Are there any examples of products containing ai inference that are successful? Products that are beyond just direct access to frontier LLM's, I mean.
About design and the product side of maintainance, what i have felt is that, deep down inside, the user does know exactly what they need. If they can learn to communicate that in a way another person or agent can understand, then design and maintainance will be fine. Thats a big if, but if the economics align for people to benefit from developing that skill, maybe they do.
Products are only viable when you do a lot of work to solve a problem, which is then shared by many potential customers. It makes sense to amortize the high costs of solving the problem across all the different customers to reap economies of scale. Businesses then pay for product design, hosting, and maintenance because those costs can also be amortized, and they are cheaper than a bespoke solution for each customer. But if the bespoke solution becomes cheaper than that, because it's generated by an LLM that doesn't need to be paid a living wage, there's no reason to have the product in the first place. Just solve your damn problem and let other people solve theirs.
This is an underrated factor in the market structure of the AI bubble going on now. Anecdotally, we're not seeing new AI-based products other than the foundational models and coding assistants gain traction. Why? Because AI makes it so easy to customize the solution to your particular needs that everybody is just solving their particular needs directly. It's the opposite of the Internet boom, where the network greatly expanded the potential market, reduced the cost of reaching them, and made it economical to spend large amounts of money building a software product that had a TAM of billions. The AI boom instead enables extremely cheap customization, which shrinks the market to a single customer who uses AI to directly solve their problem rather than building a product that's generally applicable.
Whether this could happen is a critical question.
What ive found currently is that its not possible because the non-determinism of the model means you cant trust it, and these tasks are such that people expect determinism. Even if they dont then they dont tolerate the kind of stupid mistakes these models make
What could happen is that the model becomes good enough to be near deterministic, or as deterministic as humans. I dont know if the current next token prediction foundation is sufficient for that. But who knows, maybe it is
For a lot of tasks, classical deterministic computer programming is better than an LLM. It's just that LLMs are very good at classical deterministic computer programming now.
How am I going to use ChatGPT instead of, say, Figma or an office suite with my coworkers?? It's no use for it to spin up a clone for me if my coworkers can't collaborate. And if they can, what it will serve is a product.
How can it be cheaper for every company to re-invent the wheel?
> Products are only viable when you do a lot of work to solve a problem, which is then shared by many potential customers.
Yes, that's what businesses pay services for.
Is it as good as a professional could've done with Photoshop? No. But it's about 90-95% of the way there, which is good enough that neither her nor any customers would care.
A great deal of business is precisely this, tasks that need to be done but where you only care about "good enough" solutions. After all, a key principle of business is you don't outsource your competitive advantages. You pay people for the table stakes, the things that everybody needs but that they only need to be "good enough". LLMs can generate "good enough" facsimiles of a wide variety of fields.
Remember that in the mid-00s, this is what YouTube and the social web was. Folks doing little, productive economic activity, usually unpaid, which can now reach an audience through technology. And now the top career aspiration in young kids is "Be a social media influencer." That's how disruptive innovations start: with little transactions that aren't considered business at all, which then grow until they subsume the economy that everybody knows of.
Companies not matching their prices to current reality, mostly.
This is the part I'm least convinced by.
Corporates used to develop their own tools and they moved away from it for many reasons. Cost of development was only one of those reasons.