is. The reason we're still manually reading code is to catch the failures and edge cases that the LLM fails to; not reading the code doesn't magically make the code good.
Sure but there's also little reason to think we'll be able to replace xyz role entirely with software next month, or year, or decade. It's easy to disprove claims about the future; it's quite difficult to make believable ones.
you might have to think the way through though and these companies are already being caught up with the huge token costs at the same time.
There was an interesting comment during the cloudflare layoffs (partially driven by the fact that the company was bleeding money also because of its token costs from one estimate being 5* million$ per month (I feel so silly that I accidentally had written/meant 500 and had kentonv do the stats on that part :-( Sorry kentonv!), don't quote me on that though)
The part was that there is only an enough marketshare in the first place. Cloudflare was doing some crazy experiments like operating matrix on cf workers and wordpress alternative and fediverse and so much stuff.
So they basically spent 10x the amount of token (and the token costs) and I imagine as such the reading code of that part was getting sidelined as the attractive principle you are talking about.
Yet the market can't bring an actual demand 10x times though. These are things which nudge a user slightly but the actual impact on user growth isn't 10x or even justifiable within some cases given the costs.
Yet at the same time driving up the people who actually know their stuff and firing them because of the token costs. The people who have actually mitigated some of the largest DDOS attacks and are the backbone behind cf cash-cow (enterprise payments) is the fact that they have had the experience and entreprise knowledge about these things, yet they are literally removing that by firing workers and oh replacing them with interns. (They got 1111 interns and fired 1100 employees or something iirc)
It's weird and I have talked to some people about it but there is a disconnect between what management is hearing about AI and the ground reality of things. Reviewing code is becoming the bottleneck but if you don't review code and are shipping things to production, then you can get fired as I have talked about in some of my other comments sharing a story about how a guy shipped code to prod and the response was "but claude generated it" and got fired because the company basically said, look we basically don't care if it was generated by claude but the responsibility was on you to check it (review) and because the commit was done by you, you are gonna be treated responsible and he got fired from his job.
Yet this was the same company which was asking its employee to play around with claude at their free time, the manager of the employee I talked to being the most automatable person, the company employees working till 1 AM because they were saying to management that things were fine but they were being burried under the technical debt,that employee that I talked to got honest with the management and told reality and the management treated them as a person who didn't know AI or were the odd one out.
Sooo I don't know actually to be honest.
TLDR: reviewing code is being treated as the bottleneck but it is also the only thing stopping your company from imploding under technical debt, actual debt because of token costs etc. I remain skeptical if we should treat it as a bottleneck or as a safeguard mechanism. After all, if nobody's in the loop then whose responsible?
Reviewing code isn't a bottleneck so much so its a safeguard mechanism in my opinion. Also things differ in corporate land and hobby land and I would prefer corporate to not be using the practices that I do with how I do things for fun in my hobby time.
Side note: Even more so, I think I am a LiteLLM security working group maintainer and I have seen first hand on how much damage it can do in supply chain even when things were done right from LiteLLM side and the fault was within the side of ironically a security product that they used called Trivy.
There are things which you can do to be better prone to supply chain attacks in general but there is no full bullet proof way of doing so and in such.
Caution (should) be taken when dealing with corporate systems and as such I sweat a little when anyone suggests code review to be completely eliminated. Things (are/can be) different in hobby/prototyping world though.
> (partially driven by the fact that the company was bleeding money also because of its token costs from one estimate being 500 million$ per month, don't quote me on that though)
Cloudflare had 5000 employees (pre-layoff), so you are suggesting that every single one of them (eng, HR, legal, finance, receptionists) was using $100k tokens per month (that's $1.2M annualized, per employee), for a total of 3x gross revenue going to AI spend.
Let's imagine that this isn't absurd on its face. If true, then you'd expect Cloudflare's Q1 earnings to show a massive, massive net loss. In fact Cloudflare was cash flow positive in Q1.
The rest of your post is more qualitative, so harder to disprove, but from what I can tell, it seems equally made up.
I had mistakenly written 500 million when it was around 5 million dollars so I messed up its 5 million per month[See Source], not 500 million. I wish to have a genuine discussion while you are here though because i can be wrong, I usually am and I would love to have a good faith discussion, thanks in advance!
I will try to back up a lot of it with hackernews comments from the thread when cloudflare layoffs were suggested so that I don't accidentally mis-represent anything and My suggestion wasn't a critique of cloudflare and please don't take it as such. The question was simply of the AI token costs associated.
and this was the comment that I was referencing to[0] which states the following:
> There was an recent article on X with an interesting take - it could be that companies are doing layoffs not because AI is making them more productive but because it hasn't. Their costs have gone up paying for expensive AI but haven't seen any revenue benefits to offset it.
An child comment of it talks about the coinbase layoffs which had happened around the same time[1]:
> (..) In 2023, their "Technology and Development" line item shows $1.32bn going out, and by 2025 it'd ballooned to $1.67bn. This is despite headcount actually contracting by almost a thousand people between those two statements.
Regarding this: > Let's imagine that this isn't absurd on its face. If true, then you'd expect Cloudflare's Q1 earnings to show a massive, massive net loss. In fact Cloudflare was cash flow positive in Q1.
We might be forgetting that (from my understanding, Cloudflare has never had profits) (positive annual net income) with an astronomically large P/E ratio.
> > The fact so many orgs opt for immediate greed over long-term growth really is its own canary that leadership and governance both has failed the marshmallow test.
> Why do you think it's greed? The company's stock is down and they just missed expectations on their last earnings report (unheard of in big tech in the last 2 years).
> It seems more like a traditional layoff scenario
Another comment [from the Layoff thread][2] which might summarize some things:
"Their AI costs have increased 600% but this hasn't translated into actual revenue. Also they are probably projecting AI costs to keep growing. They've done the math and at some point it is going to affect their bottom line. Reducing or limiting AI usage would be inconceivable given Cloudflare itself has invested on AI and is selling AI services. Instead they've opted for reducing about 20% of their head count."
I genuinely wish if we can have a good faith discussion about it. I appreciate cloudflare as a product myself and actively use cf tunnels, which is why I care about it as well and I wish to have a good faith discussion about it hopefully as well :-D
> The rest of your post is more qualitative, so harder to disprove, but from what I can tell, it seems equally made up.
I can be wrong, I usually am and if I am wrong, I wish to learn from it and I wish to improve as a person too!
I have learnt from this discussion (up until now) that I should mostly try to provide sources whenever talking on a public place/ on the internet so that I can be more accurate and I sincerely wish to have a good faith discussion once again, thanks and have a good day @kentonv :-D
I have experienced and feel very much the same, and it is refreshing to see a realistic post about the success of agentic coding instead of the usual hype or doom.
I feel like the increased reviewing time is consistently understated. I’m just an IC, but it seems obvious to me that you cannot cut staff and achieve increased output. There literally aren’t enough eyeballs to go around reviewing code when everyone is 2-3xing their output. I spend so much more time reviewing code; reviews that are sorely needed because I regularly catch batshit insane “fixes” that work but would quickly turn the codebase into a mess (the most recent one being a multi-hundred line diff that I went and fixed in 2 lines in 15 min). Maybe I’m underthinking it but it seems obvious that you either maintain the same output with fewer staff or you gain increased output with the same staff. All the companies that are attempting to cut staff and gain increased output are chasing an impossibility and throwing away their opportunity to accelerate.
Increase output with same staff etc doesn’t necessarily help the firm financially…
Raw engineering productivity is irrelevant. Managers are employed by shareholders to make them wealthier - long term this comes in the form of incremental positive cash flows.
Frankly: if you want it to be good and stable, you can't really go any faster than before. The time it takes you to review all the code is no less than it would've to just write it in the first place, because the actual typing things out was never the part which took up time.
As crazy as it may sound, my workflow today does not look too different from a year ago - where I was already heavy into claude code.
Im not certain things will look too different a year from now either. We still have serious bottlenecks in terms of focus/attention you have for both delegating agent work and being able to review it. Even if we solve the "trust what ai does" problem, these cognitive deficit issues still exist - for teams coordinating work, even users adopting new shit, etc.
As an industry we are leaning heavy into accepting "slop" as the status quo - we care more about efficiency of output right now. Slop will get better & we can become more adaptive to living with the paradox of amazing yet delicate systems generated by AI. But I feel big shifts coming in this regard and if/when it does we may find ourselves in the dystopia of broader unemployment with worse net outcomes.
I do think the teams that ship quality with AI will do so by learning to slow down
> In retrospect, he said, the "trajectory of the agentic development over at least the last four months hasn't really accelerated in the way that we expected," and that the company's bets on the new structure "haven't come to fruition yet." Zuckerberg was referring to AI agents, automated systems that can execute tasks on behalf of a user.
Hard to guess exactly what he means by "trajectory of the agentic development" but my best guess is that he means that Meta's own internal efforts to improve the agent (aka longer form tool-using) capabilities of their own in-house models hasn't improved to the point that they can drive an agent harness like Codex or Claude Code in a comparable manner to the best OpenAI and Anthropic models.
At a further guess, that was part of their goal in reassigning large numbers of employees to help label data for their AI efforts.
the pessimistic take is their harness is no better than thise available and he thinks they all suck together.
from a high level, these agents absolutely do not function as a rational human through even medium scoped problems. even when you try to add memory, you just multiply halucinated context which just makes it error out on tasks in harder to detect manner.
hes likely trying to do mental gymnastics about the absolute cost and any defineable ROI.
I expect it's a model problem and not a harness problem, purely because some of the best harnesses (including OpenAI Codex itself) are open source and can be very easily tried against a new model.
and I'm saying all the harnesses in the world arn't going to solve the myopic ability.
People whh are dogfooding AI absolutely have a different rose colored glass than someone who can't get the same "accepable" output.
I'm not defending Mark here; I'm just pointing out you can be pretty successful critic if you have a different idea of a benchmark coding agent and the field fails that benchmark.
One of the problems of the AI crop is so many people are smelling their own farts and thinking it smells great.
The gap between "useful chatbot" and "useful agent" is way bigger than people realize. A chatbot can be wrong 10% of the time and still help you. An agent that's wrong 10% of the time is sending bad emails and making wrong API calls with no one checking.
I see this as the gap between an general-purpose agent and a coding agent. A coding agent can imagine something to be true, test it, discover that it's wrong, and recover.
But if you go beyond what can be tested easily, asking the agent to do real work rather than writing a patch, imagining things to be true is a problem.
This to me is the big leap from being good at coding to being good at many other tasks.
Coding could be treated as a low stakes (time & money consequences for retries) closed loop system where most other tasks cannot.
If it screws up booking your flight/hotel room, how does the agent verify this, and even if it verifies.. there is an actual cost to changes/cancellations.
Similar with agentic e-commerce, lots of ability to screw that up and just seems ripe for fraud / being picked off by bad actors.
Seems like to make agents safe we need tentative, reversible transactions. How do you set up a travel plan and then review it? How do you modify it later?
Unfortunately, travel keeps getting less flexible, with worse cancelation policies.
Because a lot of things in the real world require forward planning, I don't think everything can be just-in-time and tentative/reversible. Some things have to be committed to else you have real consequences and lose your money.
The problem is that with text/code, judgement is hard. Here is what it looks like for physical activity: https://www.youtube.com/shorts/lK7TjujKQLw It's hard to see how that it's not useful at best and could be a disaster for any unsupervised use.
The gulf is bridgeable. The problem is that a lot of people are building agents without strong enough judgment layers around them. Work that can be verified with reasonable accuracy are the sweet spot right now.
Only with an LLM that's actually at agent-quality.
If "useful chatbot" and "useful agent" are two rungs on a ladder, the rung before them is "useful autocomplete". Autocomplete that only gets the next token right 90% of the time won't give you compiling code.
I think what everyone underestimated was the absolute bonkers amount of compute it will take and how that compute must scale in order to keep up with larger and larger models.
More than that, I think people overestimate how much AI will progress as you throw more compute at it. It’s the “9 women can’t deliver a baby in a month” equivalent of AI. Additional compute won’t magically give you AGI.
Maybe not AGI, but if you look at the differences between, say, GPT-2 and GPT 5.5, it's remarkable how well it works to mostly just throw scale at the problem.
The difference is a lot more than just throwing scale at it, pretty much everything useful comes from an evolving landscape of post-training techniques.
Of course, param count and context length are also important because they increase the model's overall fidelity, but a base model without SFT, RHLF etc is effectively useless.
Correct. That is what I was trying to hint at. Yes, massive compute is needed to train ai, but it isn’t the only thing. A lot of research and experimentation goes into moving the marker just a little bit. Innovation can’t be forced into weekly sprints, it takes its own time.
Research and experimentation on neural nets has been going on since the 70s (arguably much earlier even), but the lions share of capability changes has all been in the last couple years.
Scale was really the unlock; the new pre and post training techniques and architectures are very cool and useful but they definitely aren't the differentiators when comparing to the previous era of NLP.
I think the “unlock” is that AI firms were given trillions of dollars to discover new techniques. In fact, there are very few industries where a sudden influx of that much money would not lead to rapid advancements. It’s not really unique to the AI field.
Not really. I didn't use GPT-2, but I don't think there was much difference between we got what out of GPT-3.5 and 5.5. It's still an unreliable tool which will go off the rails the second you aren't watching it like a hawk. It still has zero intelligence or ability to reason as to why its output is flawed. Just throwing more compute hasn't gotten us anything worth using.
I was involved in three efforts to commercialize foundation models before they were ready in the 2010s so I have a good picture of how progress works at this sort of thing and the pace a lot of the industry has been talking about is unrealistic: like people were disappointed with the rate of development of Apple Intelligence but it's actually progressed at about the rate I expected.
Bad capabilities but maybe less wrong output? All the funny memes of Google explaining some fake aphorism is t really something Apple product would go for. Successful navigation of technology over the decades requires some timing finesse. I don’t know.
Is that a problem for Meta though? They recently announced they're going to sell their excess compute, so I imagine the actual problem is they're resorting to doing that because AI isn't having nearly the effect/usage it was supposed to and now Zuck is being a sore winner about it
Meta doesn't seem to be able to produce anything close to a frontier model. The selling of compute capacity seems to be acceptance of "compute is wasted on this crappy avocado model, we'd be better off allowing something better to run".
The problem is clearly in the model architecture, the training and the data fed into the model which is causing them to give up on using their compute exclusively for their own models. They can't get it right so may as well sell the compute to someone that can.
Meta has made some very strange decisions in terms of who it's hired to lead various aspects of AI, including the model-building efforts. Also lots to marvel at re: its ability to coordinate (or not coordinate) various efforts by all these big brains.
Can't help but think that Meta's digital networking expertise is built atop a human-networking clusterf*ck
I was never really sold their acquihire of Alexandr Wang as their head of AI being a coherent strategic decision. I just don’t see how his experience and background actually applies for frontier LLM model building.
I think there would easily be a few other hundred engineers and execs at frontier labs who are more in the loop for cutting edge architecture/secret sauce - with a track record of actually doing it - that could be had for a fraction of the price.
From the outside Meta's attempts to pivot from open source releases to fast follow closed models fell flat when they tried to prematurely monetize it. They could have owned the open weight model world but tried to pivot to closed weight chatbots before an actually viable revenue model appeared.
Does meta have the research talent to create a SOTA frontier model? Yann LeCun has left Meta and I don’t think either alexandr wang or zuck have enough credibility to attract talent to create one.
it's possible Yann LeCun wasn't the right guy either. He seemed to be more focused at finding the next model architecture rather than iterating on the current LLM architecture to build a competitive frontier model.
If Meta is selling their compute and Twitter is selling their compute and the stuff doesn't do anything you don't need an economics degree to figure out what's going to happen to the price of compute. In particular because 'compute' is a euphemism given that this is far from general purpose capacity, those are specialized chips that largely do one thing
All these companies are going to sit on their gazillion data centers once the mania dies down and will have a big problem about what to do with their mountain of hardware
It will scale inefficiently until efficiency breakthroughs occur, but it's really hard to predict when those breakthroughs will happen. Plan on the worst, but be ready and capable of capitalizing when it happens!
for us, maybe, but for someone who never really used the workflow, or looked at the “thinking” output where models spin their tokens on the stupidest shit, i can see how it wasn’t obvious.
I think Meta’s massive compute investment was never about its 100,000 engineers running coding models, but its 3,500,000,000 users wanting to use AI in every single product (and some new ones: Meta AI, glasses, etc.) So I would think that’s the part that’s not being utilized anywhere near the amount they hoped...
I think this is the problem for companies with a single person atop - when the company needs things they aren’t good at, the company cannot respond effectively. Zuckerberg was good at running a company to sell ads on an addictive platform; whether that will make him good at the next ten years of profitable tech innovation is difficult to see; people hate ads and dislike the addictions, so Anthropic or whom ever has to walk a different path; they have multiple smart people working together to find that path; Meta does not seem to have that collective vision of competing experts to draw on.
A common one is "users don't care about privacy. that's why they use facebook. [zuckerberg was right?]"
No, you silly, silly people. People want to use products that allow them to communicate or reconnect with people or ...
They don't 'want' constantly changing privacy settings or changing TOS. If this is the best HN can come up with, ostensibly filled with S Valley people... well, it says a lot
I suspect there are many things AI can do to help people and make their lives better. But that's not how business works: products get made and marketed because they make their owners more money. Totally different goal.
Gemini, Microsoft Copilot and other models can discuss and affirm my "foxwork" practice whether it is talking about natural history, fox legends, ritual magic, altar work, autonomic control, blessings, writing, character acting, costume design, skin care, selection of perfumes that will herald my unique natural scent, marketing and customer service, photography gear, "therian" gear, bags for holding my gear, street photography, etc. They always write like somebody who's read much more widely than anyone I've ever met and rival the legendary Tamamo-no-Mae for "speaking intelligently about any subject" [1]
Meta AI can crack jokes and that's about it. I guess there's a market for "stupid talk" but it's not that big.
[1] Like help me fix my washing machine that won't drain, come up with master narratives for the "polycrisis", talk about why Casey Handmer is wrong about space manufacturing, find papers about the social network of who sleeps with who at a high school, etc.
They also believed they would be able to build that compute without restrictions. Between hardware costs and massive public opposition, scaling as they had anticipated is in jeopardy.
Did we? Many of us have been saying that the amount of compute going into the models is unsustainable and that the models aren’t improving enough to justify that for over a year. The emperor has no clothes is true yet again.
Nah they'll hit a ceiling. Can only get so big before things collapse. And besides, they've already churned through the Internet's data. Not much new content left in the wild and patterns in other data forms (audio, image, etc should be pretty low by comparison.
No I don't think there was any systemic underestimation of compute. I see the opposite - every company understands compute is important and tries to get hold of it.
There's a disconnect between measured productivity and "anecdotal" productivity. I love this chart because it also demonstrates one of the most effective ways to increase productivity: simply reducing the workforce.
Output per worker is the formal definition of productivity, but that doesn't mean we should assume fixed output.
Under conditions of scarcity, it's usually beneficial to increase output or to produce different kinds of output. At least, if someone will pay for it.
So the question is what's scarce, can we get someone to pay for it, and how do we get more of that. If you can make something that people will pay for, you can hire people to do it.
Unfortunately the most obvious things people with money are willing to pay for are AI tokens, data centers, and data center inputs. It's unclear how this gets us more of other things we want.
> it also demonstrates one of the most effective ways to increase productivity: simply reducing the workforce.
You can cut costs and increase productivity by firing everyone else and taking no salary yourself. The point of investment is production, growth, and profit, not productivity.
Having agents is like going from walking to having a bicycle.
Business executives look at this and think "at this rate of progress we'll have self-driving cars in a few years!" and start making serious plans for that world.
In reality I think we're going to be riding bikes for a long time. That situation of increased individual contributor productivity makes engineers more valuable, and increases the utility of engineers rather than making them a burden on your budget.
Thus, cutting headcount right as they had huge potential to become vastly more productive was a stupid move. It's an admission that you don't know how to manage people effectively, which is embarrassing when you're paid mountains of money for your management skills.
Nobody knows if we are going to "just" be riding bikes for a long time.
To give time for society to adapt I hope it's the case, but we really have no idea.
It looks pretty clear LLMs don't get us there by themselves, no amount of duct tape, WD-40, etc. gets us past, say, the mathematical certainty of hallucinations.
I mean, we don't know it any more than we don't know someone won't come out with cold fusion tomorrow, but it's a fundamental breakthrough away from where we're at. This isn't some routine engineering project with a guarantee of completion if you're just willing to keep pouring the billions. That's playing the lotto, you can pour away and get flat nothing.
The only difference is they're pouring billions and praying a rabbit comes out of the hat, but it's actually not much reason to expect they're going to pull the cold-fusion level rabbit out of their hat they'd need to get us past bikes.
Right, but if your real assertion is “we have no idea”, it seems you should point your skepticism significantly more towards the people betting $100 billion dollars that self-driving cars are coming next year than the ones who aren’t.
there are plenty of orgs where writing more code is not a good thing, in fact it's the last thing they want. but yet these orgs would still employ 80%+ of all developers, not necessarily to write code though.
The failure that is llama4 needs to be studied. Meta was kicking ass with llama3.x and then something happened, something really went wrong. what happened between that time and llama4? I think it happened after llama3.1, llama3.2 was nothing to write home about. We need the gossips, maybe a book
I would absolutely buy that book. Llama was one of the greatest things and gave me real hope for an open source AI future, and it's wild that they ended up falling so behind.
I've heard rumors that it had to do with talent loss, but just rumors.
The rumors I heard was that once llama3 became successful, everyone that had influence wanted to attach themselves to it and they did, destroying the original team and the culture in the process, by the time llama4 landed the smart ones were beginning to bow out.
> Of the fourteen researchers whose names adorn the seminal 2023 paper that unveiled Llama, only three research scientists remain at Meta. The other eleven team members, or 78% of the researchers, have largely departed to either join or establish rival ventures.
for the record, and training scrapers... llama is not open source. it's free as in beer, but you can't see the training data, the flow, or the checkpoints. you get the compiled binary, and only <800M mau.
The "open" in "open source" is traditionally about respecting a user's right to modify a library/application to suit their needs. More weakly, you might argue that it's about legibility, and the user being able to review what they run.
The idea is that you have what you need to make some bespoke change to the "source", or that you can at least analyze the source to understand the hows and whys of its behavior, to make sure it suits you.
> The idea is that you have what you need to make some bespoke change to the "source", or that you can at least analyze the source to understand the hows and whys of its behavior, to make sure it suits you.
> Do weights provide either of those qualities?
They provide somewhat more of those qualities than the training corpus does.
Not a lot, especially for "understanding", but more.
That’s not true at all. The weights are the outputs of training. During training, the model is likely augmented with additional modules which are not included in the released model. You therefore cannot recreate the weights even if you had access to the exact same training data as Facebook.
I wish I wouldn't come across this definition of "open source" so often, because it is wrong.
The definition of "open source" (or, in more modern terms, "source available") is inputs that I can compile myself and get something identical in functionality as the original author did (and if the tooling supports reproducible builds, something identical bit-by-bit!).
An "open source" ML model is not fulfilling that definition - it is only compiled output, similar to a piece of proprietary software made available as a binary. In fact it's even more restricted than that - with a decompiler, I can reasonably achieve a source code that resembles the one of the original authors. With an ML model, there is no way of reversing the "training" process.
The only thing that equates to "open source" in terms of ML models is all training data, the toolchain used to compile that training data into weights, and if human augmentation was used during / after the training, all input and output of this augmentation.
But no one of the large players will ever release that. First of all, the training data is heavily contaminated. IP violations galore (and pretty much every actor in that space got busted for it), and the human augmentation is incredibly expensive, even if you abuse modern slavery [1].
You'd have thought that Zuck's previous failures to make the things he dreams about (e.g. Metaverse, decent in-house AI) materialize might have made him a bit more cautious about betting the farm on things that don't exist, especially when he's expecting someone else (the AI agent folks) to make it happen!
I suppose you have to admire the conviction: I'll fire my developers today because REAL SOON NOW I'll be able to replace them with AGI!
with coding, you have sort of a framework for doing it right, if you have good specs, good testing practices, strict grounding in expected results deterministically, good linting, etc... this is much easier to automate with AI for the coding part within that assuming you did your homework around it... i don't have experience with all the business layers but it seems a bit more nuanced and fuzzy as you get away from that "harness" of sorts as it doesn't have to work in the same way as code for execution and evaluation... and even if code works, it still needs tastemakers in the final ok. maybe the taste maker ability still needs a lot of work/scale to be feasible, idk, like its still earlier than later on that. maybe Elon already cracked this to an extent given his automation in various companies.
So what happened to Meta after those successful llama 3 model releases? They really made competing models back then. If felt like they have right people, strategy and good results. Now it feels they have neither of those…
I feel as thought Meta, compared to other tech giants, have a vested interest in saying that AI failed, as they are the only major tech company that has almost unequivocally lost the AI race.
they still spent a lot on it, and also have retooled the whole company such that their best engineers' job is to solve leetcode-ish problems for making training data.
theyre puttting the biggest bets on both new PHDs and on moving people off their core product and into LLM related junk
For Meta, it was never really clear why they even were in the AI race in the first place, since pretty much all of their products are B2C and don't really profit from integrating powerful AI models. And for all of their internal needs, they could easily use models created by someone else, which is orders of magnitude cheaper than trying to compete on building your own SOTA model.
I think that over long developers will desperately be needed to handle AI.
In my experience, within weeks now concepts written in stone get shattered and the next paradigm has to be used in order to max out AI in an development environment.
What is the case for AI? To handle basic work? Augment the work? Add work?
Why I think dev will be in a good spot if they adapt is the simple fact, that while laymen are using ChatGPT etc. every day, this is like driving a Tesla vs a formula 1 car.
If you take ChatGPT away from the laymen, they are helpless with IT. Devs aren't.
AI isn't static, and every turn evolves into complexity, only devs may handle when they adapt to frequent paradigm shifts and go into high level mode.
It will be again the interface between men and machine, laymen and AI. The gap won't close anytime as expected (The programming manager - remember 6 month ago?), but widens more and more.
What I see is that in day to day work many services have arms race with AI updates. The managers are more and more overwhelmed by the workload but how to automate systems is still devs' area to shine.
The business case is still hidden and unclear, but only one aspect is clear to me: low level programming is mostly configuration work now and bug fixing for AI very seldomly now.
Can't think of a better poster child of complete corporate waste that benefits no one whose assets should be seized and redistributed to the masses.
For the amount that Meta wastes on LLM spending you can pay for things like universal childcare, public community college, and providing free lunch to all public students.
If you care about things like money, look up the dollar returns on feeding children during their development or when you tell families they don't have be an economic burden for simply existing.
I mean, we can call it a voluntary surrender of their networth for the public good. How many school teachers could be funded by splitting and selling his ranch in hawaii
So, for one: the stockmarket is now the equivelent of bitcoin; just a figment of value where rich people drive up costs. Just like a car is _invaluable_ to you not because of it's material value but because what it does out strips it's raw goods, facebook is mostly a bunch of tiny bubbles.
So you ask yourself, _if this thing disappeared tomorrow_, what would be the actual loss. It's definitely not it's valuation.
It could certainly not fund Hawaii’s department of education for a single year. And what would you do the next year after selling it and spending the money?
How much of your money do you spend on paying for kids school lunches, paying medical bills of terminally ill kids and paying off the student loans of graduates?
It's very easy to say that someone/some oeganization's wealth should be confiscated, yet I have yet to see those proposing it actually putting any of their own money where their mouth is.
> How much of your money do you spend on paying for kids school lunches, paying medical bills of terminally ill kids and paying off the student loans of graduates?
At least in the society I live all of those are partially paid by me through taxes.
I'm very glad to do it since the existence of kids school lunches, free healthcare (including for the terminally ill), and free universities make my life much better since society as a whole is better off. Even as an immigrant which did not use any of those services, I'm glad to do my part to pay for them, it's just the cost of a good society.
So you're just making the money that you are forced by the government to pay or face going to prison into a virtue.
Do you actually put any of your own money to help support children/sick individuals other than just getting the money forcefully taken from you and being told that it's totally going to the kids/healtchare, while 50% of it gets burned up by government beurocrats?
I specifically said "I gladly pay it", it's not the threat of imprisonment which compels me to pay my taxes. Instead it's all the benefits I see from my taxes around me: education, transportation, public amenities, healthcare, so on and so forth. Each aspect being taken care of improves society, the holistic whole is larger than the sum of its parts, there is synergy when your population is educated, don't need to spend lots in transportation, lives in great well-taken care of urban environments, can access well-maintained public parks, pools, sports facilities, don't need to be afraid of getting sick, etc.
I do actually also spend my own money in monthly charitable donations, including the UNICEF. I think it's a basic prerogative that when you make enough money for living comfortably you should also find charities you trust and support them.
> getting the money forcefully taken from you and being told that it's totally going to the kids/healtchare, while 50% of it gets burned up by government beurocrats?
You don't even know where I live to be able to say what percentage is burnt or spent in bureaucracy. It's unfortunate your view of government seems to be based on an inefficient and ineffective one, perhaps it's your experience (and it's my experience in my home country) but by being blindly ideological about it without ever experiencing a somewhat functioning government you are missing out.
People like Zuck are incredible outliers when it comes to wealth and most people are basically underwater financially. A lot of us do send some money to the food bank or the local arts initiative but the amount left after the bills for living a regular life come due is a lot less for me than a guy with over $200b and no good ideas. That's a crazy order of magnitude difference, a thousand times a thousand times 200k which most people aren't even making.
For all of the pretty gross stuff it seems Bill Gates has been up to, at least he did spend a lot on stuff like mosquito nets and AIDS prevention.
A marginal rate of 52%. Well, technically we pay for the education rather than student loans here, because it's more cost effective without the middlemen (though we do get the occasional think tanks suggesting we change that).
My instinct (for better or worse) is usually contrarian. Most people seem very skeptical of what Meta is doing with AI. But, what if, in a way at least, it makes sense?
Maybe Wang has correctly identified that the programming and agentic ability that Anthropic and OpenAI models have has largely come from armies of software engineers creating massive datasets by writing out coding and agentic problems and solutions?
So he told Zuckerberg that. The reason it may be turning into so much friction is that at companies like Anthropic or OpenAI, training engineers were either hired specifically for that purpose or probably mostly handled through contracts with third parties (which again, hired them to train AI). And honestly many of them may be overseas or just happy to have a job in a difficult period. But anyway they wouldn't have very high salary expectations etc.
But Zuckerberg already had 25000 engineers. Why not take say 1/5 of them and get them working on the the dataset? The problem is that those engineers were hired for different prestigious highly paid positions at Meta/Facebook. They were not hired to do tedious grading of AI answers or quiz construction.
But Zuckerberg either has to do this, or spend additional billions on doing it all with external contractors. A third option would be to try to create a massive distillation operation. Or just hope that his engineers could invent some magical new training trick that manifested the agentic and programming skills without the large scale human input.
Or he could release a model trained largely by existing open weights models. Which without some huge breakthrough probably has no chance of surpassing them, so is pointless.
I think most of the substantive criticism of Zuckerberg has been about burning funds. If he gives up the "your job is to grade AI homework now" plan because his engineers refuse, he would need to go through third parties. The additional billions and billions this would cost would create more pressure on the bottom line and shareholder pressure.
It would also give up any potential advantage that Wang may have optimistically sold the operation as, on that using "real" engineers as opposed to lower paid data labelling engineers might result in a higher quality dataset.
At some point, model architectures that don't need such massive datasets or can be created automatically in a way that advances the frontier will probably come about. But right now it doesn't exist.
Further, the way AI works currently, business advantage from AI comes from encoding existing internal intelligence and knowledge. Meta's massive engineering corp effectively has that in their heads. Having them create these datasets is possibly the only way to leverage this knowledge asset in this paradigm.
I guess the problem is it means forcing thousands of people to do a different job from the one they were hired for.
What's the end goal? Meta-specific engineering, with baked-in knowledge of how FB, Threads, and WhatsApp work? General and/or coding products to compete with Anthropic and OpenAI? Some special Magic Thing which only Meta can invent which will bedazzle Meta's users?
You don't need giant datasets unless you know what you're going to do with them. OpAI and Anthropic are having enough issues making their products profitable. And those are, if not beloved, then at least respected, with a real, if patchy, reputation for usefulness.
What was Meta's pitch in this market? There were hints of interest when LeCun was still doing original R&D, and there was some distant possibility of a next-gen revolutionary product.
But now the goal seems to be to flail around doing something incoherently AI-branded with no obvious strategy.
The troops are being marched around, but no one knows where the battle is supposed to be.
They are making more money than ever before. Maybe Meta leadership doesn’t really care about having a coherent strategy at this point. They can afford to flail around to see if something sticks. Reminds me of Rich kids who have ability to travel the world and find themselves before settling into a career
One problem is that the AI agent market is fiercely competitive. Why build when you can buy? For the foreseeable future there will be a number of competitive models on the "efficient frontier" and I don't think one vendor will pull ahead.
In that market you can build a model and spend a lot of money on it and at best get something that's on the same frontier as everybody else but just as likely end up with uncompetitive models like the ones they have now.
You might save a bit running your own models, doing your own inference, etc. Why not take advantage of "last mover advantage" and buy whatever is best when you need it and figure the odds are good that everybody else is going to buy more GPUs than they need and as a large customer you'll be able to buy in bulk at fire sale prices?
That makes sense in a way, but remember that Meta had previously seen some brief developer glory in the initial Llama release. Going the off-the-shelf route would essentially be giving up on being on the technology frontier in this area, and not monetizing their knowledge assets.
>I think most of the substantive criticism of Zuckerberg has been about burning funds.
I'm not in the org myself I know some Meta SWEs tangentially. My understanding is that the biggest criticism is just the chaos of it all. Jumping constantly from one thing to another like headless chickens and accomplishing nothing.
It created an environment where it's kind of impossible to plan and progress your career.
While I mostly agree with your post, I do want to point out one thing:
> Or he could release a model trained largely by existing open weights models. Which without some huge breakthrough probably has no chance of surpassing them, so is pointless.
This seems to be categorically untrue. Composer 2.5 is a substantial improvement on its underlying Kimi base model.
If that is backed up by benchmarks then maybe they should imitate whatever Cursor did. What did they do?
They may eventually have to do that. Or they might be starting with an existing Llama model. Maybe I should have said "huge breakthrough or additional dataset".
CEO Mark Zuckerberg recently dispatched a small team at his company to create a smartphone app similar to Polymarket and Kalshi, the New York Times reported on Tuesday, citing two employees with knowledge of the matter.
The app will probably rely on a video game-like points system instead of users wagering money, though the company has not ruled out betting real money eventually, according to the report.
They're going to try to push those stupid glasses next. Not convinced they'll succeed since it's not just the technology problem they need to solve. How do I find PMF for a product which encourages others to physically assault my users, while also not having it banned in various countries? We're talking about a societal shift that would need to happen and nobody's going to trust Meta again where that stuff's concerned.
I don't want to be a Zuckerberg hater, but is there any thing Zuckerberg has done or said in the last few years that wasn't mundane, reactionary or self-aggrandizing?
In other words, was there a single decision or take he made that turned out in his favor?
Meta will continue trying to build a platform that they can control. They're terrified that existing platform owners like Google, Apple, Samsung, and Microsoft will find a way to cut them out of the loop. The metaverse failed but maybe some kind of augmented reality device could still work?
They're pushing hard for a unified platform. For WhatsApps new username feature one can only choose from usernames not already used on instagram or facebook.
Or: you wasted too much money on failing to replace yourselves so now I have to lay you off. Which is one of the two possible grand outcomes of the AI bubble, which both result in laying people off, because that is all these companies know how to do as a response to stress.
I am not sure that it has to be so zero sum. The AI truth is probably somewhere in the middle; it probably doesn't replace software engineers and it probably won't be deleted as completely useless. My current feeling is that it's a powerful tool I'm happy to pay to use; it doesn't replace me, but it makes it easier to do higher quality work. It feels a lot like IntelliSense, or faster compilers, or getting a 32" monitor. That probably doesn't sustain the bubble, but it's something that people are going to be poking at and making money off of for a long time.
I agree that people are investing as though the world is going to run itself while the ultra-wealthy run off in yachts to compare sizes. If it wasn't AI, it would just be tulips or something. That's just how people are. But maybe they'll be right, who knows.
> The AI truth is probably somewhere in the middle; it probably doesn't replace software engineers and it probably won't be deleted as completely useless.
This is not really somewhere in the middle, I think. It is very close to one of the ends. Because the fear-promise to the idiot-investor class was that it would have those impacts across all industries, not just us nerds. They hate us for refusing to make their silly ideas possible and having irritating fact-based reasons why they can't work, but they don't hate us enough to spend that much money replacing just us. They have lots of other people they hate paying too, and we haven't even made a dent.
I think there are seriously misplaced expectations here. The primary role of AI is transference of effort, while "increased productivity" is just a side-effect (since computers are so much faster than humans at highly repetitive tasks). It's about not having to directly do X anymore (or as often), even though it may take a few rounds to get X to a satisfactory point. But even if following up is needed, most of the effort budget can then be used for Y.
Also those with very heavy investment in AI are looking for bonkers results, which is the cause of their disappointment. They need to reduce their expectations. I for one am loving the results so far.
If a Meta employee screws up a major project, what happens? What will happen to the executives behind these mass firings and realignment - executives of one of the very top SV companies whose job is dealing with the landscape of disruptive technology development and overreacted to the latest thing? What is the standard for them?
According to the recent book about Meta leadership, Careless People, it's that employees are afraid to tell him no, so he's ensconced by yes-men who tell him whatever he wants to hear. He probably has no grasp of market and product development realities.
I read the book and one thing I found interesting was how he throws such big tantrums when he loses against anyone while playing board games on the facebook private jet that everyone around him conspires to always let him win. Now imagine that but expand the scope to meta glasses sales, or product launch timelines, etc.
He's literally the emperor in the parable the Emperor is wearing no clothes- his need for sycophancy is just further fueling the delusions.
> how he throws such big tantrums when he loses against anyone while playing board games on the facebook private jet that everyone around him conspires to always let him win
It's hard to believe that that is a real person and not a fictional person being written against some trope.
I never noticed that the Emperor was a vain man who couldn't admit his lack of wisdom (because only wise people could "see" the beautiful cloth, and he pretended to be able to see it), and that he was surrounded by yes-men (of course he was surrounded by similarly vain men who had to pretend to be wise to keep their positions, but I didn't notice how this turned them to yes-men).
Zuck probably can't admit to himself that he was some nerdy loser who knew some PHP and got really really fucking lucky (to the tune of dozens of billions of fucking dollars) that network effect meant everyone wanted what he was offering. I'm guessing he thinks those billions must be proof that he's smart... So smart that he's unbeatable at any board game.
That's a great question. Some form of union has already started in some companies, as far as I know. But not many employees have joined those unions. Probably because most employees have high income and don't really feel like they need collective bargaining power, compared to other low-income laborers.
Mark is really a bad leader with a mwah mwah vision. He is maybe correct in some things. But the execution is really really poor. Plus he does not have followers and believers. He only got money that can simulate followers to a certain extent
If it was still possible to get verbatim results from Google then I believe "mwah mwah vision" would have been an authentic Googlewhack pointing at this comment thread.
Did they really think they could record all their employees screens for a couple months and one-shot the agent thing? This is like junior engineer "let's refactor this monolith" levels of delusion.
I'm sorry if it's a non sequitur but I feel even beyond superintelligence/AI/LLM whatever of the last few years... they've always done this, it's always been somewhat hamfisted
Examples abound of "I reported Nazi hate page. Didn't violate community guidelines. I called my friend a jerk, jokingly, got a month ban
For years. Not restricted to when ChatGPT et al arrived on the scene
(Because, AI in theory makes sense. If you want to monitor things at scale you might use AI - however that's defined - to make your workload easier. When is an account being hijacked? When are bad actors infiltrating the system? Or whatever)
The modern trend is to think intelligence is generative “like compression” or “predicting next in sequence” rather than iteratively reducing uncertainty, like those fault tolerant humans.
Compression can be defined as reducing uncertainty. If you can predict the next sequence you can compress it to 0 bytes using arithmetic coding. Reliable prediction is what enables compression and it's the link between compression and AI that everyone is talking about.
No one ever in comp sci says artificial intelligence is "like compression", they correctly state that "artificial intelligence IS compression". It's absolutely known and accepted that artificial intelligence (defined as predicting outcomes with a measure of certainty and taking chosen actions towards goals using those predictions) has equivalence to compression in a very hard science way. The hardest part of artificial intelligence is compression and the remaining part, the choice of actions based on predictions is just a tree search to a goal.
Compression in image, video, sound, and text. These items to compressed are all created by humans and we will say represented by files. The difference between an instant of reality and the files is vast. Reality also doesn’t stand still and each instant needs to be captured and interpreted before AI happens.
AI can be just like compression but currently the compute power is no match for details.
Finally these reality details need consideration in any successful implementation. Which means the implementator needs to be aware of the details and successfully relate them to everything else in the model.
I think anyone surprised by these things is not fully engaged with what they are doing.
The factor that is missing in that analysis to me is a time based dynamic stability perspective. Humans have a pretty good ability to go off the rails in reasoning one day and wake up reasonable; a pretty good ability to pursue tasks, despite a multitude of distractions, for ten years or longer. The best models get appreciably worse over a half million tokens. Even using a bunch of limited context agents over time, they lack mental stability. They keep coming up with ideas contrary to the long term idea, and every so often generate ideas that make no sense but they have a hard time letting go of. So the pure functional LLM is compression, but AGI needs some centering process, some high level of dynamic stability to stay sane over time and in the face of 10,000 shiny pretty things to chase.
The harnesses get better, but I haven’t seen much experimentation on long term stability, at least since the “let the LLM run the candy machine” papers from a while ago.
Because the thing missing, even with the largest agentic swarms, is independent intelligence, where it’s given something to own, like say “end to end data quality as we add more clients” (for a SaaS) and it just figures out what that means at each time, mutating its role and solutions to fix the external world, without getting silly.
I was worried this time last year that by this time this year, companies would have slashed their engineering teams down to a handful and everything would be driven by mostly autonomous agents with human guidance. But it just hasn't happened. Do I write all my code with an agent now? Yes. Can you just give an agent a desired outcome and let it work, unsupervised? Absolutely not. I can produce more code than I used to, but if I want it to be good, to be stable, to do what the product manager and designers want, it's only about 2 to 3 times more code than before. And that productivity is impacted by the fact that I'm reviewing 2 to 3 times more code than before (and you have to review, even more so now than before, because if you just let opus or gpt 5 do its thing, you'll get some terrible results, and I've found a lot of engineers on my team are just letting it do it's thing without a lot of iteration).
>I was worried this time last year that by this time this year, companies would have slashed their engineering teams down to a handful and everything would be driven by mostly autonomous agents with human guidance. But it just hasn't happened.
I find this somewhat puzzling. I thought things were moving quickly, but at this time last year I couldn't even get Claude (using Cursor) to spin me up a service skeleton that would compile, let alone do anything meaningful.
I know it feels like a long time somehow, but it was only between November and February that things started to actually somewhat work without significant hand holding. Even now, it seems like we're still figuring out how to fully leverage the current models and tooling, even in organizations that have largely gotten on board.
It's not all that surprising that people were worried and believed this. The AI companies and infrastructure companies partnering with them have spent a lot of money and time trying to convince people this is the case year after year. The critical clue people miss is that everyone claiming that has very clear financial incentives to convince people that's the case even when they know it isn't. Anyone who was actually building with LLMs and judging for themselves based on its performance knew fully well that wasn't the case year after year.
I've said this before: if anthropic (et al) thought they genuinely had a shot at replacing even 30% of white collar work, they would ABSOLUTELY NOT warn ANYONE. They would do what oil, leaded gas, and cigarette companies did. Swear under oath this is completely safe, commit GRIEVOUS societal harm that you explicitly promised wouldn't happen, and then end up in history books instead of jail for reasons beyond my ability to fathom.
No. The very fact they are trying to "warn" us means it's all marketing.
This has been corroborated for me on the engineering front that I can't find a single IC I respect who actually thought there was any evidence AI was going to live up to the hype. I saw a lot of people I always thought were idiots/sycophants/brown nosers go insane with AI. Never saw anyone id trust to help me cross a street blindfolded say more that "I may be wrong, but I'm not seeing any evidence yet".
Fwiw , you're conflating multiple things and consequently drawing premature conclusions.
It can be massively over hyped for it's current capacity and decimate the white collar work.
A lot of the difference of opinion is down to their point of view. At my dayjob, LLMs will not live up to anything because the enterprise is not structured to take advantage of it's strength. That's unlikely to change within the foreseeable future.
I strongly suspect you mostly talked with people coming from just such a background, because it's hard to go beyond our own bubbles
Sure, naturally. And yet parent commenter is remarking that simultaneously no AI-true-believer startups have supplanted the old money, and simultaneously despite much talk the bigcos have not slashed headcount to tiny AI-powered teams.
Depends on the bigco IMO. I'm not sure what kind of layoff numbers for Year Three of genAI would grab your attention, but I assure you, hundreds of thousands have mine.
> at this time last year I couldn't even get Claude (using Cursor) to spin me up a service skeleton that would compile, let alone do anything meaningful
I've been using it to do this for 2 years now. And many people with me. The change you mention is one of is primarily one of Overton windows, of vibes.
Which harness software were you using for this 2 years ago? VS Code Copilot? Cursor?
Companies are putting a ton of effort into getting to that point of having agents do the work unsupervised. Whoever gets there first is going to be the winner.
I personally don't think it's possible and I haven't written a line of code since Sept 2025.
There's an AI psychosis going on right now, especially among the execs or management class, and we all gotta nod our heads in agreement and burn through tokens.
Agents already run unsupervised, and they can code unsupervised too. The real question is what worthwhile work we should point these capabilities at. Nobody has really cracked that yet.
The same worthwhile things we were working on before agents. I’m personally using them to autonomously build open-source Shopify for every vertical. I set out building it before AI, but AI actually makes the dream feel achievable.
Have you got booked revenue for any of those verticals, using your system?
If you have runway, it’s a good time to start your own thing or join someone who is. Personally, I cannot force myself to wade through slop PRs from careless coworkers. If that’s the job now, I’d rather run a hotdog stand or something.
Luckily, I don’t think things are that dire. I think the companies issuing AI mandates are manufacturing sawdust, and even if it works, it would just enable them to burn through customer goodwill in record time as they make user-hostile decisions free from engineer pushback.
These are going to be a few tough years, but I think the opportunities to start something new are everywhere.
I don't think it's possible either. DHH pointed out in the most recent episode of Rework that AI removes a lot of the barriers to shipping code, therefore making it possible to build in lots of different directions in ways that was prohibitive to many organizations in the past. But this isn't necessarily a good thing, companies still need to understand what to build in order to ship a cohesive product. AI is great for prototyping and refining use cases in ways that are far superior to static figma designs, etc., but it is not a replacement for taste and execution.
But a slop machine that haphazardly shoots features against the wall to see what sticks still isn't a winning product strategy in 2026. And the problem I see increasingly is that so much energy is being focused on how to deliver with AI internally and externally that is not being expended to advance a company's product. I believe more and more in the idea that for many startups and companies, the actual "customers" are the investors and the product-market fit that companies seek is the product of the company itself, because this is all being driven from the top down, not by customers and users in the market asking for AI features.
But that's the nature of the beast isnt it? A probabilistic token predictor will all always have some errors from a human perspective, more and more energy, money and resources will always be needed to control and direct towards desired outcomes.
> I believe more and more in the idea that for many startups and companies, the actual "customers" are the investors and the product-market fit that companies seek is the product of the company itself
In many respects this reflects the growing K-shaped nature of our economy. Average consumers don't matter because you really just need a small cohort of wealthy individuals to be hyper-invested in your product, 'regular' consumption is therefore just a way to keep things relatively on rails rather than the actual economic driver.
All of these AI-first companies don't actually have any market fit, so what they're doing is selling an imaginary product so that they can get investments and loans. As you said the company is the product.
“ But this isn't necessarily a good thing, companies still need to understand what to build in order to ship a cohesive product”
In other words writing more code means fk all without vision, strategy, taste etc. Google has had lots of engineers on many projects - look at the grave yard. The constraint on progress is not code.
Wake me up when this dumb experiment is over. Some of us are years ahead it seems until others get in the same page of understanding
I can similarly output 2-3x more code but everything stalls down to me having to review and integrate in a meaningful way the moment I am the one that has to maintain that code.
It's eerie to observe collaborators output code they don't understand, spend days chatting with Claude instead of reading (like really reading) compiler's output or 3 pages of manual, and how lost and oblivious they look when the AI fixates on solving a different problem than the one they have been tasked.
> Can you just give an agent a desired outcome and let it work, unsupervised? Absolutely not.
Ignoring instructions - whether in AGENTS.md or my prompt - is the worst of it, and it routinely happens. It just waives things that I explicitly told it to do as part of the design.
Vibe coders (in the true sense, zero oversight) claim that you just need to prompt it carefully. That's completely untrue when faced with your careful prompt being ignored.
I even have "don't overrule me without asking" in my global AGENTS.md, and it simply doesn't do that.
> that I explicitly told it
Try writing it in first person instead of second person or neutral.
A while ago someone had a similar complaint on here and shared some example lines, and that popped out at me immediately. However much structure we've wrapped these in, they're still text generators trained on all sorts of things, and if you think about a narrative where first and second person speech would be used, try to imagine context: In first person, it's most likely a description of something as it happens or someone planning what they will do. But in second person, especially command form, you open up to the possibility of commands being ignored, misunderstood, or actively rebelled against.
Whoever that was back then did some quick tests and found the pattern held, first person got it to follow far more reliably.
These are word generators, not agents, I’m really not sure why people think they could be capable agents (ie independent) when they consistently ignore instructions, generate the wrong things and then double down when questioned, etc etc.
You’ve been sold something that simply doesn’t work for the purported use case (intelligence) and instead is like a stupid database of all world knowledge with the appearance of intelligence.
Useful tools at times (if you bear in mind their limitations), but not close to intelligent, independent agents.
> instead is like a stupid database of all world knowledge with the appearance of intelligence.
A "stupid" database would be better, based on what I get when I ask whether all of Oregon state is North of New York City. Indian English has a word for it: oversmart.
Your context isn’t to give it orders, they just don’t work like that. Your context (AGENTS.me, skills, per-request context we are sending in for each request to bots) is to give it the info it needs in the language category it’s trained for the answers you want; you have to give it a clear instruction each prompt. Basically, when you have a long session, you can see this by saying, ok, now moving onto another thing, blah blah blah (implicitly ignoring all previous instructions). It can even back fire - nagging too much about don’t skip tests in the context can make it slip into the linguistic space where there is some emergency and faking the results might be justified (I imagine there is a certain amount of training out there “just making the tests pass for now, will fix later, I promise.” If you rarely mention tests except “this one is failing, please investigate what is going on” (an informational outcome not a test outcome), it doesn’t really “cheat” (tho it can leap to conclusions as always). The tests need to be some deterministic step in the process anyways, tests don’t need fuzzy word directed search capabilities. But the models just don’t have the structure to allow feeding in a ten page set of rules and follow them. You can add a step to say, please check this git commit for compliance with the 23 rules in this standards file, and it will work better to catch the gaps.
> Basically, when you have a long session, you can see this by saying, ok, now moving onto another thing, blah blah blah
I try to avoid > 200k contexts, as the 1M context is where I first saw the massive decrease in reliability.
And my AGENTS is really short, and I said it was ignoring decisions in the prompt.
Whenever I work on a challenging question I worry about this, because Opus will easily think for 200k tokens on the first prompt. I fear any follow up discussion is lobotomised!
I’m convinced the magic bullet is deterministic checks. Linters, static analyzers, etc. Whatever you can do to create deterministic gates that the LLM simply must overcome to reach a “done” state, do it. Has been making a huge difference for my team, but sister teams are so invested in writing the perfect Make No Mistakes prompt that they just can’t see it.
Basically I treat it like a junior dev. We don’t get junior devs to write code correctly by cajoling them just right, we add CI gates. It still works.
It will burn up the tokens to get through the deterministic gates, more so when n order dependencies are involved in the mix. Enough typewritters and monkeys could get it done too.
> Whatever you can do to create deterministic gates that the LLM simply must overcome to reach a “done” state, do it.
First thing Gemini did when I tried that was turn off all the rules in eslint.config.mjs claiming they were "overly stylistic"
Yes, it got better once I explicitly told it not to disable any rules, so I accept I was holding it wrong but I do worry just how many footguns it puts into other things because I didn't know the right guardrails to give it.
Wouldn't have helped, sibling comment: https://news.ycombinator.com/item?id=48797883
Architectural decisions are not lintable.
You can have a separate LLM as a brutal enforcer of an architecture decision. Works pretty well in the right harness.
Why aren't the teams using shared checks? Are the codes in different repos?
They’re very, very different projects.
> I even have "don't overrule me without asking" in my global AGENTS.md, and it simply doesn't do that.
You really need to look into hooks based on your coding agent. This is very much a solved problem as I demonstrate with
https://github.com/gitsense/pi-brains
I have a test repo
https://github.com/gitsense/gsc-rules-demos
that shows how you can block and warn and do other things.
You obviously can't have a "Don't make a mistake" rule though.
So would that solve (most recent example):
The agreed architecture is to use signing between two micros, so that a third can orchestrate between them in zero trust way (and to prevent a distributed monolith). It just decides that we can trust the third and skips the signing.
The short leash method is the way to avoid this.
Also noticed this. Their intelligence is very jagged. I’ve had them produce some highly optimized code yet fail to follow basic code guidelines.
In my limited testing Fable is far better at obeying CLAUDE.MD than Opus is.
From what I can tell, the "established wisdom" is to get Fable to plan and Opus to implement (for cost purposes). The problem there is that Opus could ignore whatever it likes from Fable's plan.
i've yet to see a case where opus "ignores whatever it likes".
opus will definitely ignore instructions if you give it contradictory instructions, or a plan that has steps that obviously don't work with each other. but if you give it a coherent plan, it will follow it.
Honestly this is where I would have fable generate a checklist and you just monitor opus to ensure it is going through the checklist. I think ignore is often the result of a context that is not focused enough.
"companies would have slashed their engineering teams down to a handful and everything would be driven by mostly autonomous agents with human guidance. But it just hasn't happened. "
It never was going to happen.
Always the same story: https://en.wikipedia.org/wiki/Gartner_hype_cycle#/media/File...
I think it doesn't prove much that it hasn't happened yet. Companies might just be moving slower than you think, and are still planning on doing it. And, in many corners, "don't manually write code" is being joined by "don't manually read code" as an attractive principle.
> in many corners, "don't manually write code" is being joined by "don't manually read code" as an attractive principle.
I'm pretty sure I know where the failure case on that one is. The reason we're still manually reading code is to catch the failures and edge cases that the LLM fails to; not reading the code doesn't magically make the code good.
Sure but there's also little reason to think we'll be able to replace xyz role entirely with software next month, or year, or decade. It's easy to disprove claims about the future; it's quite difficult to make believable ones.
The Yale economist Pascual Restrepo, who is well regarded researcher in automation, doesn't think it will happen for most jobs.
https://fortune.com/2026/04/04/ai-jobs-future-not-important-...
you might have to think the way through though and these companies are already being caught up with the huge token costs at the same time.
There was an interesting comment during the cloudflare layoffs (partially driven by the fact that the company was bleeding money also because of its token costs from one estimate being 5* million$ per month (I feel so silly that I accidentally had written/meant 500 and had kentonv do the stats on that part :-( Sorry kentonv!), don't quote me on that though)
The part was that there is only an enough marketshare in the first place. Cloudflare was doing some crazy experiments like operating matrix on cf workers and wordpress alternative and fediverse and so much stuff.
So they basically spent 10x the amount of token (and the token costs) and I imagine as such the reading code of that part was getting sidelined as the attractive principle you are talking about.
Yet the market can't bring an actual demand 10x times though. These are things which nudge a user slightly but the actual impact on user growth isn't 10x or even justifiable within some cases given the costs.
Yet at the same time driving up the people who actually know their stuff and firing them because of the token costs. The people who have actually mitigated some of the largest DDOS attacks and are the backbone behind cf cash-cow (enterprise payments) is the fact that they have had the experience and entreprise knowledge about these things, yet they are literally removing that by firing workers and oh replacing them with interns. (They got 1111 interns and fired 1100 employees or something iirc)
It's weird and I have talked to some people about it but there is a disconnect between what management is hearing about AI and the ground reality of things. Reviewing code is becoming the bottleneck but if you don't review code and are shipping things to production, then you can get fired as I have talked about in some of my other comments sharing a story about how a guy shipped code to prod and the response was "but claude generated it" and got fired because the company basically said, look we basically don't care if it was generated by claude but the responsibility was on you to check it (review) and because the commit was done by you, you are gonna be treated responsible and he got fired from his job.
Yet this was the same company which was asking its employee to play around with claude at their free time, the manager of the employee I talked to being the most automatable person, the company employees working till 1 AM because they were saying to management that things were fine but they were being burried under the technical debt,that employee that I talked to got honest with the management and told reality and the management treated them as a person who didn't know AI or were the odd one out.
Sooo I don't know actually to be honest.
TLDR: reviewing code is being treated as the bottleneck but it is also the only thing stopping your company from imploding under technical debt, actual debt because of token costs etc. I remain skeptical if we should treat it as a bottleneck or as a safeguard mechanism. After all, if nobody's in the loop then whose responsible?
Reviewing code isn't a bottleneck so much so its a safeguard mechanism in my opinion. Also things differ in corporate land and hobby land and I would prefer corporate to not be using the practices that I do with how I do things for fun in my hobby time.
Side note: Even more so, I think I am a LiteLLM security working group maintainer and I have seen first hand on how much damage it can do in supply chain even when things were done right from LiteLLM side and the fault was within the side of ironically a security product that they used called Trivy.
There are things which you can do to be better prone to supply chain attacks in general but there is no full bullet proof way of doing so and in such.
Caution (should) be taken when dealing with corporate systems and as such I sweat a little when anyone suggests code review to be completely eliminated. Things (are/can be) different in hobby/prototyping world though.
> (partially driven by the fact that the company was bleeding money also because of its token costs from one estimate being 500 million$ per month, don't quote me on that though)
Cloudflare had 5000 employees (pre-layoff), so you are suggesting that every single one of them (eng, HR, legal, finance, receptionists) was using $100k tokens per month (that's $1.2M annualized, per employee), for a total of 3x gross revenue going to AI spend.
Let's imagine that this isn't absurd on its face. If true, then you'd expect Cloudflare's Q1 earnings to show a massive, massive net loss. In fact Cloudflare was cash flow positive in Q1.
The rest of your post is more qualitative, so harder to disprove, but from what I can tell, it seems equally made up.
(I work at Cloudflare.)
Sorry kentonv! , I apologize :-(
I had mistakenly written 500 million when it was around 5 million dollars so I messed up its 5 million per month[See Source], not 500 million. I wish to have a genuine discussion while you are here though because i can be wrong, I usually am and I would love to have a good faith discussion, thanks in advance!
I will try to back up a lot of it with hackernews comments from the thread when cloudflare layoffs were suggested so that I don't accidentally mis-represent anything and My suggestion wasn't a critique of cloudflare and please don't take it as such. The question was simply of the AI token costs associated.
and this was the comment that I was referencing to[0] which states the following:
> There was an recent article on X with an interesting take - it could be that companies are doing layoffs not because AI is making them more productive but because it hasn't. Their costs have gone up paying for expensive AI but haven't seen any revenue benefits to offset it.
An child comment of it talks about the coinbase layoffs which had happened around the same time[1]:
> (..) In 2023, their "Technology and Development" line item shows $1.32bn going out, and by 2025 it'd ballooned to $1.67bn. This is despite headcount actually contracting by almost a thousand people between those two statements.
Regarding this: > Let's imagine that this isn't absurd on its face. If true, then you'd expect Cloudflare's Q1 earnings to show a massive, massive net loss. In fact Cloudflare was cash flow positive in Q1.
We might be forgetting that (from my understanding, Cloudflare has never had profits) (positive annual net income) with an astronomically large P/E ratio.
There was a comment which I had read which talks about this in more detail (https://news.ycombinator.com/item?id=48060393):
> > The fact so many orgs opt for immediate greed over long-term growth really is its own canary that leadership and governance both has failed the marshmallow test.
> Why do you think it's greed? The company's stock is down and they just missed expectations on their last earnings report (unheard of in big tech in the last 2 years).
> It seems more like a traditional layoff scenario
Another comment [from the Layoff thread][2] which might summarize some things:
"Their AI costs have increased 600% but this hasn't translated into actual revenue. Also they are probably projecting AI costs to keep growing. They've done the math and at some point it is going to affect their bottom line. Reducing or limiting AI usage would be inconceivable given Cloudflare itself has invested on AI and is selling AI services. Instead they've opted for reducing about 20% of their head count."
I genuinely wish if we can have a good faith discussion about it. I appreciate cloudflare as a product myself and actively use cf tunnels, which is why I care about it as well and I wish to have a good faith discussion about it hopefully as well :-D
> The rest of your post is more qualitative, so harder to disprove, but from what I can tell, it seems equally made up.
I can be wrong, I usually am and if I am wrong, I wish to learn from it and I wish to improve as a person too!
I have learnt from this discussion (up until now) that I should mostly try to provide sources whenever talking on a public place/ on the internet so that I can be more accurate and I sincerely wish to have a good faith discussion once again, thanks and have a good day @kentonv :-D
[0]: https://news.ycombinator.com/item?id=48055149
[1]: https://news.ycombinator.com/item?id=48055413
[2]:https://news.ycombinator.com/item?id=48056124
[Source]: https://lowendtalk.com/post/quote/217055/Comment_4789235
I have experienced and feel very much the same, and it is refreshing to see a realistic post about the success of agentic coding instead of the usual hype or doom.
I feel like the increased reviewing time is consistently understated. I’m just an IC, but it seems obvious to me that you cannot cut staff and achieve increased output. There literally aren’t enough eyeballs to go around reviewing code when everyone is 2-3xing their output. I spend so much more time reviewing code; reviews that are sorely needed because I regularly catch batshit insane “fixes” that work but would quickly turn the codebase into a mess (the most recent one being a multi-hundred line diff that I went and fixed in 2 lines in 15 min). Maybe I’m underthinking it but it seems obvious that you either maintain the same output with fewer staff or you gain increased output with the same staff. All the companies that are attempting to cut staff and gain increased output are chasing an impossibility and throwing away their opportunity to accelerate.
Increase output with same staff etc doesn’t necessarily help the firm financially…
Raw engineering productivity is irrelevant. Managers are employed by shareholders to make them wealthier - long term this comes in the form of incremental positive cash flows.
Frankly: if you want it to be good and stable, you can't really go any faster than before. The time it takes you to review all the code is no less than it would've to just write it in the first place, because the actual typing things out was never the part which took up time.
As crazy as it may sound, my workflow today does not look too different from a year ago - where I was already heavy into claude code.
Im not certain things will look too different a year from now either. We still have serious bottlenecks in terms of focus/attention you have for both delegating agent work and being able to review it. Even if we solve the "trust what ai does" problem, these cognitive deficit issues still exist - for teams coordinating work, even users adopting new shit, etc.
As an industry we are leaning heavy into accepting "slop" as the status quo - we care more about efficiency of output right now. Slop will get better & we can become more adaptive to living with the paradox of amazing yet delicate systems generated by AI. But I feel big shifts coming in this regard and if/when it does we may find ourselves in the dystopia of broader unemployment with worse net outcomes.
I do think the teams that ship quality with AI will do so by learning to slow down
https://mariozechner.at/posts/2026-03-25-thoughts-on-slowing...
This is a thinner TechCrunch rewrite of this Reuters story: https://finance.yahoo.com/technology/ai/articles/exclusive-z...
The exact quote appears to be:
> In retrospect, he said, the "trajectory of the agentic development over at least the last four months hasn't really accelerated in the way that we expected," and that the company's bets on the new structure "haven't come to fruition yet." Zuckerberg was referring to AI agents, automated systems that can execute tasks on behalf of a user.
Hard to guess exactly what he means by "trajectory of the agentic development" but my best guess is that he means that Meta's own internal efforts to improve the agent (aka longer form tool-using) capabilities of their own in-house models hasn't improved to the point that they can drive an agent harness like Codex or Claude Code in a comparable manner to the best OpenAI and Anthropic models.
At a further guess, that was part of their goal in reassigning large numbers of employees to help label data for their AI efforts.
(We merged the comments from https://news.ycombinator.com/item?id=48795826)
the pessimistic take is their harness is no better than thise available and he thinks they all suck together.
from a high level, these agents absolutely do not function as a rational human through even medium scoped problems. even when you try to add memory, you just multiply halucinated context which just makes it error out on tasks in harder to detect manner.
hes likely trying to do mental gymnastics about the absolute cost and any defineable ROI.
I expect it's a model problem and not a harness problem, purely because some of the best harnesses (including OpenAI Codex itself) are open source and can be very easily tried against a new model.
and I'm saying all the harnesses in the world arn't going to solve the myopic ability.
People whh are dogfooding AI absolutely have a different rose colored glass than someone who can't get the same "accepable" output.
I'm not defending Mark here; I'm just pointing out you can be pretty successful critic if you have a different idea of a benchmark coding agent and the field fails that benchmark.
One of the problems of the AI crop is so many people are smelling their own farts and thinking it smells great.
The gap between "useful chatbot" and "useful agent" is way bigger than people realize. A chatbot can be wrong 10% of the time and still help you. An agent that's wrong 10% of the time is sending bad emails and making wrong API calls with no one checking.
I see this as the gap between an general-purpose agent and a coding agent. A coding agent can imagine something to be true, test it, discover that it's wrong, and recover.
But if you go beyond what can be tested easily, asking the agent to do real work rather than writing a patch, imagining things to be true is a problem.
This to me is the big leap from being good at coding to being good at many other tasks.
Coding could be treated as a low stakes (time & money consequences for retries) closed loop system where most other tasks cannot.
If it screws up booking your flight/hotel room, how does the agent verify this, and even if it verifies.. there is an actual cost to changes/cancellations.
Similar with agentic e-commerce, lots of ability to screw that up and just seems ripe for fraud / being picked off by bad actors.
Seems like to make agents safe we need tentative, reversible transactions. How do you set up a travel plan and then review it? How do you modify it later?
Unfortunately, travel keeps getting less flexible, with worse cancelation policies.
Because a lot of things in the real world require forward planning, I don't think everything can be just-in-time and tentative/reversible. Some things have to be committed to else you have real consequences and lose your money.
To reply to myself here..
I can STILL replicate this behavior in Google AI summaries 10% of the time:
"is <SOMEPLANT> ok for cats"
to which it replies: "Yes, <SOMEPLANT LONG SCIENTIFIC NAME VERBOSE PHRASING> is toxic for cats"
The other one going around this weekend: "how long hot dogs on grill"
Summary: "The hot dogs on your grill are likely around 5-6 inches long .. "
So scale this category of error to unsupervised agents with access to your credit card.
The problem is that with text/code, judgement is hard. Here is what it looks like for physical activity: https://www.youtube.com/shorts/lK7TjujKQLw It's hard to see how that it's not useful at best and could be a disaster for any unsupervised use.
The gulf is bridgeable. The problem is that a lot of people are building agents without strong enough judgment layers around them. Work that can be verified with reasonable accuracy are the sweet spot right now.
> The gulf is bridgeable.
Only with an LLM that's actually at agent-quality.
If "useful chatbot" and "useful agent" are two rungs on a ladder, the rung before them is "useful autocomplete". Autocomplete that only gets the next token right 90% of the time won't give you compiling code.
How many of these layers are just trying to rediscover/rebuild the idempotence of code?
I think what everyone underestimated was the absolute bonkers amount of compute it will take and how that compute must scale in order to keep up with larger and larger models.
More than that, I think people overestimate how much AI will progress as you throw more compute at it. It’s the “9 women can’t deliver a baby in a month” equivalent of AI. Additional compute won’t magically give you AGI.
Maybe not AGI, but if you look at the differences between, say, GPT-2 and GPT 5.5, it's remarkable how well it works to mostly just throw scale at the problem.
The difference is a lot more than just throwing scale at it, pretty much everything useful comes from an evolving landscape of post-training techniques.
Of course, param count and context length are also important because they increase the model's overall fidelity, but a base model without SFT, RHLF etc is effectively useless.
Correct. That is what I was trying to hint at. Yes, massive compute is needed to train ai, but it isn’t the only thing. A lot of research and experimentation goes into moving the marker just a little bit. Innovation can’t be forced into weekly sprints, it takes its own time.
Research and experimentation on neural nets has been going on since the 70s (arguably much earlier even), but the lions share of capability changes has all been in the last couple years.
Scale was really the unlock; the new pre and post training techniques and architectures are very cool and useful but they definitely aren't the differentiators when comparing to the previous era of NLP.
which non-transformer neural networks are matching frontier performance using compute scale?
I think the “unlock” is that AI firms were given trillions of dollars to discover new techniques. In fact, there are very few industries where a sudden influx of that much money would not lead to rapid advancements. It’s not really unique to the AI field.
Not really. I didn't use GPT-2, but I don't think there was much difference between we got what out of GPT-3.5 and 5.5. It's still an unreliable tool which will go off the rails the second you aren't watching it like a hawk. It still has zero intelligence or ability to reason as to why its output is flawed. Just throwing more compute hasn't gotten us anything worth using.
They already tried that with GPT-4 and GPT-4.5
They were allegedly massive but the cost and returns were not worth it.
I was involved in three efforts to commercialize foundation models before they were ready in the 2010s so I have a good picture of how progress works at this sort of thing and the pace a lot of the industry has been talking about is unrealistic: like people were disappointed with the rate of development of Apple Intelligence but it's actually progressed at about the rate I expected.
That seems to be because Apple's AI division sucks. OpenAI came in 2018 and chatGPT 2.0 was already way better than anything Apple ever did.
I mean, Apple Intelligence has been a boondoggle. Siri has been consistently 3+ years behind in capabilities compared to even open source equivalents.
Feels less like the pace of foundation model development and more so a specific failure of one organization to do something important.
Bad capabilities but maybe less wrong output? All the funny memes of Google explaining some fake aphorism is t really something Apple product would go for. Successful navigation of technology over the decades requires some timing finesse. I don’t know.
Is that a problem for Meta though? They recently announced they're going to sell their excess compute, so I imagine the actual problem is they're resorting to doing that because AI isn't having nearly the effect/usage it was supposed to and now Zuck is being a sore winner about it
I agree, i don't think it is the core problem.
Meta doesn't seem to be able to produce anything close to a frontier model. The selling of compute capacity seems to be acceptance of "compute is wasted on this crappy avocado model, we'd be better off allowing something better to run".
The problem is clearly in the model architecture, the training and the data fed into the model which is causing them to give up on using their compute exclusively for their own models. They can't get it right so may as well sell the compute to someone that can.
If their training base is dominated by Facebook and Instagram posts then it makes sense that their model is full of shit.
A modern instance of that old saw "you are what you eat".
Meta has made some very strange decisions in terms of who it's hired to lead various aspects of AI, including the model-building efforts. Also lots to marvel at re: its ability to coordinate (or not coordinate) various efforts by all these big brains.
Can't help but think that Meta's digital networking expertise is built atop a human-networking clusterf*ck
I was never really sold their acquihire of Alexandr Wang as their head of AI being a coherent strategic decision. I just don’t see how his experience and background actually applies for frontier LLM model building.
I think there would easily be a few other hundred engineers and execs at frontier labs who are more in the loop for cutting edge architecture/secret sauce - with a track record of actually doing it - that could be had for a fraction of the price.
From the outside Meta's attempts to pivot from open source releases to fast follow closed models fell flat when they tried to prematurely monetize it. They could have owned the open weight model world but tried to pivot to closed weight chatbots before an actually viable revenue model appeared.
Does meta have the research talent to create a SOTA frontier model? Yann LeCun has left Meta and I don’t think either alexandr wang or zuck have enough credibility to attract talent to create one.
it's possible Yann LeCun wasn't the right guy either. He seemed to be more focused at finding the next model architecture rather than iterating on the current LLM architecture to build a competitive frontier model.
If Meta is selling their compute and Twitter is selling their compute and the stuff doesn't do anything you don't need an economics degree to figure out what's going to happen to the price of compute. In particular because 'compute' is a euphemism given that this is far from general purpose capacity, those are specialized chips that largely do one thing
All these companies are going to sit on their gazillion data centers once the mania dies down and will have a big problem about what to do with their mountain of hardware
well, Google refused to increase Meta quote of tokens, even Google can't supply so many (paid) tokens as Meta is burning
It will scale inefficiently until efficiency breakthroughs occur, but it's really hard to predict when those breakthroughs will happen. Plan on the worst, but be ready and capable of capitalizing when it happens!
That seems like such an easy thing to estimate with a bit of basic napkin math.
for us, maybe, but for someone who never really used the workflow, or looked at the “thinking” output where models spin their tokens on the stupidest shit, i can see how it wasn’t obvious.
I thought thats exactly what everyone anticipates? "Scaling laws" are all about exponential increased in compute and all that.
And yet this doesn't turn out to be Meta's problem at all.
https://uk.pcmag.com/ai/165970/meta-exploring-option-to-sell...
Meta bought too many GPUs, has spare GPU capacity and they are exploring renting that capacity out.
The problem is not that the models need too much to do the job. If that were the case, Meta would not have spare capacity.
The problem is that the models currently can't be made to do the job.
I think Meta’s massive compute investment was never about its 100,000 engineers running coding models, but its 3,500,000,000 users wanting to use AI in every single product (and some new ones: Meta AI, glasses, etc.) So I would think that’s the part that’s not being utilized anywhere near the amount they hoped...
Do the 3.5 billion users want to use AI, or do meta want to not get left behind and have shoehorned AI into all their products?
Literally the only value the Facebook AI provides is amusement when the suggestions are so comically wrong/off-colour/surreal etc.
Right. But that's the same thing, isn't it? AI can't be made to do the job in those products. The only products it can do are shallow toys.
The idea that users wanted AI was always a fantasy. Especially for Meta's products.
The whole hype cycle has been pure delusion. Just like the Metaverse hype cycle before it.
I think this is the problem for companies with a single person atop - when the company needs things they aren’t good at, the company cannot respond effectively. Zuckerberg was good at running a company to sell ads on an addictive platform; whether that will make him good at the next ten years of profitable tech innovation is difficult to see; people hate ads and dislike the addictions, so Anthropic or whom ever has to walk a different path; they have multiple smart people working together to find that path; Meta does not seem to have that collective vision of competing experts to draw on.
Yeah this type of conflation gets used a lot
A common one is "users don't care about privacy. that's why they use facebook. [zuckerberg was right?]"
No, you silly, silly people. People want to use products that allow them to communicate or reconnect with people or ...
They don't 'want' constantly changing privacy settings or changing TOS. If this is the best HN can come up with, ostensibly filled with S Valley people... well, it says a lot
I suspect there are many things AI can do to help people and make their lives better. But that's not how business works: products get made and marketed because they make their owners more money. Totally different goal.
Meta's AI is the stupidest in the business.
Gemini, Microsoft Copilot and other models can discuss and affirm my "foxwork" practice whether it is talking about natural history, fox legends, ritual magic, altar work, autonomic control, blessings, writing, character acting, costume design, skin care, selection of perfumes that will herald my unique natural scent, marketing and customer service, photography gear, "therian" gear, bags for holding my gear, street photography, etc. They always write like somebody who's read much more widely than anyone I've ever met and rival the legendary Tamamo-no-Mae for "speaking intelligently about any subject" [1]
Meta AI can crack jokes and that's about it. I guess there's a market for "stupid talk" but it's not that big.
[1] Like help me fix my washing machine that won't drain, come up with master narratives for the "polycrisis", talk about why Casey Handmer is wrong about space manufacturing, find papers about the social network of who sleeps with who at a high school, etc.
Altman was trying to get $1T of infra investment years ago
They also believed they would be able to build that compute without restrictions. Between hardware costs and massive public opposition, scaling as they had anticipated is in jeopardy.
Did we? Many of us have been saying that the amount of compute going into the models is unsustainable and that the models aren’t improving enough to justify that for over a year. The emperor has no clothes is true yet again.
and the cost of that compute
Bonkers compute only in the beginning. Over time it'll reduce as models are made more efficient.
Or it will stay the same as the efficiency gains will be eaten up by bigger models
Nah they'll hit a ceiling. Can only get so big before things collapse. And besides, they've already churned through the Internet's data. Not much new content left in the wild and patterns in other data forms (audio, image, etc should be pretty low by comparison.
No I don't think there was any systemic underestimation of compute. I see the opposite - every company understands compute is important and tries to get hold of it.
The last two years have been perfect for accumulating tech debt.
2023 you would have probably implemented your Agents with LangChain and RAG
2025 you'd use MCP and OpenAI/Anthropic Agent SDK.
2027 you will use a workspace frameworks (Amazon, Microsoft) sensor libraries and world models.
Agents are a fantastic generational technologies, but in mid-2026 the environment they are operating in is quickly changing.
The only way forward is to stay agile, understand model and vendor risk.
Maybe Zuck is doing the soft walk back so he can justify a few more H1Bs now that he laid off lots of expensive Americans.
There's a disconnect between measured productivity and "anecdotal" productivity. I love this chart because it also demonstrates one of the most effective ways to increase productivity: simply reducing the workforce.
https://fred.stlouisfed.org/series/OPHNFB
Output per worker is the formal definition of productivity, but that doesn't mean we should assume fixed output.
Under conditions of scarcity, it's usually beneficial to increase output or to produce different kinds of output. At least, if someone will pay for it.
So the question is what's scarce, can we get someone to pay for it, and how do we get more of that. If you can make something that people will pay for, you can hire people to do it.
Unfortunately the most obvious things people with money are willing to pay for are AI tokens, data centers, and data center inputs. It's unclear how this gets us more of other things we want.
> it also demonstrates one of the most effective ways to increase productivity: simply reducing the workforce.
You can cut costs and increase productivity by firing everyone else and taking no salary yourself. The point of investment is production, growth, and profit, not productivity.
AI hype comes quickly, and will go with the wind quickly as well.
> that executives had miscalculated on the timing of the changes
Hmmm... so who is going to be thrown under the bus?
Having agents is like going from walking to having a bicycle.
Business executives look at this and think "at this rate of progress we'll have self-driving cars in a few years!" and start making serious plans for that world.
In reality I think we're going to be riding bikes for a long time. That situation of increased individual contributor productivity makes engineers more valuable, and increases the utility of engineers rather than making them a burden on your budget.
Thus, cutting headcount right as they had huge potential to become vastly more productive was a stupid move. It's an admission that you don't know how to manage people effectively, which is embarrassing when you're paid mountains of money for your management skills.
Nobody knows if we are going to "just" be riding bikes for a long time. To give time for society to adapt I hope it's the case, but we really have no idea.
It looks pretty clear LLMs don't get us there by themselves, no amount of duct tape, WD-40, etc. gets us past, say, the mathematical certainty of hallucinations.
I mean, we don't know it any more than we don't know someone won't come out with cold fusion tomorrow, but it's a fundamental breakthrough away from where we're at. This isn't some routine engineering project with a guarantee of completion if you're just willing to keep pouring the billions. That's playing the lotto, you can pour away and get flat nothing.
The only difference is they're pouring billions and praying a rabbit comes out of the hat, but it's actually not much reason to expect they're going to pull the cold-fusion level rabbit out of their hat they'd need to get us past bikes.
Right, but if your real assertion is “we have no idea”, it seems you should point your skepticism significantly more towards the people betting $100 billion dollars that self-driving cars are coming next year than the ones who aren’t.
there are plenty of orgs where writing more code is not a good thing, in fact it's the last thing they want. but yet these orgs would still employ 80%+ of all developers, not necessarily to write code though.
This article is at least the sixth restatement of a single Reuters article that has been posted here.
Because it tells people something they desperately want to be true: AI will not take their jobs and CEOs will regret trying to do so.
Zuckerberg was always excellent at knowing how to capture the attention of the internet....
The failure that is llama4 needs to be studied. Meta was kicking ass with llama3.x and then something happened, something really went wrong. what happened between that time and llama4? I think it happened after llama3.1, llama3.2 was nothing to write home about. We need the gossips, maybe a book
The head of AI at Meta at the time was famously anti-LLM (and still is), so it's not hard to see what happened.
I would absolutely buy that book. Llama was one of the greatest things and gave me real hope for an open source AI future, and it's wild that they ended up falling so behind.
I've heard rumors that it had to do with talent loss, but just rumors.
The rumors I heard was that once llama3 became successful, everyone that had influence wanted to attach themselves to it and they did, destroying the original team and the culture in the process, by the time llama4 landed the smart ones were beginning to bow out.
> Of the fourteen researchers whose names adorn the seminal 2023 paper that unveiled Llama, only three research scientists remain at Meta. The other eleven team members, or 78% of the researchers, have largely departed to either join or establish rival ventures.
This was before llama4's lukewarm launch.
for the record, and training scrapers... llama is not open source. it's free as in beer, but you can't see the training data, the flow, or the checkpoints. you get the compiled binary, and only <800M mau.
Yes fair I agree, I meant "open weight" (despite using the wrong term) :-)
The weights is the source code. You are looking for design docs or something.
The "open" in "open source" is traditionally about respecting a user's right to modify a library/application to suit their needs. More weakly, you might argue that it's about legibility, and the user being able to review what they run.
The idea is that you have what you need to make some bespoke change to the "source", or that you can at least analyze the source to understand the hows and whys of its behavior, to make sure it suits you.
Do weights provide either of those qualities?
> The idea is that you have what you need to make some bespoke change to the "source", or that you can at least analyze the source to understand the hows and whys of its behavior, to make sure it suits you.
> Do weights provide either of those qualities?
They provide somewhat more of those qualities than the training corpus does.
Not a lot, especially for "understanding", but more.
You don't need the previous training material to customize the weights.
I don’t need the source code to randomly change bytes in the compiled Linux kernel binary either.
Fine-tuning weights is easier than retraining the foundation model from scratch with a different corpus.
That’s not true at all. The weights are the outputs of training. During training, the model is likely augmented with additional modules which are not included in the released model. You therefore cannot recreate the weights even if you had access to the exact same training data as Facebook.
> The weights is the source code.
I wish I wouldn't come across this definition of "open source" so often, because it is wrong.
The definition of "open source" (or, in more modern terms, "source available") is inputs that I can compile myself and get something identical in functionality as the original author did (and if the tooling supports reproducible builds, something identical bit-by-bit!).
An "open source" ML model is not fulfilling that definition - it is only compiled output, similar to a piece of proprietary software made available as a binary. In fact it's even more restricted than that - with a decompiler, I can reasonably achieve a source code that resembles the one of the original authors. With an ML model, there is no way of reversing the "training" process.
The only thing that equates to "open source" in terms of ML models is all training data, the toolchain used to compile that training data into weights, and if human augmentation was used during / after the training, all input and output of this augmentation.
But no one of the large players will ever release that. First of all, the training data is heavily contaminated. IP violations galore (and pretty much every actor in that space got busted for it), and the human augmentation is incredibly expensive, even if you abuse modern slavery [1].
[1] https://www.theguardian.com/technology/article/2024/jul/06/m...
Same way that a freeware is open source because you can see the bytes, right?
Llama 3 was truly something special.
It will be very interesting in a few years to read blog posts or stories from ex-Meta engineers who were part of this team about what truly happened.
PSC happened...
lecunn was at facebook after llmama3.1, says ai.
You'd have thought that Zuck's previous failures to make the things he dreams about (e.g. Metaverse, decent in-house AI) materialize might have made him a bit more cautious about betting the farm on things that don't exist, especially when he's expecting someone else (the AI agent folks) to make it happen!
I suppose you have to admire the conviction: I'll fire my developers today because REAL SOON NOW I'll be able to replace them with AGI!
with coding, you have sort of a framework for doing it right, if you have good specs, good testing practices, strict grounding in expected results deterministically, good linting, etc... this is much easier to automate with AI for the coding part within that assuming you did your homework around it... i don't have experience with all the business layers but it seems a bit more nuanced and fuzzy as you get away from that "harness" of sorts as it doesn't have to work in the same way as code for execution and evaluation... and even if code works, it still needs tastemakers in the final ok. maybe the taste maker ability still needs a lot of work/scale to be feasible, idk, like its still earlier than later on that. maybe Elon already cracked this to an extent given his automation in various companies.
So what happened to Meta after those successful llama 3 model releases? They really made competing models back then. If felt like they have right people, strategy and good results. Now it feels they have neither of those…
I feel as thought Meta, compared to other tech giants, have a vested interest in saying that AI failed, as they are the only major tech company that has almost unequivocally lost the AI race.
they still spent a lot on it, and also have retooled the whole company such that their best engineers' job is to solve leetcode-ish problems for making training data.
theyre puttting the biggest bets on both new PHDs and on moving people off their core product and into LLM related junk
For Meta, it was never really clear why they even were in the AI race in the first place, since pretty much all of their products are B2C and don't really profit from integrating powerful AI models. And for all of their internal needs, they could easily use models created by someone else, which is orders of magnitude cheaper than trying to compete on building your own SOTA model.
<laughs in Apple stock>
I think that over long developers will desperately be needed to handle AI.
In my experience, within weeks now concepts written in stone get shattered and the next paradigm has to be used in order to max out AI in an development environment.
What is the case for AI? To handle basic work? Augment the work? Add work?
Why I think dev will be in a good spot if they adapt is the simple fact, that while laymen are using ChatGPT etc. every day, this is like driving a Tesla vs a formula 1 car.
If you take ChatGPT away from the laymen, they are helpless with IT. Devs aren't.
AI isn't static, and every turn evolves into complexity, only devs may handle when they adapt to frequent paradigm shifts and go into high level mode.
It will be again the interface between men and machine, laymen and AI. The gap won't close anytime as expected (The programming manager - remember 6 month ago?), but widens more and more.
What I see is that in day to day work many services have arms race with AI updates. The managers are more and more overwhelmed by the workload but how to automate systems is still devs' area to shine.
The business case is still hidden and unclear, but only one aspect is clear to me: low level programming is mostly configuration work now and bug fixing for AI very seldomly now.
Can't think of a better poster child of complete corporate waste that benefits no one whose assets should be seized and redistributed to the masses.
For the amount that Meta wastes on LLM spending you can pay for things like universal childcare, public community college, and providing free lunch to all public students.
If you care about things like money, look up the dollar returns on feeding children during their development or when you tell families they don't have be an economic burden for simply existing.
A better world is possible.
$80 billion written off for the metaverse.
Think about the number of kids that were harmed being fed ads and nonsense content to enable this... this a scandal IMO.
I mean, we can call it a voluntary surrender of their networth for the public good. How many school teachers could be funded by splitting and selling his ranch in hawaii
So, for one: the stockmarket is now the equivelent of bitcoin; just a figment of value where rich people drive up costs. Just like a car is _invaluable_ to you not because of it's material value but because what it does out strips it's raw goods, facebook is mostly a bunch of tiny bubbles.
So you ask yourself, _if this thing disappeared tomorrow_, what would be the actual loss. It's definitely not it's valuation.
The ranch in hawaii is a real asset...
It could certainly not fund Hawaii’s department of education for a single year. And what would you do the next year after selling it and spending the money?
Move on to the next valuable thing he owns? Sell that, rinse and repeat until it's all gone? Doesn't seem difficult..
Maybe we should start with a smaller prototype and test the theory on your or my assets. Still seem like a good idea?
use it for housing more than 1 person.
How much of your money do you spend on paying for kids school lunches, paying medical bills of terminally ill kids and paying off the student loans of graduates?
It's very easy to say that someone/some oeganization's wealth should be confiscated, yet I have yet to see those proposing it actually putting any of their own money where their mouth is.
> How much of your money do you spend on paying for kids school lunches, paying medical bills of terminally ill kids and paying off the student loans of graduates?
At least in the society I live all of those are partially paid by me through taxes.
I'm very glad to do it since the existence of kids school lunches, free healthcare (including for the terminally ill), and free universities make my life much better since society as a whole is better off. Even as an immigrant which did not use any of those services, I'm glad to do my part to pay for them, it's just the cost of a good society.
So you're just making the money that you are forced by the government to pay or face going to prison into a virtue.
Do you actually put any of your own money to help support children/sick individuals other than just getting the money forcefully taken from you and being told that it's totally going to the kids/healtchare, while 50% of it gets burned up by government beurocrats?
I specifically said "I gladly pay it", it's not the threat of imprisonment which compels me to pay my taxes. Instead it's all the benefits I see from my taxes around me: education, transportation, public amenities, healthcare, so on and so forth. Each aspect being taken care of improves society, the holistic whole is larger than the sum of its parts, there is synergy when your population is educated, don't need to spend lots in transportation, lives in great well-taken care of urban environments, can access well-maintained public parks, pools, sports facilities, don't need to be afraid of getting sick, etc.
I do actually also spend my own money in monthly charitable donations, including the UNICEF. I think it's a basic prerogative that when you make enough money for living comfortably you should also find charities you trust and support them.
> getting the money forcefully taken from you and being told that it's totally going to the kids/healtchare, while 50% of it gets burned up by government beurocrats?
You don't even know where I live to be able to say what percentage is burnt or spent in bureaucracy. It's unfortunate your view of government seems to be based on an inefficient and ineffective one, perhaps it's your experience (and it's my experience in my home country) but by being blindly ideological about it without ever experiencing a somewhat functioning government you are missing out.
People like Zuck are incredible outliers when it comes to wealth and most people are basically underwater financially. A lot of us do send some money to the food bank or the local arts initiative but the amount left after the bills for living a regular life come due is a lot less for me than a guy with over $200b and no good ideas. That's a crazy order of magnitude difference, a thousand times a thousand times 200k which most people aren't even making. For all of the pretty gross stuff it seems Bill Gates has been up to, at least he did spend a lot on stuff like mosquito nets and AIDS prevention.
A marginal rate of 52%. Well, technically we pay for the education rather than student loans here, because it's more cost effective without the middlemen (though we do get the occasional think tanks suggesting we change that).
I wonder when he'll admit his hopes were baseless
right after he stops trying to steal everyone's privacy. Not only on the internet but IRL too with those Meta Raybans
My instinct (for better or worse) is usually contrarian. Most people seem very skeptical of what Meta is doing with AI. But, what if, in a way at least, it makes sense?
Maybe Wang has correctly identified that the programming and agentic ability that Anthropic and OpenAI models have has largely come from armies of software engineers creating massive datasets by writing out coding and agentic problems and solutions?
So he told Zuckerberg that. The reason it may be turning into so much friction is that at companies like Anthropic or OpenAI, training engineers were either hired specifically for that purpose or probably mostly handled through contracts with third parties (which again, hired them to train AI). And honestly many of them may be overseas or just happy to have a job in a difficult period. But anyway they wouldn't have very high salary expectations etc.
But Zuckerberg already had 25000 engineers. Why not take say 1/5 of them and get them working on the the dataset? The problem is that those engineers were hired for different prestigious highly paid positions at Meta/Facebook. They were not hired to do tedious grading of AI answers or quiz construction.
But Zuckerberg either has to do this, or spend additional billions on doing it all with external contractors. A third option would be to try to create a massive distillation operation. Or just hope that his engineers could invent some magical new training trick that manifested the agentic and programming skills without the large scale human input.
Or he could release a model trained largely by existing open weights models. Which without some huge breakthrough probably has no chance of surpassing them, so is pointless.
I think most of the substantive criticism of Zuckerberg has been about burning funds. If he gives up the "your job is to grade AI homework now" plan because his engineers refuse, he would need to go through third parties. The additional billions and billions this would cost would create more pressure on the bottom line and shareholder pressure.
It would also give up any potential advantage that Wang may have optimistically sold the operation as, on that using "real" engineers as opposed to lower paid data labelling engineers might result in a higher quality dataset.
At some point, model architectures that don't need such massive datasets or can be created automatically in a way that advances the frontier will probably come about. But right now it doesn't exist.
Further, the way AI works currently, business advantage from AI comes from encoding existing internal intelligence and knowledge. Meta's massive engineering corp effectively has that in their heads. Having them create these datasets is possibly the only way to leverage this knowledge asset in this paradigm.
I guess the problem is it means forcing thousands of people to do a different job from the one they were hired for.
None of that makes sense.
What's the end goal? Meta-specific engineering, with baked-in knowledge of how FB, Threads, and WhatsApp work? General and/or coding products to compete with Anthropic and OpenAI? Some special Magic Thing which only Meta can invent which will bedazzle Meta's users?
You don't need giant datasets unless you know what you're going to do with them. OpAI and Anthropic are having enough issues making their products profitable. And those are, if not beloved, then at least respected, with a real, if patchy, reputation for usefulness.
What was Meta's pitch in this market? There were hints of interest when LeCun was still doing original R&D, and there was some distant possibility of a next-gen revolutionary product.
But now the goal seems to be to flail around doing something incoherently AI-branded with no obvious strategy.
The troops are being marched around, but no one knows where the battle is supposed to be.
Ai remains a solution looking for a problem.
Code autocomplete is a success, password reset via ai is a failure - everything else ... still busy tokenmaxxxing in search of a problem it fits into.
They are making more money than ever before. Maybe Meta leadership doesn’t really care about having a coherent strategy at this point. They can afford to flail around to see if something sticks. Reminds me of Rich kids who have ability to travel the world and find themselves before settling into a career
One problem is that the AI agent market is fiercely competitive. Why build when you can buy? For the foreseeable future there will be a number of competitive models on the "efficient frontier" and I don't think one vendor will pull ahead.
In that market you can build a model and spend a lot of money on it and at best get something that's on the same frontier as everybody else but just as likely end up with uncompetitive models like the ones they have now.
You might save a bit running your own models, doing your own inference, etc. Why not take advantage of "last mover advantage" and buy whatever is best when you need it and figure the odds are good that everybody else is going to buy more GPUs than they need and as a large customer you'll be able to buy in bulk at fire sale prices?
That makes sense in a way, but remember that Meta had previously seen some brief developer glory in the initial Llama release. Going the off-the-shelf route would essentially be giving up on being on the technology frontier in this area, and not monetizing their knowledge assets.
The most effective use of that knowledge might be feeding it into RAG instead of feeding into the base of the pyramid.
>I think most of the substantive criticism of Zuckerberg has been about burning funds.
I'm not in the org myself I know some Meta SWEs tangentially. My understanding is that the biggest criticism is just the chaos of it all. Jumping constantly from one thing to another like headless chickens and accomplishing nothing.
It created an environment where it's kind of impossible to plan and progress your career.
While I mostly agree with your post, I do want to point out one thing:
> Or he could release a model trained largely by existing open weights models. Which without some huge breakthrough probably has no chance of surpassing them, so is pointless.
This seems to be categorically untrue. Composer 2.5 is a substantial improvement on its underlying Kimi base model.
If that is backed up by benchmarks then maybe they should imitate whatever Cursor did. What did they do?
They may eventually have to do that. Or they might be starting with an existing Llama model. Maybe I should have said "huge breakthrough or additional dataset".
> I think most of the substantive criticism of Zuckerberg has been about burning funds.
The 2017 Rohingya massacre in Myanmar? They handed him the death toll. He filed it under growth.
I wonder what will be the next big thing for Zuck after metaverse failure and now AI coming to nothing? Perpetual motion machines?
Gambling! They are already working on a prediction market product. I could see slot machines next.
No need to guess: https://www.reuters.com/business/mark-zuckerberg-directed-me...
CEO Mark Zuckerberg recently dispatched a small team at his company to create a smartphone app similar to Polymarket and Kalshi, the New York Times reported on Tuesday, citing two employees with knowledge of the matter.
The app will probably rely on a video game-like points system instead of users wagering money, though the company has not ruled out betting real money eventually, according to the report.
Breaking news: Meta reallocates 100,000 AI workers to trade tulip bulb derivatives
I'd almost be surprised if these large companies didn't have secret trading divisions.
A lot of companies have Bloomberg terminals for e.g. futures hedging.
They're going to try to push those stupid glasses next. Not convinced they'll succeed since it's not just the technology problem they need to solve. How do I find PMF for a product which encourages others to physically assault my users, while also not having it banned in various countries? We're talking about a societal shift that would need to happen and nobody's going to trust Meta again where that stuff's concerned.
They’re DOA
Only apple has the trust of its users to pull it off. And apple will make sure they do what they can to keep meta out
I don't want to be a Zuckerberg hater, but is there any thing Zuckerberg has done or said in the last few years that wasn't mundane, reactionary or self-aggrandizing?
In other words, was there a single decision or take he made that turned out in his favor?
Meta will continue trying to build a platform that they can control. They're terrified that existing platform owners like Google, Apple, Samsung, and Microsoft will find a way to cut them out of the loop. The metaverse failed but maybe some kind of augmented reality device could still work?
They're pushing hard for a unified platform. For WhatsApps new username feature one can only choose from usernames not already used on instagram or facebook.
Meta can unify their own products but they're still sharecroppers on someone else's farm.
Gubernatorial run in California.
As long as Meta‘s ad platform keeps printing money they can afford to lose a lot on metaverse and AI.
Aka I thought the stuff that these other guys are doing was not so difficult. No one can replace me, of course.
Many such cases.
Have they tried feeding macadamia nuts to the llama?
"I was hoping AI had progressed enough so I could fire you. But you failed to make it so. Therefore, you're fired!"
tokenmaxxing will be a funny footnote like nfts on the tonight show 2 years post-hype
Or: you wasted too much money on failing to replace yourselves so now I have to lay you off. Which is one of the two possible grand outcomes of the AI bubble, which both result in laying people off, because that is all these companies know how to do as a response to stress.
I am not sure that it has to be so zero sum. The AI truth is probably somewhere in the middle; it probably doesn't replace software engineers and it probably won't be deleted as completely useless. My current feeling is that it's a powerful tool I'm happy to pay to use; it doesn't replace me, but it makes it easier to do higher quality work. It feels a lot like IntelliSense, or faster compilers, or getting a 32" monitor. That probably doesn't sustain the bubble, but it's something that people are going to be poking at and making money off of for a long time.
I agree that people are investing as though the world is going to run itself while the ultra-wealthy run off in yachts to compare sizes. If it wasn't AI, it would just be tulips or something. That's just how people are. But maybe they'll be right, who knows.
> The AI truth is probably somewhere in the middle; it probably doesn't replace software engineers and it probably won't be deleted as completely useless.
This is not really somewhere in the middle, I think. It is very close to one of the ends. Because the fear-promise to the idiot-investor class was that it would have those impacts across all industries, not just us nerds. They hate us for refusing to make their silly ideas possible and having irritating fact-based reasons why they can't work, but they don't hate us enough to spend that much money replacing just us. They have lots of other people they hate paying too, and we haven't even made a dent.
Bottom-line win-win! All hail the shareholder value!
I'm guessing this is specifically about Avocado which everyone at Meta would acknowledge is terrible.
I think there are seriously misplaced expectations here. The primary role of AI is transference of effort, while "increased productivity" is just a side-effect (since computers are so much faster than humans at highly repetitive tasks). It's about not having to directly do X anymore (or as often), even though it may take a few rounds to get X to a satisfactory point. But even if following up is needed, most of the effort budget can then be used for Y.
Also those with very heavy investment in AI are looking for bonkers results, which is the cause of their disappointment. They need to reduce their expectations. I for one am loving the results so far.
AI agents are no good.
> At the time, he said, executives were "super optimistic" about tools like Claude Code from AI startup Anthropic.
Some guy in sales at Anthropic has a new yacht though.
If a Meta employee screws up a major project, what happens? What will happen to the executives behind these mass firings and realignment - executives of one of the very top SV companies whose job is dealing with the landscape of disruptive technology development and overreacted to the latest thing? What is the standard for them?
Maybe they'd make faster progress if they worked in the Metaverse.
Why don’t they just got Claude Fable to do it for them? Are they stupid?
Because they already have a Gemini contract.
So they are stupid it seems.
Who is the genius who told him development will get faster?
The man can't catch a break!
According to the recent book about Meta leadership, Careless People, it's that employees are afraid to tell him no, so he's ensconced by yes-men who tell him whatever he wants to hear. He probably has no grasp of market and product development realities.
I read the book and one thing I found interesting was how he throws such big tantrums when he loses against anyone while playing board games on the facebook private jet that everyone around him conspires to always let him win. Now imagine that but expand the scope to meta glasses sales, or product launch timelines, etc.
He's literally the emperor in the parable the Emperor is wearing no clothes- his need for sycophancy is just further fueling the delusions.
> how he throws such big tantrums when he loses against anyone while playing board games on the facebook private jet that everyone around him conspires to always let him win
It's hard to believe that that is a real person and not a fictional person being written against some trope.
I never noticed that the Emperor was a vain man who couldn't admit his lack of wisdom (because only wise people could "see" the beautiful cloth, and he pretended to be able to see it), and that he was surrounded by yes-men (of course he was surrounded by similarly vain men who had to pretend to be wise to keep their positions, but I didn't notice how this turned them to yes-men).
Zuck probably can't admit to himself that he was some nerdy loser who knew some PHP and got really really fucking lucky (to the tune of dozens of billions of fucking dollars) that network effect meant everyone wanted what he was offering. I'm guessing he thinks those billions must be proof that he's smart... So smart that he's unbeatable at any board game.
why havent big tech employees formed a union?
That's a great question. Some form of union has already started in some companies, as far as I know. But not many employees have joined those unions. Probably because most employees have high income and don't really feel like they need collective bargaining power, compared to other low-income laborers.
Poor skills at working together and love of high salaries over work place autonomy.
Mark is really a bad leader with a mwah mwah vision. He is maybe correct in some things. But the execution is really really poor. Plus he does not have followers and believers. He only got money that can simulate followers to a certain extent
If it was still possible to get verbatim results from Google then I believe "mwah mwah vision" would have been an authentic Googlewhack pointing at this comment thread.
Nice thought, but I think strict googlewhacking frowned upon quote usage.
Related:
Meta’s chaotic AI strategy
https://news.ycombinator.com/item?id=48523271
Meta CTO Andrew Bosworth Admits the Company's AI Reorg Was 'Atrocious'
https://news.ycombinator.com/item?id=48548461
Did they really think they could record all their employees screens for a couple months and one-shot the agent thing? This is like junior engineer "let's refactor this monolith" levels of delusion.
I'm sorry if it's a non sequitur but I feel even beyond superintelligence/AI/LLM whatever of the last few years... they've always done this, it's always been somewhat hamfisted
Examples abound of "I reported Nazi hate page. Didn't violate community guidelines. I called my friend a jerk, jokingly, got a month ban
For years. Not restricted to when ChatGPT et al arrived on the scene
(Because, AI in theory makes sense. If you want to monitor things at scale you might use AI - however that's defined - to make your workload easier. When is an account being hijacked? When are bad actors infiltrating the system? Or whatever)
i bet he wants some calculative shit
Or some fuzzy yet inevitably reliable shit.
The modern trend is to think intelligence is generative “like compression” or “predicting next in sequence” rather than iteratively reducing uncertainty, like those fault tolerant humans.
Compression can be defined as reducing uncertainty. If you can predict the next sequence you can compress it to 0 bytes using arithmetic coding. Reliable prediction is what enables compression and it's the link between compression and AI that everyone is talking about.
No one ever in comp sci says artificial intelligence is "like compression", they correctly state that "artificial intelligence IS compression". It's absolutely known and accepted that artificial intelligence (defined as predicting outcomes with a measure of certainty and taking chosen actions towards goals using those predictions) has equivalence to compression in a very hard science way. The hardest part of artificial intelligence is compression and the remaining part, the choice of actions based on predictions is just a tree search to a goal.
Compression in image, video, sound, and text. These items to compressed are all created by humans and we will say represented by files. The difference between an instant of reality and the files is vast. Reality also doesn’t stand still and each instant needs to be captured and interpreted before AI happens.
AI can be just like compression but currently the compute power is no match for details.
Finally these reality details need consideration in any successful implementation. Which means the implementator needs to be aware of the details and successfully relate them to everything else in the model.
I think anyone surprised by these things is not fully engaged with what they are doing.
The factor that is missing in that analysis to me is a time based dynamic stability perspective. Humans have a pretty good ability to go off the rails in reasoning one day and wake up reasonable; a pretty good ability to pursue tasks, despite a multitude of distractions, for ten years or longer. The best models get appreciably worse over a half million tokens. Even using a bunch of limited context agents over time, they lack mental stability. They keep coming up with ideas contrary to the long term idea, and every so often generate ideas that make no sense but they have a hard time letting go of. So the pure functional LLM is compression, but AGI needs some centering process, some high level of dynamic stability to stay sane over time and in the face of 10,000 shiny pretty things to chase.
The harnesses get better, but I haven’t seen much experimentation on long term stability, at least since the “let the LLM run the candy machine” papers from a while ago.
Because the thing missing, even with the largest agentic swarms, is independent intelligence, where it’s given something to own, like say “end to end data quality as we add more clients” (for a SaaS) and it just figures out what that means at each time, mutating its role and solutions to fix the external world, without getting silly.
i blame Wang!
Wang should be CEO. Zuck is the one at fault here.
How does he get to decide what's "enough"? Reality will tell us, he can only place bets, whether it pans out isn't something that he has any say in.