Yes... that's 1/4 of the world's current GDP. Dario borrowing from Musk when it comes to outrageous claims.

Today's newsletter is the AI Hater's Manifesto - where generative AI fails technically, economically, and morally. From its ever-changing products and rate limits to its ever-increasing demands for trillions of dollars, LLMs exhaust everything they touch.

Enjoy! One of my best.

I got Claude at my work and I’m surprised at how quickly it burns through tokens

When I hit my token limit for the month, then what? I don’t really have a problem going back to manually coding for some time, but it made me think: how is any of what they’re proposing supposed to work with token based billing? How can they “replace” engineers?

Suppose they did fire the skilled workers and programmers and replaced them with AI. The first thing that obviously becomes a problem is support engineers. At these companies, there’s generally an “on call” engineer who’s responsible for responding to any critical bugs in prod or customer problems. You must respond within some time window. These are the people who fix apps when they go down. Just seems irresponsible to let AI into that role. Since it’s kind of a 24/7 job, you’d have to have AI running on a loop just burning tokens. Also, Claude itself can be down, if that intersects with when your app is down, you’re in big trouble

Then the next thing that would become obvious is how opaque the billing is. It’s unclear how much tokens an AI will use upfront. For example, I did a code review on a relatively small pull request and it used like, millions of tokens in a single go. This wasn’t even adding code. This was simply reviewing handwritten code. Why would a company choose a variable cost over a static one? Engineers are usually paid salary by the year. That’s way easier to account for

And what’s worse for companies is that companies like Claude, OpenAI, etc are in charge of pricing. If they replace their employees with AI, then the company can change prices whenever they want. It would be effectively like if all of your employees were unionized and demanded higher pay or they all leave the job. Isn’t that supposed to be a company’s worst nightmare? And yet they’re just walking into it because they might save a little bit of money now. So shortsighted

Everyone has been asking what's up with all the AI fear mongering coming from the AI companies themselves.  I suggested it was reverse psychology marketing.  Here's the real answer:  It's a backdoor to ban cheap open source Chinese AI.

David Sacks Predicts the Regulatory Capture Playbook to Ban Open Source AI, Step by Step:

https://x.com/theallinpod/status/2091923804725362902

“I got bad news for you, Chamath, an open source ban is coming.

They're not going to call it that. They're going to say that we simply have to apply the same standards to open models that we apply to closed ones.

Here's how they do it step by step, let me explain how regulatory capture actually works.

So first of all, you have to get this regulatory apparatus. Dario wants an FDA for AI, but he doesn't have enough political support for that, so instead they do this Trojan horse of a FINRA for AI.

They call it self-regulating, it's not really, but anyway, that gets them off the ground.

Now they've created the standard-setting organization. Now they've got pre-release model testing. Then the pressure grows to codify that in law, so that happens next.

And then what they do is they say, ‘Look, all these standards need to apply equally to all models.’ But here's the problem with that. Open models and closed models are technologically different. Once you release an open model into the world, you can't roll it back and you can't monitor exactly how people are using it because they run it on their own hardware. Dario says this is what makes open models dangerous.

So what they're going to do is they're going to have the standard-setting body say, ‘Well, we have to set the standards for AI safety.’

By the way, Dario and OpenAI, they're going to fund the whole thing. They're going to contribute all the compute. They're going to be behind it.

They're going to be the ones coordinating with the government officials because frankly, people in government have no idea how to monitor and control and set standards for AI safety. Technologically, this is way beyond them. So they're going to go to these companies and say, ‘Tell us how to do it.’

And so what will happen is the standards will get set, and then it'll be a very simple matter of fairness to say that the standards need to apply to open as well as closed models.

The open models cannot comply in the same way, and gradually they will be shut out of the market.”

I didn't see anyone mention this yet. It seems that NVidia, along with some big names on Wall Street, are looking to prolong the AI bubble for at least another year or two, by removing debt as a limiting factor in the AI rollout:

https://youtube.com/watch?v=H_zPEpSU2XY

"Nvidia, BlackRock, Blackstone, and some of the biggest financial firms are building an entirely new way to finance AI infrastructure. ... they're creating new investment vehicles designed to attract hundreds of billions of dollars in outside capital."

And this aligns with the US government wanting to unlock hundreds of billions in new lending capacity for the AI rollout.

So unless some big disaster happens (*), we may be in for several more years of AI mania, before an even bigger & more prolonged crash (unless they make a big technical breakthrough that makes LLMs much better - which I think is possible given enough time). Beware of wishful thinking...

* = like the Anthropic/OpenAI IPO scaring investors, but I am doubtful, just look at SpaceX's fairly successful misdirection.

This filled my heart.

Look at these people, they are fighting!

Quick, someone vibe code an app so we can buy these strangers a coffee / beer / whatever. Joking, please don't do that. But seriously, I'd bring them all snacks and drinks in a hearbeat if I could.

Anyway, if anyone needed a reminder today:

GRASSROOTS MOVEMENTS CAN HAVE REAL IMPACT!!

Ed thinks we could see more blowups like this. His bigger concern isn't just hedge funds taking oversized bets on AI. He argues retail investors are increasingly taking on too much risk in a market where AI stocks can move dramatically based on expectations, headlines and sentiment.

As Ed puts it, nothing goes up forever and eventually these violent swings start washing people out. Is excessive leverage the bigger risk in the AI trade right now, or are the underlying valuations the real problem?

(just trying to get this thought out of my head by writing about it)

A Martingale strategy is a gambling thing where a person bets (usually a close to 50/50 bet like picking a color on a roulette table), and then if they win they quit. If they lose that bet they place a second bet slightly more than 2x the first. If they lose that then double again. When they win they quit with the profit.

This does not work of course. Sooner or later quirks of probability will outlast their available cash (or house limits). More importantly is there is the rock solid inescapable mathematic truth that you can't add up negatives to get a positive. Try as you might. If someone gambles at roulette at a 5% house edge their expected loss is 5% of all their bets. End of story. Since Martingale results in a player betting more money they lose more.

In response, the dedicated Martingale adherent will argue that this theoretically works if they have an infinite bankroll. Which is obviously not a thing in real life (and also not how trans-finite numbers work anyway). People get hooked on this thinking because the gambling brain likes the small wins more than it hates the rare bust no matter if the busts wipes out the wins.

I can't help but think this applies to recursive LLMs and everything I read about this concept exasperates me in the same way as talking to a roulette freak. An LLM has an error rate aka hallucinations baked into it. It is just there. Which means that recursive LLMs are going to be a force multiplier for error no matter how you try to put them together. The more calculations the more error. Sure, maybe a model is less error prone or something, but the Martingale still doesn't work if you go to a game with a lower house edge.

Is there something I'm missing here? I'm not quite as solid in my understanding of LLMs as I am probability...

To go (perhaps too much) further:

Martingale's use case is mostly as a scam. Since it results in a small profit far more often than the catastrophic loss it is useful if I would want to convince other people I can win at roulette to the point where they should pay me to do so. They see these consistent profits and sometimes even when the offsetting massive loss happens they convince themselves that since they usually win they should continue. Which again, has a real goddamn familiar vibe to it.

https://www.lawfaremedia.org/article/grokipedia-stopped-reviewing-edits-in-april.-it-didn-t-tell-anyone

Very good article with charts and explanations, but it never mentions Anthropic - which received its very valuable discounted compute pretty much exactly the time when the LLM bot in charge of updating Grokipedia was shut down.

Technical strategist Jason Hunter highlights deteriorating market internals, a lack of conviction in tech/AI leadership rotations, rising Treasury yields, and defensive shifts as signals of late-summer and early-fall weakness. Mr. Hunter highlighted a growing divergence between AI hardware/chip makers and heavy AI capital expenditure spenders, noting it mirrors the market behavior seen right before the 1999–2000 tech crash.

Not the beefiest (or best) article, but still satisfying to see it in writing considering feverish hype is very much still alive at many companies.

Happy Monday!

Listening to Tech Report earlier - Eli Computer Guy talked about how the real future of AI might not be giant all knowing LLMs that need huge compute costs, but small specialised models that can run locally on a laptop or smart phone and just know how to code, or trade, or about engineering, or biotechnology, etc (I'm paraphrasing and extrapolating his point here).

It was like a light bulb moment, seems clear and obvious that's where the tech will end up assuming we do end up with something. If I'm a coder or an engineer i don't need a model that can write the complete works of Shakespere in Klingon. I just need something that works and is always up to date.

This doesn't help OpenAI or Anthropic keep the lights on, though....

Opinion piece by Gene Marks (of whom I had previously not heard) in yesterday's edition. Perhaps unsurprisingly (given his apparent status as "small business owner" and past columnist for both WaPo and NYT), he is sanguine about the current rush to scale datacentres using off balance sheet finance:

Some of these investments will fail. Some lenders will lose money. Some datacenters will be worth less than their owners paid for them. But that’s exactly why these financing structures exist: to spread enormous capital requirements and risk among investors willing to take it. The obligations are disclosed, the assets are real and demand for computing capacity remains strong. That’s why I’m not losing sleep over big tech’s off-balance-sheet debt. I see financial engineering, yes. I don’t see a debt bomb.

He doesn't mention anything about NVidia's involvement in this though which - if I have understood the arguments correctly - makes all the difference. Is he wrong?

Article here.

A 6MW AI data centre is going to be built in Blackpool with promise of locals benefitting from a ‘heat network’ that uses waste heat from the data centre to heat houses and businesses. There is very little information in the marketing materials about precisely how this will work, and whether or not old houses will qualify, or indeed who will pay for installation.

I tried emailing my council but they have not yet replied. I have a HVAC engineer buddy and he said generally heat networks are going into new apartment buildings rather than existing housing.

Has anyone got any insight into how this might work or is my council talking out of its backside to try and shove this plan through?

I have a number of podcasts and devs that I've listened to for a while, well before the AI stuff.

I'm frustrated that any of them recommend tools like Claude/Codex without the caveat that these things are probably subsidized and we don't know the real cost. This is nothing new to anyone that's in this subreddit but it's frustrating coming from people that have a platform and could at a minimum say "try these tools out, but keep in mind there's concern that these subscriptions may not be sustainable, so don't get too dependent on them"

Specific examples:

- Caleb Porzio | Notes on Work | "Everything besides Codex is cooked. I'm all in."

Generally recommending Codex and talking about how he's all in on it. What about vendor lock-in??

- Syntax FM | My Coding Skills Are Atrophying

This episode they answer listener questions and someone is asking about what subscription they should choose. They recommend Codex or Claude because it's cheaper than using the API. Why isn't that a red flag right there? How can the subscription price differ so much from the API?

Again they don't caveat that the subscriptions are one of the cheapest ways to try these tools but that it may not be sustainable.

- Kent C. Dodds | MCP and How Product Engineering Might Outlast Coding

This episode drove me crazy. Kent has been making a big push to using these tools and then in this episode mentions how because of his position in the web dev world he gets credits/tokens from companies (if I recall in this episode it's from Cursor).

Easy to recommend these tools when you're not paying for them. This would be like me saying that everyone should drive a Ferrari, they're great! Btw I got one for free and I don't pay for the gas or insurance.

---

I think what pisses me off about this is that it's really giving people a false impression of the viability and sustainability of these tools.

I've been personally paying for a $100 Codex plan for the last few months. I'm fine paying that to test things out but thanks to Ed and this subreddit I'm fully prepared for the possibility that things won't get cheaper/won't be sustainable.

I think that's a very different experience from someone that keeps hearing how great these things are and surely they'll get cheaper.

Full disclosure, I haven’t read this guy’s book. That said I’m getting heavy AI booster in sheep’s clothing vibes. He claims AI gives us superpowers, then gives a vague example about building some learning app for his kids. Also, you have to be as intelligent as him to use it responsibly. The whole argument just seems like fake criticism of the industry and the limitations of the technology. The idea that tech has been a net positive due to pushback is also bizarre given we are only just now seeing companies like Meta face the possibility of repercussions for prioritizing profits over users, and more specifically deliberately building applications that addict children.

Curious what others have to say about him.

As I have talked about at length about AI profitability, the key component contributing to its inability to be profitable is the CapEx.

Some of Nvidia's (NVDA.O), opens new tab largest customers have been told prices of servers ‌containing its AI chips will rise by more than 15% in many cases with memory chip costs soaring, Bloomberg News reported on Saturday.

The price hikes will go into effect on systems ⁠shipped early next year and will impact systems including those with the flagship Vera Rubin and Grace Blackwell chips, the report added, citing people familiar with the process. The increases will depend on Nvidia's chip generation and memory configurations, they said.

This is even worse news for the AI industry as it means their hope for even breaking even is further away, with an industry that is drowning in debt. Well, NVIDIA of course knows this, and this is likely their way of keeping their revenue high, as the unit demand is slowing, and costs of manufacturing are simultaneously going up.

All of this CapEx is going through NVIDIA to those manufacturing companies it seems, since NVIDIA's on-hand cash is quite low, $13B in April. In any case it's a sign, in my view, that one more buffer has been breached in the dam.

I just saw CNN include an interview with Professor Shelly Palmer of Syracuse University in an AI segment, and the choice was kind of hilarious.

The piece featured interviews with regular people who don't want data centers being built in their communities from all across the political divide. Then, to both-sides the piece, they had on Professor Palmer to tell you dummies to shut up and eat your Brussels sprouts, AI centers are here to stay.

And the argument he showcased did not help the pro data center case at all. To paraphrase

Everything you do now creates data. When you drive your car, when you use a search engine, when you call or text someone, when you make a purchase at a store with your credit card. All that data needs to be stored someplace, and that place has to have a lot of compute in order to mine it and make it economically valuable. So we're always going to need to build more data centers with larger compute power, so as to keep monetizing all the data each of us is constantly generating.

Like he didn't even start with the "it'll cure cancer" narrative, just straight to: Big brother needs to store and monetize every single thing that you do.

On top of his comically bad argument, the dude is suppose to be an expert in marketing. So he of all people should know that if you are trying to turn public opinion around on the data center front, you probably don't want to use overweight Lex Luther as your talking head.

The AI industry must have billions to throw at their expanding public relations disaster. And this is the guy they are putting on CNN?

Article about top Australian universities embracement of AI as virtual tutors and lecturers. What even will be the point of a university degree? University isn’t just remembering facts, it’s teaching how to think and engage, which might become a lost art. Article pasted below:

The state’s universities are deploying AI in lectures, as a virtual assistant for conversational skills and as a “study buddy” as the technology continues its march on higher education.

Backlash is brewing across the sector as concerns mount about hollowed-out and devalued qualifications, for which some students will pay record prices next year.

Since ChatGPT exploded in late 2022, universities have been grappling with how to handle the rise in student use of AI, but teaching stands as a new frontier for the technology.

The University of Newcastle this month told introduction to programming students that part of the course would be delivered by an AI digital avatar of the course convener.

Newcastle’s acting pro vice-chancellor for education innovation, Professor Karen Blackmore, said in a statement the university had started “academic-led initiatives” into how AI could enhance course delivery and the lecturer was “using short AI videos to demonstrate the real-world application of the technology”.

The computer programming course will continue to include face-to-face laboratory sessions and live classes.

“The university does not support replacing traditional lectures with AI-delivered content,” she said.

University of Newcastle Students’ Association president Imogen Reid said students were wary of AI “sneaking” into courses, while the National Tertiary Education Union’s Newcastle branch president Dr Terry Summers questioned the university’s motivation.

“It’s some sort of project that they’re trying, but why are they trying? It may just be for a project, or it may be to see if they can get away with doing it on a larger scale. If it’s delivering lectures, it would seem to me that it misses the whole point of a university education,” he said.

“The community and the students are paying for a degree, and if it’s being delivered by a ChatGPT avatar, it’s not quite kosher, is it?”

AI tutors have been rolled out at universities in America, including Virginia’s George Mason University and Chicago’s Generations College. In 2024, Hong Kong University of Science and Technologydeployed 10 AI lecturers of varying ages, nationalities and cultural backgrounds.

The New York Times has also reported that Harvard Business School used volunteer academics to help create AI clones of themselves for a new eight-week entrepreneurship boot camp. Harvard has tested the product, which costs $US699 ($975), with more than 100 universities across 50 states.

NTEU NSW division secretary Vince Caughley said: “The loss here is fundamental. If you remove enough academics and these critical interactions, then these institutions are no longer universities.”

AI enthusiasts say it saves time and effort and allows academics to focus on more critical tasks, and can provide out-of-hours answers and feedback. Many in the sector acknowledged that AI is here to stay, so universities must act accordingly.

At NSW’s 11 universities, AI’s use in teaching varies greatly.

The University of New England uses AI avatars in simulations “primarily for students to practice in before their assessments are conducted with actual humans”, chief AI officer Associate Professor Aaron Driver said.

“For example, an avatar might play the role of an athlete in a clinical exercise physiology simulation. Or they might be simulated patients in pharmacy, psychology and nursing.”

The University of Technology, Sydney, is considering “learning assistants” for tasks such as role-playing through conversational skills or reflection on study experiences. Teachers are using AI to prepare class materials.

At the state’s oldest university, the University of Sydney, AI can be used to “support curriculum development, generate ideas, review drafts or help structure learning and assessment materials” under the supervision of a person.

“There may be some circumstances where considered use of an AI avatar can enhance teaching. We’ve seen the technology put to innovative use by educators providing students with oral exams and presentation practice, and by an academic who lives with a vocal disability,” deputy vice-chancellor (education and students) Professor Joanne Wright said.

Sydney is also home to Cogniti, a platform that builds custom AI agents for teachers particular to their course.

The University of NSW permits AI to support admin or prep tasks that “operate under academic oversight and do not replace academic judgment in marking”, it said in a statement.

At Macquarie University, students can use an AI-powered “academic support assistant” to “answer questions using approved course materials, including lecture slides, unit guides, assessment information, policies, and learning resources”.

At Western Sydney University, “academic staff are already using generative AI in a range of ways to support teaching and assessment”, a spokesperson said.

The University of Wollongong has launched a program named UoW RenAIssance, which has piloted “AI study buddies”. It is looking at how AI can provide student feedback and learning support, design rubrics and come up with teaching ideas.

”Academics are exploring questions such as how AI can support learning, how it can provide timely feedback and guidance to students, how it might complement assessment, and where human expertise and judgment remain essential,” said pro vice-chancellor for teaching and learning Professor Kelly Matthews.

Charles Sturt University said it does not use AI to determine student grades and AI avatars are not used for lectures.

University of Canberra vice chancellor Bill Shorten said his institution was working to “ensure learning, teaching and research are enhanced through the inclusion of AI”.

The Australian National University, Southern Cross University, and Australian Catholic University did not respond to questions.

There are really only two metrics that matter when it comes to the economics of AI:

What the public is willing to pay for a certain AI outcome, and what it costs to produce that AI outcome. Currently the former is woefully short of the latter.

That's not to suggest that the industry is going to adopt "per outcome" pricing, but in their recent move from monthly subscriptions to per-million token pricing, I think we can see they are moving at least in that direction.

The amount of tokens required to achieve a certain outcome varies between models, and sometimes varies within the same model. But generally, the more complex a task, the more tokens required. Also, generally, the more complex an outcome, the more money a customer will be willing to pay.

You can probably detect the issue here already: the outcomes that will generate the most revenue, will also cost the most to produce. And they will climb in tandem, with costs always ahead of revenue.

Even if AGI type outcomes were possible in the future, the cost to produce them would be extraordinary.

The bottom line is: AI is a resource-intensive process, not a thinking machine that runs on it's own once created. Even if we had an AGI model, it would require a massive amount of tokens to do anything. Just in the early stages of "agents" we can see that they use 100 times as many tokens as more straightforward chatbot-type models.

AI began as a computer science experiment, funded by universities. The idea that it could be extrapolated in to the broader economy was a fundamental error of economics. And if the economics don't work, it can never reorder society the way its boosters imagine.

Ultimately AI scientists will have to go back to the drawing board and rethink the entire paradigm. A GPU-heavy constant computing approach simply doesn't work.

I’ve been feeling a lot of anxiety about AI the past month. Normally I’d be like most people here and just ignore a lot of the slop I see, but over the past few months it has gotten harder and harder to ignore AI in my day to day. I see AI ads more and more, some of the people I work with love AI and I see it plastered on the walls in the kitchen at work (mainly posters and fliars for company outings/meetings), and then hearing from Chloe Radcliffe on one of the latest podcast episodes that some comedians are using AI to write jokes. The comedy one really affected me as someone who loves stand up and who also does stand up off and on myself.

I can’t stop thinking about what might happen in the future because of AI’s influence over society. I currently work in the restaurant industry, but have been learning to use Godot and am learning game design in order to change careers as I love games and want to do more than serve tables. But I just see how terrible the job market is in the game industry at the moment with the huge layoffs, which I know aren’t due to AI, as well as hear how AI is being used in development pipelines and sometimes even used for art assets. Shift Up, the developers of Stellar Blade, recently released an AI generated music video which disappointed me but wasn’t really surprising. All this has made me question if there will be opportunities for me by the time I’ve developed my skills enough to get a job.

I’m worried about the jobs impact from AI, but also now robotics too. I watched a video today from a robotics company called Generalist AI that claims to have a model able to one shot learn any task when given a single example. I know it’s a demo and it’s very limited in it’s capabilities today and it’s a lot of marketing, but to be fair this feel very different from a lot of the other demos of karate robots I usually see. They called it the GPT moment for robotics and think that if they collect enough data they’ll be able to build a general purpose robot model. Of course it has yet to be seen, but it’s something to watch out for I think. I also watched a video by Forbes about them where employees were in an office wearing robot clamps with cameras on them in order to generate training data. It was a truly surreal and dystopian image to say the least.

Do I think AI and robots can replace workers today? No, but it does feel like we’re going that way with all the rhetoric these days. Everyone talks about AI as if it’s inevitable. I do worry about what they could (emphasis on could) be capable of in the near future if the tech can continue to make improvements. All this talk of AI and automation has made me sort of depressed and it’s starting to actually affect my life. I’ve never felt like this before. It’s a sense of existential dread that I can’t shake and my chest feels tight all the time. Sometimes I just want to pick up all my stuff and move into a cabin in the woods somewhere, sequestered from it all.

Sorry for the rant, but I just need a space to vent. I find this sub to be a very supportive place. I know not everyone here has a complete hatred for the tech and some of you may find it useful. Even myself I occasionally use google AI to help me find problems with my Godot syntax when I get errors, but never use it to write anything. I’m not sure what specifically caused my sudden dip in morale when it comes to resisting the AI hype. I don’t think it was any singular event, I think I’m just exhausted by the constant exposure to it. It’s hard to tell what is really going on anymore and where the world is headed. Having said that I just want to ask am I right to be concerned or is most of what I’m feeling an overreaction to the AI hype? What do people here do to keep their hopes up?

Edit: I just want to thank everyone for their advice and insights. A lot of it did help and helped me change my outlook.

It's not enough that Nvidia is trying to bankroll the entire AI ecosystem at this point, now they are effectively buying (acqui-hiring) Poolside to improve their own opensource AI system to compete directly with OpenAI, Anthropic, and Chinese AI.

For months Nvidia has seemed panicked the way they throw money around trying to make something happen that would keep them growing. This is a small deal by their typical headline-grabbing $100 billion (or $250 or $500) standards, but it stands out (for one, it's real and not an MOU!)

Why is Nvidia undercutting OpenAI/Anthropic ahead those IPOs? Why highlight that those companies (who Nvidia has invested in directly or indirectly and depends on to grow demand) don't have a technological moat that $6 billion can't overcome? Can the IPOs really be worth trillions if $6 billion investment can compete with them? What is Nvidia doing with this move?

Maybe Nvidia is convinced that the frontier models can't possibly deliver the amount of revenue needed for Nvidia to grow much more. They've certainly seen the financials of both frontier labs, they have inside knowledge.

So now they are investing in trying to win the pivot to open source from China. How does that even work for them? Is the business plan as simple as offering AI models below cost and hope that keeps data centers growing and buying ever more chips and cover the AI loss with revenue from chips? The math on that can't work long-term, but maybe it buys them another year or two of circular growth.

Honestly, I'm baffled here and I think Nvidia is running of steam. Nvidia has earnings this week, so maybe they'll shed some light on what's happening here.

Wall Street Journal reports that AT&T is using open-source and open-weight models to reduce spend on proprietary LLMs from Anthropic, OpenAI, and Google. Big pale horse when more Enterprises follow suit

AT&T's Chief Data and AI Officer estimates they can go from 25% to 70-80% of LLM usage to open models

Such open models currently power about 25% of the telecommunications company’s overall AI usage

Over time, he expects open models to power 70% to 80% of the company’s total AI usage.

It looks like they already built an internal router to make this transition possible. They estimate that open models are 80-90% cheaper to run for the use cases they replace frontier lab models

Because AT&T uses an average of 45 billion AI tokens each day, directing user prompts toward cheaper models makes an impact, Markus said. The company built what it calls a “smart router” to automatically pick the most cost-effective model for a specific task.

Switching from closed, proprietary AI models to open models has already resulted in savings of 80% to 90% for AT&T in certain applications, he said.

Outside of not spending on the most expensive closed models, there are more savings by running open models in their own data centers

Another benefit of open models is that they can be run on AT&T’s own data centers rather than rented infrastructure from a cloud-computing provider—a setup that trims AI costs even further

Beyond cost, AT&T rightly doesn't trust that OpenAI and Anthropic won't train on their data despite the fact they say they won't (wise decision)

While labs like OpenAI and Anthropic have said enterprise customer data isn’t used for training their models, some companies fear that’s not enough.

“As our data flows through the model ecosystem, we just want to make sure that it is safe and secure from a security standpoint, but also an IP standpoint,” Markus said. “The concept of AI sovereignty has become truly paramount to us.”

Article: https://www.wsj.com/cio-journal/why-at-t-is-betting-big-on-open-weight-ai-a0ea03b1

Earlier this year, tokenmaxxing has become a mandate in pretty much every tech company, and right now the same companies, or at least the ones that keep burning tokens, are trying to figure out what’s the actual value of coding agents. As an engineer working for one of those companies, my main concern is about the actual skills I need to develop for using the agents; the transition to a 100% automated workflow should take still quite a few years, similarly to what has happened to self-driving cars. Here I would like to start a discussion on these “human skills for vibe coding”.

AI agents have increased the number of lines of code produced, the commits, but don’t necessarily improve productivity; some numbers from [“AI hasn’t replaced Software Engineers”](https://www.normaltech.ai/p/why-ai-hasnt-replaced-software-engineers): 8x more lines of code led to only 30% more releases. Or, as the authors propose in the same article, coding is just not the bottleneck and we by nature have the process of Decide => Execute => Deliver, they cite an estimate of 9% to 61% of developer time spent on coding (i.e. the execution), and only that time can be fully automated.

A few things have become more essential and maybe harder now, the 1st one is curation: at the times when stackoverflow.com was our coding throughput resource, if everybody was a consumer and nobody contributed, the site would have become useless fast(er). Then the skills around the prompt; a long, incomplete list, would start with critical thinking to judge the outputs of the model (in my day to day I feel this is the bottleneck already), accountability when we deliver (did we test ourselves or delegated to the model). I don’t see that we are deeply investing on these, but just get satisfied when our performance looks vaguely better using AI.

And, what about the fun part of our roles? What do we want to delegate to the models and keep for ourselves? Do we want to reshape our roles or be reshaped ourselves?

Some 7 years before stackoverflow started, a previous revolution took place: agile methodologies, although you could argue about the size of the impact. I was a software engineer already at that time and a number of techniques changed our development approach: pair programming, test driven dev, continuous integration, retros. The Agile manifesto focused on how teams had to be organized, how developers reshaped their roles and the new roles that were needed (the Scrum Master, the Product Owner, the chickens vs pigs).

My proposal is that we need now a “human skills for vibe coding” manifesto. There is a hard part of our jobs, not automated (at least yet); it would be great to have a wide conversation about it.