Microsoft Paint's new AI image generator builds on your brushstrokes

It's because it gives them an excuse to hoover up as much data about you as possible and send it home for "safety" reasons. They can sell AI compute as a service to businesses, while giving the spyware version to consumers for free.

There's absolutely no excuse for monitoring an art programs inputs and sending them to Microsoft. What next, are they going to put AI into notepad and then monitor that to keep you from writing dangerous ideas?

If that was true, there would be a free laptop by now.

At this point I see training costs as basic research and R&D, partially and not entirely oriented at -making training scale- and cost optimization for next generations of foundational and fine tuned models. For example Falcon is literally basic research funding.

That’s relevant because if they stopped with say Claude Opus and 4o and did no more training they would be handsomely profitable indefinitely because the product is that useful. However it’s an arms race and the limit of effectiveness hasn’t been reached as training costs fall dramatically. So it’s not the right time to stop because whoever stops first loses everything to whoever doesn’t stop. More they’re feeding off each other in a virtuous cycle.

Once diminishing returns kill the race whoever is left in the race will have a handsome business indefinitely as the moat to build such a return diminished model is probably enormous. But if inference optimizations keep going as they’re going it’ll be crazy cheap to operate.

The second layer market of tools that constrain, optimize, and effectively apply the models in effective ways will be the first to really turn a profit. Many hype wagon AI companies are already being snapped up by larger companies to bootstrap their internal work.

I don’t feel like you really have much experience using LLMs in business. However an example of where they’re very powerful is in summarization. For instance we have a pretty complex customer support model for our fraud and other cases with various disparate data sets including prior cases, related possible fraudsters identified via our fraud models, etc. We built a copilot LLM multi agent system that has access to various functions as sub agents that are prompted and context aware of how to summarize their specified data sets. They also have the ability to render widgets on demand or if their context implies it’s relevant. This allows quite a lot of complex high cognitive load information to be distilled rapidly and the investigators to interrogate the copilot on a case. As the copilot develops “answers” as a summary it dynamically renders an appropriate contextual dashboard with the relevant visualization.

By structuring the application as a multi agent model we can constrain the LLM to pretty well specified tasks with fine tunings and very specific contexts for their specific task. This almost entirely eliminates hallucination and forgetfulness. Even if it were to do so the actual ground truth is visualized for the investigator.

Prior systems either dumped massive amounts of cognitive load in the investigators face or took man years of effort to create a specific workflow, and in an adversarial dynamic space like fraud you need a much more dynamic approach to different types of new attacks.

We aren’t replacing anyone. That’s not our goal. In fact we grew our investigator footprint because both our precision and recall have grown dramatically making our losses much less. We hire more skilled investigators and greater number to address more suspected cases faster and better.

Listen. When John Henry battled the steam drill he did win, but it killed him. Go to any modern bore site and you won’t see less people working on the tunnel but more people - people who aren’t there for their strong back and ability to swing a pick but because they’re highly trained experts. They’re just building more complex tunnels that don’t collapse and don’t lose dozens of workers per dig.

This form of automation is no different in my experience so far.

So, if all you can see is SEO and grift, it might be a lack of imagination and experience on your part and some magical AI thinking sprinkled in. All your points about LLMs failures are true but they also all have solutions that don’t require slot machines as you say or imply it’s all a scam. They’re a tool like any other and they require handling in specific ways to be most effective. Even if chatgpt is a pretty unconstrained interface and that leads to issues doesn’t mean that’s the only way to use the tech.

Use of LLMs to generate software is dumb. Although a protio, LLMs are actually pretty remarkable at generating Cucumber tests as Gherkin is a natural language grammar that plays into their native strength better than producing computer language grammars. This is useful if say you have business people or whatever writing effectiveness or whatever testing where they can provide a specification of policy and a well prompted LLM can generate pretty exhaustive cucumber tests (which can be pretty redundant and formulaic when asserting positive and negative cases exhaustively) which can then be revised by hand as needed. Since they’re natural language as well the business people tend to be pretty good at debugging the tests up front and with a large set of cucumber tests written by hand you’ll see tons of errors anyways. The LLM tests tend to be much much higher quality than the human written ones.