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Published June 16, 2026

LLM's $3 Trillion Dollar Problem

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AI Summary

The creator argues that frontier AI labs and companies investing in Large Language Models (LLMs) face a systemic path to unprofitability despite massive investment, suggesting a fundamental disconnect between the cost of building these models and their actual monetization potential.

Key Takeaways

  • **Profitability Gap:** The massive capital expenditure required to train and maintain frontier LLMs outweighs the current revenue streams generated from them.
  • **Investment Risk:** The '3 trillion dollar problem' refers to the extreme amount of capital being poured into AI infrastructure without a clear, scalable business model to recoup those costs.

Description

Book a call: https://calendly.com/itshassanaziz/discuss-a-project ==== ==== ==== None of the frontier labs and companies investing in building LLM models are going to make a profit off of them. In this video I will explain why... ==== ==== ==== LINKS Website: https://www.hassandev.me Portfolio: https://www.hassandev.me/work YouTube: https://www.youtube.com/@itshassanaziz?sub_confirmation=1 My Book: https://www.hassandev.me/designing-websites X / Twitter: https://x.com/nothassanaziz

Transcript

Auto-generated transcript
I'm starting to feel like LLMs are now a commodity. If I asked you what the best model is right now, the quote-unquote best model, most of you would probably give me very different answers. There's a lot of people in the audience who think Codex models are the best, and there's also a lot of people who think Flot models are the best. Heck, there's also a lot of people who use Gemini for everything and think that's the best. The differences between these models are becoming harder and harder to see, right? And not just across companies, but also in the same company or the same LLM provider, there's no meaningful, clear improvement between Opus 4.6, 4.7, 4.8, right? Like if you just ignore all the hype of every single new model released on Twitter and everything, and you actually use these things in real-world tasks, you will notice none of them are actually improving at a very fast rate, right? There's no clear noticeable improvement between any of these new models at all. And you might think you can just look at the benchmarks for every single model, right? Because benchmarks are just raw numbers and don't have any subjective bias in them, right? You might think that, but the benchmarks don't show real world usage. But you can look at all of these numbers and maybe decide which model is right for you. But these benchmarks aren't going to show real world usage, right? One, because a lot of companies are just trying to benchmark max, right? They're trying to do some benchmark maxing where they're trying to purposely create models that get higher and higher scores on benchmarks instead of real world task performance right And the second reason why I don think these benchmarks really matter all that much is because the hardness around the models is so much more important than the model itself right And we'll talk more about this in just a second. So for 99% of tasks that you might want to automate or whatever, as long as you're using any reasonably frontier model, right, You're going to see no difference between the outputs of all of these different models, right? As long as you're building a good harness around it. Hey, just give it another six months and nobody's even going to care what model you're using. Nobody's going to care about it because it's going to become a commodity, right? And a harness around the model, that's going to become more important than the model itself. And when I say harness, I mean things like the user experience around the harness, the context provided to the model and the tools available to the model and these things are becoming more important than the actual model itself because if you nail down these three things you can get a hundred times better output from even a quote-unquote worse model as compared to the quote-unquote best model with none of these things available right since models are becoming harder and harder to distinguish right and it can just be swapped for another model very easily now right the user experience and the harness around the model just became much much much more important like just look at how this look at how cursor has so many more users than say anti-gravity right even though in both of these IDs in both of these editors you can have cloud models can have codecs models all of these things right But cursor as a harness as an AI agentic code editor is so much better right That's what I mean when I say the harness is more important than the actual model itself. Like, I don't know if you guys saw the new anti-gravity 2.0, you know, app, but it's basically just a complete ripoff of codex, right? And codex still gets many more users. So really, even though the models don't really seem to matter that much anymore, the hardness around the model is just as much important, right? The hardness around the model is so much more important, right? Even open source models like, I don't know, Gamma 4, I've been using this a lot. And DeepSea, again, another popular model. These models are so good that you can literally use a tiny 12 billion parameter model for so many different complex tasks, right? As long as you provide it with the right tools and the right context that the model needs to get the job done, right? Exactly what we discussed here with the harness. And a lot of people are getting great results with DeepSea, right? Like you can do the same tasks as the latest codecs and plot models at a fraction of the cost, right? And for most business tasks, you don't even need the latest frontier models, right? You don't need these Opus 5.6 or whatever, right? It's a waste to use such expensive models for the common business tasks that most companies are trying to automate. You can achieve the same result at a fraction of the cost by using these open source models. So really I see a future in the next six months where nobody really gives a fuck what model you using the specific model that you using is going to become less and less relevant And what going to become more important is the hardness around the model the tools available to the model, the context provided to the model. So quick pro tip, if you're someone who builds AI agents, right, let me give you some quick tips over here, you need to start focusing more on the hardness around the models, as opposed to the model itself, start paying more attention to harness engineering. OpenAI has a really awesome article on their blog about this exact topic. I'll try to link it in the description. Go read that as well. And just to give you an example, let's say you're building an AI power app over here, right? You need to give it the tools it needs to take actions on user's behalf, right? So the users have to do less work themselves. Also make it so it can automatically pull the context and the information it needs from the app, right? To give personalized relevant outputs without the use of having to manually provide that context himself, right? The harness or the app should be able to pull that context automatically, right? And just a quick sales pitch before I end the video. If you're a business owner and you don't understand what I'm talking about here, right? And would like to integrate AI into your company, look no further than this channel. This is the work I do. I'm a freelance software developer and I build AI workflows and agents as well as websites and mobile apps for businesses just like yours. And if that's you, check out the top link in the description. You can book a call with me right now and let's get you integrated with AI. And that's all for this one. Thanks for watching and I'll see you in the next one.

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