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Model Market Fit

nicolasbustamante.com

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Re: Model Market Fit

#11

Post by dvrp »

echelon wrote: post #9
minimaltom wrote: post #5

Some of what OP is saying generalizes to the concept of being "too early" - if you are early, your engineering / innovation spend is used to discover that at-the-time reasonable ideas don't work, or don't work with the current appetite, whereas later entrants can skip this exploration and start with a simple copycat. My (business-school) partner reminds me that first movers are seldom winners.

That perfectly surmised my experience. I've been "too early" far too frequently. Before ElevenLabs, I built an AI TTS website that got 6.5 million monthly users at peak [1]. PewDiePie and various musicians were using it. It didn't have zero shot or fine tuning, so it got wiped out pretty easily when ElevenLabs arrived. Before Image-to-Video models got good and popular, I built a ridiculous 3D nonlinear video editor […

Hey, if you are ever looking for a job at Krea, just let me know!

Re: Model Market Fit

#12

Post by storystarling »

augusteo wrote: post #6

This maps to what we've seen building AI at work. When we started building a voice agent for inbound calls, the models were close but not quite there. We spent months compensating for gaps: latency, barge-in handling, understanding messy phone audio. A lot of that was engineering around model limitations. Then the models got better. Fast. Latency dropped. Understanding improved. Suddenly the human-in-the-loop wasn't…

The danger is that we bridge that gap with backend complexity. I spent weeks over-engineering a chain of evaluators and retries to get reliable outputs from cheaper models, thinking I was optimizing margins.

Then a smarter model dropped that handled the nuance zero-shot. That sophisticated orchestration layer immediately became technical debt—slower and harder to maintain than just swapping the API endpoint.

Re: Model Market Fit

#13

Post by dbuxton »

The flip side of this is that if model capabilities are extremely strong such that they are able to saturate the benchmarks, the differentiation and defensibility of a wrapper solution built on top are significantly reduced.

IANAL but e.g. Claude Cowork is already good enough that it's hard to see how the legal tech startups are going to differentiate except around access controls, visual presentation of workflows, etc. And that's in a heavily enterprise/compliance-aware/security-focused context.

Don't get me wrong, that's still a big "except" - big enough for massive companies to be built. Personally the anxiety of being so close to being squashed by the foundation models would make me unhappy as an entrepreneur but looking at the market it seems like many people have a higher risk tolerance.

Re: Model Market Fit

#15

Post by TeMPOraL »

dbuxton wrote: post #13

The flip side of this is that if model capabilities are extremely strong such that they are able to saturate the benchmarks, the differentiation and defensibility of a wrapper solution built on top are significantly reduced. IANAL but e.g. Claude Cowork is already good enough that it's hard to see how the legal tech startups are going to differentiate except around access controls, visual presentation of workflows, e…

I keep saying (need to coin a name for this at some point): LLMs, by their general-purpose nature, subsume software products.

Whatever domain-specific capability some software product[0] has, if it's useful to users now, it's more useful if turned into a tool an outside LLM can wield[1]. Users don't care about software products - on the contrary, the product is what stands between the user and what they actually want. If they can afford to delegate using the product to someone else, they do - whether it's to a friend, an external contractor, or an employee hired for that purpose.

This is the value offering LMMs provide to the user: general delegation. If an LLM can operate some software for you, it frees you to focus on problems you need solved. If it can operate multiple software tools, the benefit to you grows superlinearly, as the LLM can use multiple tools to solve problems not addressed individually by any of them. Problems there are no dedicated tools for at all.

This is a big problem for the software industry as it is, because we're relying on the concept of software product as a monetizable unit - some UI layer that defines what can and cannot be done, that we can charge for, and then double-dip with upsells and dark patterns, as UIs are the perfect marketing platforms. General-purpose LLMs sitting on the outside, they break all that by erasing the "product" boundary - and what's worse (for the industry, it's great for me as the user!), as the multi-modal capabilities get better, there's nothing one can do to stop it - even if you purposefully block and obscure any (classically) machine-friendly endpoints, the LLM will just take the hard way, and operate the UI the same way human does.

There's no way I see this won't upend the entire industry in the next couple of years.

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[0] - This includes both products you buy, and products you rent, aka. SaaS.

[1] - As opposed to "inside LLMs", AKA. AI-in-product integrations everyone's doing these days, in a desperate attempt to stay relevant. Outside vs. inside LLM is a difference between your personal assistant and the assistant at some company's reception desk.

Re: Model Market Fit

#16

Post by barrenko »

storystarling wrote: post #12
augusteo wrote: post #6

This maps to what we've seen building AI at work. When we started building a voice agent for inbound calls, the models were close but not quite there. We spent months compensating for gaps: latency, barge-in handling, understanding messy phone audio. A lot of that was engineering around model limitations. Then the models got better. Fast. Latency dropped. Understanding improved. Suddenly the human-in-the-loop wasn't…

The danger is that we bridge that gap with backend complexity. I spent weeks over-engineering a chain of evaluators and retries to get reliable outputs from cheaper models, thinking I was optimizing margins. Then a smarter model dropped that handled the nuance zero-shot. That sophisticated orchestration layer immediately became technical debt—slower and harder to maintain than just swapping the API endpoint.

Whatever we do now to "steer" the model to do the job, my 5 cents, it will all get sucked into the model itself; skate where the puck is going as they say, and relentlessly focus on user experience and the overall product, that's how you get something like Granola.
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