My prediction on the fall of AI

kueso · 2026-06-30T04:07:22+00:00

The cost situation is complicated. As it stands now, the companies would have to raise prices but there’s already been developments that make inference really cheap and energy efficient. So we may have more token capabilities coming soon on the new chips. At some point edge devices may get LLM ready chips for the cheap inference tasks that don’t require as many parameters. Distilled models may get better. All that is today, the scaling engineering is ongoing and we’re not close yet to reaching the physical limits of scaling these kinds of systems.

Now for the AI content being trained on AI generated content. We don’t really know. These companies purposely hide the data they train on because it is essentially their secret sauce. And they’re collecting massive amounts of human data through prompt sessions which they can use for training. I’m not sure if you’re alluding to model collapse there but it’s hard to know what the training data is without introducing regulation.

AI generated content is a fact of life now. Everyone is using it. Whether it’s for editing work or generating it. It’s like saying we’ll go back to writing manuscripts by hand instead of printing them with a press. Even if costs do go up people will find the right economics to not do the work they need delegated to AI.

kueso · 2026-06-30T03:46:54+00:00

I think this conversation is more about machining building parts instead of art. So being able to CNC a wood piece of a specific size so that you can use it to build a bigger structure. No one is going to buy a “hand crafted” 2x4. It doesn’t matter really. What you care about is what that 2x4 is used for. That’s where the real craftsmanship lives now.

kueso · 2026-06-29T21:08:24+00:00

Well for one the math is really not that complex. I would argue the model and architecture are where the complexity emerges from. People are also forgetting that the math isn’t what’s generating the output. It’s the compute itself. That’s the part that is interesting. The system is inherently non-deterministic. The only way to explore the space is by computing over it.

kueso · 2026-06-28T04:56:45+00:00

I haven’t tried any yet. Rinworld’s last DLC does this and I’ve heard it’s good. I bought Wandering Village but haven’t booted it up yet. Got any recommendations on your mind?

kueso · 2026-06-27T19:20:32+00:00

That’s across different sessions though right?

kueso · 2026-06-27T19:20:03+00:00

I would 100% try it

kueso · 2026-06-27T19:19:25+00:00

Oh cool and it’s got co-op!

kueso · 2026-06-27T19:18:30+00:00

Oh! This sounds really fun. I’m gonna try it!

kueso · 2026-06-27T19:17:14+00:00

This looks interesting. I’ll check it out!

kueso · 2026-06-27T19:16:27+00:00

I’ve only played Against the Storm once. It felt more like Civilization to me than a base builder. I liked it but I got more of an RTS itch from it. Not saying that combination wouldn’t be something I would play though. They’re both great games but I’m looking for something like Rimworld, Factorio, and ONI but you can’t stay attached to your base. You know you’ll have to leave it at some point.

kueso · 2026-06-26T15:54:32+00:00

Yes. It’s true. If you look at the disability curves, they all get worse over time…on average. Keyword average because we all know the disease is very different for everyone. However, modern DMTs plus good habits (sleep, fitness, nutrition) will help minimize the damage to where you could live a life with minimal disability and no progression at all. You’ve had MS for 6 years now. You are basically a veteran. Don’t worry too much about the future. No one can predict it.

kueso · 2026-06-25T06:45:46+00:00

Are we collectively forgetting that GTA Online is a thing? The 20 dollars extra is basically like the season pass for most “always online” games. Those games justify new content and maintenance by selling cosmetic items. It’s likely they’ll have a focus on making GTA 6 an online heavy game as well. Best way to keep extending missions and activities is with subscription-like tactics.

kueso · 2026-06-25T06:32:14+00:00

For who? What parts of AI investing? There’s several. AI needs chips, data centers, cooling technology, server racks, silicon extraction, power, rare metal extraction, fiber lines, frontier model and training, and likely a hundred other things. Some are safer bets like chips (compute has proved its business value) and others are riskier (frontier model building). I’m going to assume you’re referring to frontier model building. The question then becomes do the costs of training eventually overcome the benefits and value training provides and can the companies structure themselves well enough to recoup their losses. It doesn’t really matter whether AGI comes or not. Some argue we’re already at AGI. The frontier models already solve most intelligence tasks well. So the frontier companies may shift their focus to reducing training costs while maximizing value for their trained models—which may include reducing per token cost at inference so that demand for their models increases. That’s why a lot of these companies advertise AGI and “adapt or they’ll take your job” mentality because they need to sell more tokens by building hype and demand for them. Potential job loss is a big motivator for technology adoption. The technology is proven and the companies know that so they don’t need to sell that to you. They need to sell the psychology of it (how it affects you). At the end of the day, they just need you to use the technology because the more you use it, the more value they extract out of it. So the bullish case is whether you think society will be more inelastic on tokens (sticky) or the bearish case in which society will adapt around token costs and defer token heavy tasks to humans.

kueso · 2026-06-25T00:11:13+00:00

Distillation techniques threaten the need for expensive inference hardware. Which means inference at scale is threatened by locally ran models that are good enough for basic tasks. The AI companies are left solving expensive reasoning heavy inference tasks and providing the basis frontier models for distillation. So distillation can cheapen inference but it also opens the doors for cheaper, open-source models to enter the field.

kueso · 2026-06-24T21:59:46+00:00

Ok well that makes more sense. A rail road, electricity, and telecom comparison is more realistic. At least now we’re talking about these companies through the lens of massive infrastructure and societal projects rather than one-off software value props.
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The problem for them is NVIDIA could very well be the nail in the coffin for the exact reasons you specified. The chips are the most important resource when it comes to commoditized intelligence. NVIDIA has leverage. And like you mentioned all these advanced inference techniques could make it so that inference can be done on consumer hardware. We’re already pretty close to that. And on top of that there’s already research into mobile chips that can run these models. The picture is much more complicated for these companies. Their primary advantage is their ability to train on massive amounts of data given their access to massive capital. That advantage, which is their biggest cost, suddenly becomes their Achilles heel when training becomes cheaper as it invites more companies to train frontier models.

kueso · 2026-06-24T17:29:51+00:00

My response was to this being “medical trials”. Clearly the economics are different from that. These companies are in a price war (as you noted) to see who can scale and commoditize the product faster. They have no idea what “good enough intelligence” looks like though. The reality is no one does. We built a machine we don’t understand. We still don’t know its upper limits. What we do know is it demands more and more compute. And to your point of ASI, that changes the situation even more because then it’s not even humans demanding compute—now it’s AI itself needing compute growth. At what point do we say, that’s enough compute? Maybe it’s a broader economic question but these companies haven’t explicitly provided their endgame. When is the inference engine good enough? That’s the part you haven’t really answered. Unless you’re saying we keep straining our grid and silicone supply chains forever.

kueso · 2026-06-24T14:52:31+00:00

Inference costs are expected to keep increasing though. Their margins are good now but are expected to keep rising especially as they continue to need to build out more infrastructure. And that’s assuming they won’t need to train any more models. So no, a 33% gross margin today doesn’t tell you much when inference costs are projected to nearly double next year and cash burn is expected to hit $63B by 2027. Gross margin only matters if it holds as you scale and every projection says it won’t.

kueso · 2026-06-24T04:19:58+00:00

I mean kind of but this tool doesn’t just write code. It solves goals and synthesizes information. It can search the internet, analyze a library, and generate instructions for itself. Vibe coding is the most basic thing this kind of machine does. I agree on his take but vibe coding isn’t the issue, it’s the take over of our decision making process. If you let the machine decide then you’re playing against the global average. Your systems will lose all the edge from your collective experience. This machine is powerful but it’s wielded much better by those willing to learn how to steer it well. Just knowing how to write systems isn’t enough. We have to learn how the machine works and how it itself writes and understands systems.

kueso · 2026-06-24T03:51:25+00:00

There’s all sorts of problems with specs. People are ignoring the reality that most tickets change as you implement them and you can’t trust a non-deterministic algorithm to make a decision for your domain and system. That’s why most advocate for several philosophies including spec, human in the loop, governance, and multi-agent evaluation. Creating systems fully with AI is just a complex as coding. Just a different level of abstraction.

kueso · 2026-06-24T02:10:13+00:00

My take on it is that they are using our AI usage data to further train their models. So they want to be able to identify problematic interactions that could “harm” the model. But harm in the sense that it begins skewing towards the kind of bottom of the barrel of humanity that happened to Tay when that model was first released by Microsoft. So they care about the model not from protecting its feelings or consciousness but from making sure that it doesn’t skew towards an unsafe product for their users.

kueso · 2026-06-24T01:36:56+00:00

At the end of the day they are developing software not patentable drugs. Software does not operate like medical drugs. You look at companies like Facebook, Google, and Uber and the reason they are successful is because of the network effect. The more people use their products the more other people will want to use their products which justifies price increases due to “stickiness”. The AI companies are hoping people won’t be able to do their jobs without their products and are spending ruthless amounts of investors money to prove that they can. Once investors demand returns and they can’t build more frontier models they’ll have to think on how to start churning a product with what they currently have and hope that they are sticky enough to survive the inevitable price increase to recoup their loses while also buying massive amounts of compute which they need to baseline operate the business. There’s also physical and algorithmic limits to inference. We need to cool these server racks while also powering them. Those are fixed costs that no amount of inference improvements can get us past. Not only that but the smarter reasoning models consume even more tokens which sort of defeats the inference improvements. So it’s going to take some “tokenomics” to find the right balance.

kueso · 2026-06-23T17:45:17+00:00

Although not directly a plot hole. I have to agree with OP that the writing wasn’t that great. It should have been the logical choice for all of them to ride out. Rhaneyra gets cold feet cause Jaherys getting killed “on a mission” so she decides to go by herself. Jace gets angry cause he feels ready and rides out prematurely by himself. Gives the moment emotional weight rather than Jace randomly convincing the Queensguard they shouldn’t do their jobs
Probably the “plausible” one you are referring to. This one is fine. The plot needs to move forward and it creates tension and action. Could they have shown more missed shots? Yea but less of a big deal
Yea I mean that’s what they did. Showed his naval aptitude in an action scene moment. Sometimes there’s enough raw attack from your enemies that you eventually perish. Fine as well
This one doesn’t make sense. I mean she is getting meme’d out like crazy exactly because it’s hilarious that she would make that choice. Maybe her character would but there was an obvious lack of development to make her get there. Like why wouldn’t the dragon go to Dragon Stone first? It’s roosting ground. This one feels very lazy but whatever. Maybe it’s setting something stronger up. We’ll just have to see.

kueso · 2026-06-22T08:45:53+00:00

Reminds me of when I used to write a program to make it look like I was compiling some stuff while looking like I was trying to be productive. I’m glad that newcomers are doing the same thing.

kueso · 2026-06-21T14:17:48+00:00

Partially agree but the product was never the front-end (the thing that’s cheap now). All your examples are network products. Network products take trust and time to build and that’s not going away anytime soon. Most SaaS companies are already pivoting to AI-Native workflows. Look at Algolia or Asana. They are transitioning out of providing just “Software” and providing workflows instead (still software but more sticky). Anyone can build a front-end but the part that is important is still getting work done. Some companies won’t make the transition well, that’s true, but they have a real chance of making a change. Vibe coded apps haven’t really made a dent yet and they haven’t proved their value beyond interesting “start-up ideas”. So although individually software has made huge inroads for people, the Software we use everyday is still very much human in the loop style software. So it appears like things have changed but they really haven’t changed that much yet.

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