- The abrupt swing in many non-technology company IT departments from "hey developer, you aren't using enough tokens" to this is just too funny.
And I'm seeing almost no self-awareness from leaders. They are making decisions about things that they just don't understand. And are completely unworried about it. Just blindly following whatever the news cycle is about AI.
- > leaders
Don’t play their game and call them leaders. They are management, bosses, executives.
> They are making decisions about things that they just don't understand. And are completely unworried about it.
Clowns, even.
> Just blindly following whatever the news cycle is about AI.
But followers might be most apt.
——
This is such a huge pet peeve of mine. Describing management goofs using their language that makes them sound all-so-brilliant. We constantly watch these people do the dumbest shit and then they go around describing themselves as “thought leaders” and “servant leaders”. When, really, most are just clowns with fragile egos.
- The closer people live to the consequences of their decisions the more rational they become. Until leaders(and I use that term loosely) are held accountable, the insanity will continue.
- I’m sorry you are used to working with out of touch leadership. Not all companies are like that. Even big ones can have smart, empathetic leaders. Although very often money gets in the way of empathy.
- Money alao has the problematic tendency to warp the people around you, it's its own kind of gravity. The more powerful you are the more you attract yesmannerism and the more you lose touch with what's going on.
- I've been enjoying journalist Ed Zitron's recent diatribes about how impossible it is to find a business leader who had a plan for measuring their ROI from adopting AI coding.
What he says he's consistently hearing from them mirrors what I saw at my own employer: they thought they had ROI metrics, but they actually only had usage metrics such as "lines of code committed" or "number of pull requests". The only way those could possibly work as an ROI measure is if your business charges customers by the line of code.
- I feel like most successful businesses have such a moat of required capital to compete with them that even tho in theory poor decisions like this is supposed to give opportunities for entreprenuers to hit when the big dogs make a wrong move, it doesn't end up happening.
- Groups resist to change - the bigger the group, the most resistance there is.
As a leader, pushing for rapid change cannot really be nuanced lest the push dissipates into the organization's entropy.
- Perhaps, but the change you get (if any) is most likely to be what you push for and reward/punish.
It's irrational to push for tokenmaxxing (literally "please increase our AI spending") and not expect that this is the result you are going to get. You won't get productivity increase, since that is not what you are pushing for - you will get token usage maximization (engineers running inane agentic tasks against your code base to increase usage, using company paid AI for their side projects, etc, etc).
- The evidence suggests that many tech leaders do not realize that an immediate result of heavy handed uninformed top down decision making is transforming the “work together, succeed together, giving quality” ethos into a cynical game theory minimax effort to game whatever stupid arbitrary metrics are used to implement the top down fad of the quarter; do it consistently and you get a work force that can be given a metric and immediately, instinctively, tell you how the work flow will be adjusted for the new metric, and where the difficult problems will be shunted to.
- I'm not sure the leaders would disagree with what you're saying. They tokenmaxxed to understand what it looks like when AI gets into every corner of the business; now they feel they've gotten enough info (or at least that more info wouldn't be worth the cost), so they're adding in cost controls. As the article says, this is not great for AI model providers trying to predict what their future revenue is going to be, but it's not obvious that there's any mistake here for AI users.
- > They tokenmaxxed to understand what it looks like when AI gets into every corner of the business
Perhaps that is what they were trying to do, but the reality is that all they will have got is a large token bill. The decision makers may have hoped that tokens would be used in most productive fashion possible so they could evaluate if the cost was worth it, but what they will have actually got is what they asked for and measured, high token usage (applied to whatever people needed to do to get their usage stats up, regardless of productivity).
The other business-as-usual factor is that there will be false reporting up the chain, so if the company understands the CEO want to see high AI usage and productivity gains, then s/he will see high AI usage (a large token bill) and will be fed success reports of corresponding productivity gains.
In a typical corporate environment, if all your peers are reporting success, achieving what the CEO wants, do you want to be the only one reporting failure? So - everyone reports high AI usage (easy for the employees to make happen), and most everyone also reports productivity gains if they understand this is the expectation.
- I’m imagining a lot of programmers suddenly being given the impossible task of reporting what worked and what didn’t, and middle management making up some retrospective evaluation with fat PowerPoint decks and meaningless graphs in an effort to present to C-levels some measures of success other than token use.
- As the saying goes "figures can't lie, but liars can figure".
If you want to report productivity gains or cost savings from some initiative (increased AI usage or whatever) and need some stats to point to, then you just point to whatever is working, for whatever reason, and attribute the success to the new initiative.
In a company I used to work for, one manager, when pushed to increase machine learning usage (a few years back, before ML became AI), just renamed his product from foo to foo-ML (with ZERO ML usage), and reported how well it is working. He has since been promoted twice.
- It’s not clear companies were measuring anything but token usage. What information could leadership have collected to determine what worked, what didn’t, and what needs more data? Other than the balance sheet and revenue, do companies actually have sufficient information to understand the results?
- Were they trying to measure other things? Definitely. The COO at Uber, one of the examples in the source article, has talked publicly about how they've searched for (and so far failed to find) a link between micro-level metrics driven by AI and concrete improvements in high level project velocity.
Do these measurements have sufficient information? As much as any, I'd guess. It sounds like you already know that it's pretty hard in general to measure the productive output of software development organizations.
- That's nothing new though. It's just very obvious this time.
- During ZIRP they discovered that the way to lead companies nowadays is to become a maxxer of whatever current fad is, and the more you maxx the better. And then when things change and you're wrong, you'll be a strong leader and, in ZIRPs case fire everyone you over-hired, with AI will be similar.
Why be a normal guy that waits to see what happens and is measured and pragmatic when you can get attention basically through the whole cycle by being the earliest adopter, adopt it to the maxx, then also be the loudest big brain when the tide changes and be praised for "taking hard decisions" when you revert everything you said so far?
The fakemaxxing economy.
- A special case of the more general cringe economy we're in. The dumbest, most outrageous ideas win, amplified by social media. Say stupid sh*t loudly, be wrong, profit.
- I've never seen self-awareness from leaders. They always lead on vibes.
Understanding this was one of the most important things in my career.
- Having studied control theory I think it makes perfect sense. When trying to make a system target a new level it's quite natural for there to be overshoot that needs to be reigned in. It's also natural for the correction to go too far and need to be corrected in turn. This is not indicative of stupidity it's completely normal.
It would only be laughable if they waited way too long to reverse course, but I don't think that's the case.
- The actual cost is going to drop 99% in ~4 years.
How much that makes it into enterprise pricing is TBD, since none of the hyper scalers are making money yet of selling AI inference.
Almost all businesses are ahead of the gun. For most of their use cases, AI is either not yet good enough on its own, or good enough but too expensive.
No one wants to get left behind, so everyone's trying to get onto it now, even though it's not ready for what most enterprises want to do with it.
It's easy for them to look at a small startup without billions of lines of legacy business logic debt and see them having success and wonder why they can't have just as much - or more - why they're bigger so they should have better and more success, right???
Wrong...
But when it gets ~99% cheaper for local inference over the next 4 years, at the same time the price per watt improve 4x -> a lot of those cases will start to pencil out.
- Going from Opus 4.5 to 4.7 secretly required 6x more compute to run. 4.8 is apparently 30% more on top. I haven't seen any optimizations lately aside from distillation. Nobody's optimizing, they're just scaling up.
- > Nobody's optimizing
The Chinese, since they lack computing hardware due to US export controls, are.
- And our export controls are going to turn China into a winner in the AI arms race if we're not careful.
- DeepSeek and Alibaba would like to have a word.
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- > The actual cost is going to drop 99%
Do you mean the marginal cost by the producer, or the cost on the consumer? I can't see the price of electricity falling much, and the demand curve is apparently exponential if the hype is to be believed.
- DeepSeep V4 Pro is 99% cheaper than similarly performing models were 2 years ago (if such a model even existed).
Computing has always been about how to wring out more efficiency. The ENIAC was 150,000 watts, with 3 phase 240 volt power, and cost about $500,000.
My day to day laptop (a year old) is 35 watts, with 1 phase 20 volt power, and cost $1,000, so that's 99.98% less power consumption, 99.8% cheaper, and it has about 10 orders of magnitude more computing power, all on a time span of 80 years.
- Moore’s law is dead.
- I don't see how this is even remotely true. Unless there's some super breakthrough into a fundamentally different architecture, there's not really a path to a 50% reduction in price, much less a 99% reduction.
- In fairness, I think _current_ capabilities will be cheaper. So the models of today will be run drastically cheaper in 4 years.
- And yet 90% drops for the same level of quality every 18 months have happened like clockwork...
And the technology already exists on the algorithmic front TODAY to lock in another 10x gain -> when, typically, algorithmic gains only account for ~30% of that drop and the other ~70% comes from better data (often synthetic) and knowledge distilation from frontier models.
Just look at DeepSeek's pricing...
- What makes you think prices will drop? Everyone I’ve spoken to believes they will only skyrocket. Genuinely curious
- The technology already exists now on the algorithmic front for the next 10x drop between everyone adopting DeepSeek's MLA, MoE (mostly already done), Medusa (a better version of Google's speculative decoding), Kimi's Attn Residuals, and Mimo's Sliding Window Attn, and (possibly) Microsoft's 1.58b (this may be a nothing burger).
Historic trends, every 18 months, performance for the same level of quality has gone down 90%.
See: https://www.reddit.com/r/LocalLLaMA/comments/1gpr2p4/llms_co...
And Chart 13 here: https://www.rdworldonline.com/ais-great-compression-20-chart...
And here: https://epoch.ai/data-insights/llm-inference-price-trends
Historically, algorithmic gains are only ~30% of the pie, but there's enough out there to get to 10x, with just what's available already. The other ~70% of the pie is better training data (often synthetic) and distilling frontier knowledge. There's no sign we are tapped out on that front.
Additionally, GRAM (from ~10 days ago) is likely to be a 5-10x on its own (if not substantially more for smaller models). It's unlikely within 4 years LeCun's JEPA ideas and similar ideas like GRAM applied to LLMs have ZERO impact. The preliminary results are absolutely astounding (5000x better reasoning - this is not peanuts).
Further, that's not even counting that cost per watt is still dropping ~2x every 2 years on its own on the hardware front.
If you look at the "cost" of inference. People think it's electricity - but it's currently almost ~80% hardware amortization. The memory shortage is not going to last, nor are Nvidia's ~80-90% margins.
The human brain is still 8-10 orders of magnitude more efficient than the best LLMs of today. With ~1/10th of global capex riding on AI, if you don't think they're going to knock of 2 orders of magnitude more, when it's this obvious and easy... I don't know what to tell you...
Sure, it might take 6 years instead of 4. My crystal ball isn't perfect.
- Sure, the price will come down a lot, even if we can argue about the timeline.
I think what will also happen, once we get past this current CEO AI FOMO mania, is that companies will start to look at AI spending more rationally like any other company expense, and will revert to more rational decision making.
Even if the cost comes down considerably over the next few years, that's plenty of time for companies to look at their financial results and question why AI expenditure isn't resulting in increase in revenue and/or profitability.
- This is great food for thought, thank you
- Additionally, on the context front -> all the labs are aware that for many tasks you can get 10x+ increases in output quality by feeding better context.
See https://arxiv.org/abs/2604.04364.
This won't really show up in benchmarks, but it will impact real world usage on the most common use cases.
I'm doing a study right now on the impacts of better context for small models to fix bugs.
A very dumb algorithm can make small models perform at 10x+ model sizes. I'll be surprised if it can't get to 20x+
- I didn't take you seriously initially but after reading this, i think you are the real deal.
Thank you for sharing this and for having the intellectual courage to hold to a sound reasoning that may be unpopular initially.
- This is mostly slop. But you may be directionally correct
- Prices have been very obviously trending up, not down. Even open weights models are becoming more expensive with every release. Computer hardware is ballooning in price.
- Prices are going up for BETTER quality -> not for the SAME level of quality.
People are willing to pay more for BETTER quality.
You obviously haven't seen DeepSeek v4 Pro's pricing if you think pricing only goes up...
- Grab a 5090 and run Qwen 3.6 35b on it (6 parameter seems to work best for me).
Then buy $10 (or $2, if you're cheap, and they take PayPal) of DeepSeek credits.
Whilst you're at it spring for a Claude subscription too and GPT.
Switch models between Qwen, DeepSeek Flash, DeepSeek Pro, and you can meet 99% of your code generation needs.
Hop over to Opus 4.7 (or 4.8, but I haven't really used it yet) and GPT-5.5 when doing very complex architecture/design or troubleshooting something where DeepSeek Pro is getting stuck.
It is ridiculous how cheap this stuff is now. It's affordable at third world prices.
- Just wait for the next model and the next model architecture. Just wait for it, bro.
- AI is overhyped. I have yet to see an end user product that in itself isnt a wrapper around LLMs that is impressive created by LLM assistance. I have also yet to see dramatic increases of revenue of companies using LLMs that don't involve selling things in its supply chain. Is it a nice affordance? Sure. 1T capex good? No.
If it was so good I would expect to see 2005-2015 advancements yearly.
Meanwhile China is blowing past the world with real improvements in the real world- solar, EVs, etc. meanwhile people keep making their fancy sans serif websites about todo apps, faster than ever before. Useless.
- > I have yet to see an end user product that in itself isnt a wrapper around LLMs that is impressive created by LLM assistance.
I don’t disagree that AI is overhyped. But I think you are probably looking in the wrong place.
I think most software that is written isn’t really a product, at least not a public product. It’s an in-house tool or a one-off project needed to complete some larger task. People everywhere are always writing small programs that make their life or job just a bit easier (and explains why so many corporate projects are little more than an excel spreadsheet).
And there are a lot of people who have made custom software just for themselves with AI. Not a product, just a tool or project that finally made sense to build.
- But where's the revenue from those? It has to add up to a couple trillion dollars to break even on the capital spending.
- Would you say the same about any other tool, like where is the revenue caused by Susan in accounting having a computer, shouldn't we take away her computer if she can't prove a benefit?
- The benefit of a computer would be trivial to demonstrate.
- not sure one would expect huge revenue increases from these internal tools, but maybe dramatic cost savings? Surely a lot of corporate processes could be automated?
- That's been the dream for the 40 years I've been paying attention. And in that time, I've seen plenty of incremental changes but never the kind of sudden sea change that the hype machine anticipates.
The perennial reality is that automation is inherently inflexible, so there's only so much of it that you can do before you've committed a huge strategic blunder by making your business resistant to change and severely curtailing its ability to cope with situations that don't cleanly fit the mold. So then we need to hack in ways to deal with the exceptions, but, since they're hacked in, they're often painful and time consuming. Sometimes so much so that after the new process stabilizes it turns out to be even more cumbersome and require more manual effort than the system it replaced.
When anyone other than a technologist suggests doing that kind of thing, we call it "bureaucracy", and we hate it. I think maybe what we have trouble seeing is that there's actually a pretty fundamental difference between automating purely technical processes like server deployment, and automating processes that are fundamentally about mediating human interactions.
- Productivity gains seem like it’s at best a wash when you factor in the massive tech debt cleanup and additional time needed to spec and review.
- Misuse of AI tools because of continuing a fundamentally broken software development process.
- AI is both overhyped but is also revolutionary at the same time.
I would agree that a lot of companies talking a big talk about using LLMs are failing to actually apply it in a sensible way to their business.
- Oh, war is transforming hard.
- In my opinion, the problem is not even the cost. The problem is that people are using AI for running recurrent stuff instead of writing code to automate it.
For example. Imagine that you are comparing two documents (let's assume diff doesn't exist). You could ask an AI to compare the differences from you or you could use AI to write a tool to do it. For whatever reason, people are starting to go with the former not realizing that now they basically have to pay to compare documents.
- I have exposure to AI initiatives at several companies including a few F500's. I have seen teams dump huge logs into frontier models that took hours to get so-so results that we were able to replace with a few lines of python code at 1000 times the speed and 100% accuracy. When asked why they were doing this they literally said "because we don't understand the subject matter so we were depending on the AI". I saw one team file a complaint with a vendor about a frontier backed coding harness and it's inability to consistently format headers because they were using it as a reporting engine. When I recommended they just use the coding tool to write code to generate reports you would have thought I had just cured cancer from their response. I frequently see people complain about the fact that AI is going to take their jobs and then see them gripe about the fact that AI is 'worthless' because it can't do more of their job than it already does. It's easy to see the difference between the people seeing 10x productivity gains from leveraging AI and those who aren't and it's not the AI.
- i have trouble understanding these situations, e.g. the AI itself would presumably make the suggestion to write a python script for such a task. It seems to me that there two huge problems right now * understanding which category of problems an LLM is an appropriate solution for (rather than throwing LLMs at any and all problems) * matching model capability (and therefore cost) to the problem at hand. You can easily overspend massively by using a model that's too powerful
- I've heard this framed as "AI raises the floor by 2x or less but raises the ceiling by 10x or more"
- Someone asked me if I was using models for fantasy sports, and if it was smart enough to help make decisions about drafting.
My answer: no, but it was able to help me find the website and social handles for every beat writer for every team, and generate a simple website where I can do a daily skim of teams/players and draw my own conclusions.
LLMs are a tool, not a panacea.
- Laziness, pure and simple. The inevitable consequence of “the LLm is the compiler now”. And what do you even expect people to do when they are forced at threat of termination to use AI for everything as much as possible? Not to mention people are being pressured to do insane thing like review hundreds of pull requests per day and deliver like 15 features per week so OBVIOUSLY there isn’t time to build out proper tooling. Just shove everything in a prompt and call it a day. Some people have families to feed, just do what you’re told.
- Because you look at the work from the perspective of a programmer, not the perspective of a regular person.
Normal people have never gone around automating their work. The most automation they do is dynamic tables on excel sheets.
I obviously know building a tool that can programmatically do something is a better solution, but I think that requires a fundamental shift in how people work. People need to be told by someone "this is how you should be using the AI" but right now they're simple told "use the AI".
- Agreed. I’ve been telling my team to build up internal packages so we can push all that ad hoc reinvention into something more tangible and deterministic. Invest the $$$ in inference into something the agent can reach for next time that’s neutral and consumable by other code to reduce future spend.
- Yes. Build compact CLI-driven tools, write a skill for it (you can use your agent to do most of this work for you).
It just requires being willing to think instead of mashing prompts into a keyboard.
- Same, even opus favor short term solution and scripts with a billion flags that constabtly require rescanning to understand how to launch it is a constant struggle to get it to build sane default and reusable scripts that run with minimal parameters
- Yeah, and what's up with adding dry run to everything? I saw some code that doesn't write anything but still the AI added a dry run which had a completely different codebase
- Because dry run is in a lot of scripts in its training data. It's not "thinking" about the script or the concept of a dry run.
- And everything configurable gets an environment variable. Editing the first few lines in the script is a fine way to configure things in Python.
- It's this and worse. To use your example, it's like people using AI to write a diff algorithm, incorrectly, then using AI to fix it, because they don't know that diff exists already. Lazyness and starting development with a very low level of understanding. People think lowering the barrier to entry is a good thing, when in reality there are just fundamentals and things you just have to know before you can start using a tool like llms properly.
- AI can do things around semantic analysis that a deterministic diff tool cannot.
I understand and agree with your point though.
- I'm curious if you could give me an example of something that couldn't be down deterministically. We have fuzzy search/matching too ? Regex is a monster when used correctly.
- Pretty much anything for which you'd need intelligence of any kind. Questions such as: Do these two paragraphs have the same semantic meaning? Do they have the same sentiment? Do these two methods have the same contract? etc. Not all documents our code and even with code deterministic tools gets you only so far.
- A model can 'analyze' the intent of a patch, 'understand' it, and then correctly merge it in a derived codebase, going further than merely resolving conflicts.
- I sometimes find myself with thousands of log lines from a problematic execution and a known good reference, wondering nonspecifically if "something weird" happened in the first one. I don't think there's any matching-based solution there; you need a scan process that understands variations in execution time, object identifiers, etc. aren't meaningful.
- You would need specific domain knowledge and a very clever parser, I've done one for a ridiculously over engineered system but a pain. That's fair but how often would you need it? Certainly not token maxing amounts!
- Isn't that the supposed point of it though? At least how it is marketed/hyped. Don't use your brain, you don't need one, spend all your thinking energy on... dunno, something else, and leave all the "mundane" stuff to AI. Just pay for the tokens, it's going to make you 10x more efficient, the $1000/month is worth it.
- 100% this. For my own company I mostly build deterministic workflows that may have a simple AI step in the middle using an appropriate Chinese model in a very limited way. I wouldn't want to burn tokens to satisfy some metric.
With this AI is a fallback and not the default. Sounds like large companies have it backwards.
- Same with writing boilerplate code. It’s been a solved problem yet here we are.
- it's all about cost at the end of the day. if you're allowed and encouraged to tokenmaxx, then of course this'll happen.
- I agree, but even this use case isn't the most wasteful. The interwebs says Agentic consumes 50% of token use, but I'd hazard this number is north of 90% for many shops. My cynical view of Agentic is its sole purpose is to make "number go up".
- Look at me! I'm the smartest guy. I've wasted 10M tokens! No one has wasted more!
- There's an old saying, "in the land of the blind, the one-eyed man is king."
Here we have the opposite: In the land of the one-eyed, the blind are leading.
The blind in this case are all those executives and managers who don't understand much about AI's current potential and limitations, and so far have treated it like a magic button that will solve everything. The one-eyed are rank-and-file employees who maybe sort of know a little more about AI.
- Executives and managers are the ones who correctly understood which game was being played. The game we are playing is not one of making good products, it's one of getting money from people who both have more money and are stupider than you. They're succeeding at that. We're also doing it, but we're not getting as much money.
- In many cases, the people who have more money and are stupider than you are other executives. Sam Altman is arguably one of the executives who know how the game is played. OpenAI is at the front. Microsoft's executives are an example of the ones who got played.
- The cost is a problem, but IMHO more important is delegating so much of your internal knowledge, thinking, and systems to a 3rd party.
We are very close to the point where if Claude and ChatGPT APIs are down, companies cannot function. How is that introduced so quickly into so many critical places without taking that specific fact in consideration? What is the plan for all those companies whose workflows now depend heavily on a remote LLM whenever the services get cut? What if your company account gets banned?
In some ways it is worth than depending on a company for hosting, because even your debugging tools are based on AI. MCP is great to go through datadog, sentry, until your agent or the MCP server are down and you don't know how to look for the issue yourself because you do not actually understand how your systems work.
- Those sound like problems for another quarter. The people making the decisions ride the AI hype wave, and if in the worst case the company tanks one day, they take their severance package and leave.
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- Corporate or corporate in programming space?
90%+ of corporate people are not programmers. 1 programmers can cause the same token damage with a bunch of concurrent agents as a couple thousand Karens in compliance asking a chatbot questions
It's much easier to deliver incremental AI ROI on the later even if it's hard to measure/quantify. A 1000 tokens might point this compliance person in the right direction on a key problem. Meanwhile 1000 tokens doesn't get you anything useful on coding
- They are likely also starting to realize that the end result of their anthropic contract is that nobody but anthropic knows how to run their business. Why would anthropic not treat their business like a utility in the future?
- On the one hand, organizations are without question using LLM's well beyond what is actually necessary, and as reality kicks in they're forced to scale back accordingly. However at the same time, on intervals counted in months, we're seeing breakthroughs both in hardware and software that dramatically reduce the cost of inference.
Between corporate FOMO and the rapidly decreasing costs of actually running LLM's I'm interested to see at which side of the spectrum these two meet
- Would have been nice to see 'soaring costs' with numbers. WSJ could do better here. Hundreds of thousands of dollars a month is nothing compared to how much they take with better financial models.
- The other day we (wrongly) concluded that product market fit has been achieved and now the rivers of hot molten milk chocolate and honey are all that's in the future etc.
- Don't have a subscription to wsj.
Only thing I can say AI was useful for, in a corporate environment, was learning a new coding language on the fly. Gives me a baseline to work off of and fix.
But I can learn without it, too. A nice tool, but not a need.
- > Don't have a subscription to wsj.
An ironic analogy sort of, once media started hiding behind a paywall, I just stopped reading them rather than paying. Same with LLMs - usable if cheap/free.
- These articles are weird because rationing consumption based on price is one of the most fundamental concepts in economics.
- Another reason to favor using AI to build automation instead of relying on it in prod: the risk of war and global instability.
If LLMs are genuinely helpful or even decisive in a military engagement, you can expect any host country to commandeer whatever data centers they need, leaving commercial entities to bid up the prices on the leftover capacity.
Another risk is that data centers are a great target for cyber warfare.
It’s ideal if your business can leverage LLMs when they’re online but continue to operate profitably when they’re offline.
- Even regular warfare, if the Middle East AWS regions are an indication. The giant and arguably excessive data centers being built are not hardened physically.
- There's a paywall, but it's an interesting question how much of the recent explosion of the AI companies revenues is because of the explosion in prices, and how much their customers will accept the increased prices.
- As a developer, I don’t think it’s just that costs are going up. I’m also seeing more people lately talk about “vibe slop”.
- I've noticed as well. A lot of pull requests are just agents running constantly, hoping to have produced something of value. Entropy is at an all-time high, though.
- I’ve seen comments on other threads on this subject the general idea that these article headlines are overstating the pullback from AI.
In other words, the news cycle is looking for an AI story that lands with readers, and that the example of Uber blowing through its AI budget and Microsoft discontinuing use of Claude internally are not good indicators.
I agree that those aren’t good indicators.
However, at some point we have to remember that CEOs and boards of directors are just regular morons who read the news the same way everyone else does.
At some point, if a lot of corporate leaders associate AI with mediocre results, high costs, and public backlash, they might just start saying “this juice isn’t worth the squeeze.”
- It will be interesting to see to see Anthropic’s “revenue bubble” pop as this happens. At least it should hopefully free up some capacity.
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- LLM doesn't work, let alone profit.
- Yesterday I updated our dependency on the sqlx crate and put up a PR, and it failed in the CI build in a way I couldn’t reproduce locally.
I asked codex to take a look, and it:
- Grabbed the CI logs on its own to figure out what the CI error was
- Looked at my local setup
- Looked at the changes in sqlx from 0.8 to 0.9
And figured out that sqlx depends on an updated version of the “whoami” crate but doesn’t specify default features, which causes it to fall back on a stub implementation that makes the default user “anonymous”, which was failing to authenticate to the UNIX socket we use in our CI Postgres server. It patched the environment variable for our docker container to explicitly specify a username and the issue was fixed.
It would’ve taken me probably several hours to figure this out on my own. It took codex maybe 5 minutes.
Tell me again how LLM’s “don’t work”?
- I agree with your point in the broad sense, but the example might be bad. If sqlx is an important crate, and not stable yet, upgrading it without reading the changelog is honestly a flaw in your team process. Using the AI to fix organisational issues is typically one of the reasons I'm very skeptical of AI improving productivity in the long run.
I'm not taking a shot, to be clear, we had a similar issue a few years ago and we made sure this wouldn't happen again, that's absolutely not a shot, nor do I think it's a character flaw to use AI, au contraire, this is a very good use. I'm just worried that because AI is so good at fixing minor issues caused by governance/organisation flaws, we will be stuck using it to fix those and be trapped in mediocrity (that's not an issue for me, mediocrity is where I work best, but I'm a bit sad for the great Devs I've worked with.)
- > If sqlx is an important crate, and not stable yet, upgrading it without reading the changelog is honestly a flaw in your team process
It’s not in the changelog though, this is an update of a transitive dependency that inadvertently changed the default behavior. sqlx didn’t document this because they didn’t even know it changed.
Even if it was a documented change, our process caught it because it was caught by CI. The issue itself was only a result of how our CI was configured (we had a database url with a domain socket path that didn’t explicitly specify a username, and we inadvertently relied on the default of “the current user”, which the whoami crate now defaults to “anonymous”.) I don’t see an issue in our “team process” (whatever that means) at all.
- You used it in a way where the result was simple and you could verify its correctness. You used it as a super-search tool, it's good at that. It's a different use case than having it generate a lot of code from scratch.
- Exactly. If people understood that this is super-search and super-autocomplete, we'd maybe find a real net-positive use for the tech. But I think the conversational tone will keep fooling us, especially since the LLM providers have heavily invested in that direction.
- elaborate please, how does it not work?
117 points by 1vuio0pswjnm7 5 hours ago | 111 comments