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.
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".
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
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.
Very successful by just being careful and walking it forward.
Yes its about 2 years, August 2024 from git it looks like.
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.
My main goal is to first get all of these open-source alternatives to start building themselves autonomously using loops and a Hermes-like scheduler before I focused on marketing. This is almost complete.
For marketing, we are building a GTM engine using an open-source CRM (Twenty). We have LLMs use the Twenty CRM API to bring in leads from X, LinkedIn, and the Web.
The cloud hosting is not the only monetization. We’re going to use these open-source SaaS to build a decentralized, interoperable marketplace where the people actually bring value, the sellers, can sell without those rent-seeking entities like Amazon taking a piece of every sale. LLMs are already going to start jumping across these marketplace moats.
The other monetization is going to be letting agents actually run these SaaS and see if they run a business autonomously. Like VendBench but an actual online business. I’m thinking of starting a designer brand, connecting to a POD (print on demand) and then let the agent create seasonal lines, handle customer service, and make sure orders are going to the POD and being processed. Doing this with restaurants and other verticals will probably need some human supervision.
I'm very curious how much revenue this is generating.
Next is a full stack framework and Keystone is a CMS built on top of Prisma and GraphQL. Keystone was created by this Australian company called Thinkmill. They have used it to help businesses build custom backend systems for more than a decade. But it needed to be deployed separately from Next and they were using emotion css for their dashboard and I wanted to use Tailwind/Shadcn. So first, I had to make the Next Keystone Starter that brought in Keystone into Next so each SaaS is just 1 Next app with a built-in storefront, GraphQL API, and dashboard.
Once that was built (and it took a while tbh), I started to build the Shopify and Toast alternative. But the itch to get these built quickly and autonomously had me working on the harness in the past months and now that is nearly complete.
Here is the e-commerce[2] and restaurant[3] repos. They have a link to deployed demos you can check out as well.
As far as revenue, I don’t feel comfortable relaying that right now. We have other revenue streams like fractional CTO where companies give us equity to manage all their tech and that is quite hard to quantify. Before Openfronts being built, I built Openship, and e-commerce OMS and that has exceeded 5M orders processed since its inception in 2019. That’s not counting orders by businesses running it on-prem.
I actually posted about this vision on HN[4] when I launched Openship and the response is what kept me building.
2. https://github.com/openshiporg/openfront
Second, take this for what it is: your product may not be compelling in its current form. Building it to many different markets will not make it compelling. If you had a stronger revenue, please share it. This sounds incredibly thin.
Third, dont mistake building the same thing 20 times for different verticals for bonifide software skills. When a SWE builds the thing they have built before its usually to learn a language which is the easiest part of software. There is a reason a common adage in software is "9 women cant make a baby in a month". Breath is no replacement for depth.
In the end, I built Openship and Openfront for my e-commerce business and then turned them into SaaS. All the revenue for these are just a cherry on top of our existing e-commerce businesses.
And I worked with Next and Keystone long before AI came along. Check my GitHub commits if you need some back story.
And I’m not building these 20 SaaS to prove I have bonafide SWE skills. I’m building them because I plan to have my own gyms, hotels, grocery stores down the line powered by these SaaS. SWE to me a means to an end and that’s to have many different businesses.
We’re also very bullish that the chat interface is the universal UX now. Instead of sifting through the dashboard to change a product price or sell in a new region, you can use the built-in agent and just tell it to do that. Every Openfront comes with an MCP server that interfaces with the API so the agent can literally do anything you can do using the dashboard and API. This is where an agent running the business autonomously comes in.
And even then if you’re not satisfied with the backend API for each vertical, these Next apps can be forked and adapted to tightly fit your business instead of you messing with configs, you can make the app your own.
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.
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.
"What can we get rid of for MVP" as a design strategy vs a way to iterate fast, for instance. Cutting things isn't a way to product cohesion, especially if you never go back to do the full-featured version.
Sometimes I wonder how many features or products flopped because the MVP dropped the things that would've actually taken off, and the business "smartly" pivoted away.
There's still a limit to how many new features you could shove in front of your users per month. But what if they were all much more baked out of the gate?
(See also: "data driven" product management as an excuse to not have your own vision for the product. If three competitors build a lot more in the span of six months, but have to depend more on their own skills and instincts vs A/Bing every little detail, maybe more of them will ship more bold and interesting new things.)
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.
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
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.
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.
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.
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.
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.
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.
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.
Architectural decisions are not lintable.
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.
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.
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.
It never was going to happen.
Always the same story: https://en.wikipedia.org/wiki/Gartner_hype_cycle#/media/File...
Amara’s law: We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.
This continues to be applied to AI where people think is going to be next 12 to 18 months. Changes are coming but certainly not at the rate Zuckerberg and most people are thinking.
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.
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.
https://fortune.com/2026/04/04/ai-jobs-future-not-important-...
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.
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.)
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
However, if you get 2 to 3 times the code in the interim, that's probably less than what's needed. I find myself cycle through almost 10x-20x amount of code implementations to get what I want which is actually less code, simple solution and desired behavior.
Given a specific behavior, there are usually just 1 simplest implementation, whether done by human or AI. However, there are 100 ways to do it with more complexity and either handwritten or AI slop, it will mean pain down the line. We used to have a lot of handwritten complexity because of certain design pattern culture, but they used to be contained because the ability to generate them is costly. Now it's much more risky and therefore more important to have simplicity as the guiding principle in ALL projects.
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.
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...
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.
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.
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.
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.
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.
Unfortunately, travel keeps getting less flexible, with worse cancelation policies.
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.
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.
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.
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.
The fact that their advancement suggested that pouring more compute would continue working was also especially attractive to investors: it made a massive R&D budget feel like less of a risk.
They were allegedly massive but the cost and returns were not worth it.
Feels less like the pace of foundation model development and more so a specific failure of one organization to do something important.
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.
Can't help but think that Meta's digital networking expertise is built atop a human-networking clusterf*ck
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.
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
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.
The whole hype cycle has been pure delusion. Just like the Metaverse hype cycle before it.
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
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.
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.
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.
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.
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.
Having agents is like going from walking to having a bicycle.
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.
That's... not quite right. The employee data is used in AI training and is intended to be used this way. But despite not correctly ACLing the data for a couple weeks, it is believed it was not accessed inappropriately.
Over the past six months or so, OpenAI's internal team has completely shifted from being heavy ChatGPT users to using Codex. Once you start using an agent like Codex, it is very hard to go back.This shift is truly transformative.
I am also aware that some of the consumer agent products on the market are growing very rapidly, such as Manus and GenSpark. Not to mention Claude Code and Codex.
I've heard rumors that it had to do with talent loss, but just rumors.
This was before llama4's lukewarm launch.
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?
> 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.
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...
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.
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!
Hmmm... so who is going to be thrown under the bus?
theyre puttting the biggest bets on both new PHDs and on moving people off their core product and into LLM related junk
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.
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.
Think about the number of kids that were harmed being fed ads and nonsense content to enable this... this a scandal IMO.
So you ask yourself, _if this thing disappeared tomorrow_, what would be the actual loss. It's definitely not it's valuation.
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.
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.
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 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.
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.
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
In other words, was there a single decision or take he made that turned out in his favor?
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.
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.
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?
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.
> 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.
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".
The 2017 Rohingya massacre in Myanmar? They handed him the death toll. He filed it under growth.
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.
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.
Many such cases.
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.
Some guy in sales at Anthropic has a new yacht though.
The man can't catch a break!
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.
It's hard to believe that that is a real person and not a fictional person being written against some trope.
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.
Meta’s chaotic AI strategy
https://news.ycombinator.com/item?id=48523271
Meta CTO Andrew Bosworth Admits the Company's AI Reorg Was 'Atrocious'
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.
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.
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 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.