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.
The models do occasionally understand complicated features within complex codebases.
If there's a good "human in the loop" pilot, that's all you need to get incredible productivity boosts. Seasoned engineers are discerning, have taste, and can be pragmatic about what the models can do. It's as if they get to play eng manager for a team of juniors, yet still have time to be a full-time IC.
This could be stated much more succinctly using Jobs to be Done (which is referenced in the first few paragraphs):
Your customers don't want to do stuff with AI.
They want to do stuff faster, better, cheaper, and more easily. (JtbD claims you need to be at least 15% better or 15% cheaper than the competition -- so if we're talking "AI", the classical ML or manual human alternative)
If the LLM you're trying to package can't actually solve the problem, obviously no one will buy it because _using AI_ OBVIOUSLY isn't anyone's _job-to-be-done_
> If MMF doesn’t exist today, building a startup around it means betting on model improvements that are on someone else’s roadmap. You don’t control when or whether the capability arrives.
I love this. I think there's a tendency to extrapolate past performance gains into the future, while the primary driver of that (scaling) has proven to be dead. Continued improvements seem to be happening through rapid tech breakthroughs in RL training methodologies and to a lesser degree, architectures.
People should see this as a significant shift. With scaling, the path forward is more certain than what we're seeing now. That means you probably shouldn't build in anticipation of future capabilities, because it's uncertain when they will arrive.
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 compensating, it was enhancing.
The shift was noticeable. We went from "how do we work around this limitation" to "how do we build the best experience on top of this capability." That's MMF in practice.
The timing question is real though. We started building before MMF fully existed for our use case. Some of that early work was throwaway. Some of it became the foundation. Hard to know in advance which is which.
Product-market fit has a prerequisite that most AI founders ignore. Before the market can pull your product, the model must be capable of doing the job. That's Model-Market Fit. When MMF Unlocks, Markets Explode (legal, coding...)
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.
No AI models in 2026 even "understand the whole codebase" lol what is the author even talking about
The author didn't say "whole".
The models do occasionally understand complicated features within complex codebases.
If there's a good "human in the loop" pilot, that's all you need to get incredible productivity boosts. Seasoned engineers are discerning, have taste, and can be pragmatic about what the models can do. It's as if they get to play eng manager for a team of juniors, yet still have time to be a full-time IC.
This could be stated much more succinctly using Jobs to be Done (which is referenced in the first few paragraphs):
Your customers don't want to do stuff with AI.
They want to do stuff faster, better, cheaper, and more easily. (JtbD claims you need to be at least 15% better or 15% cheaper than the competition -- so if we're talking "AI", the classical ML or manual human alternative)
If the LLM you're trying to package can't actually solve the problem, obviously no one will buy it because _using AI_ OBVIOUSLY isn't anyone's _job-to-be-done_
> If MMF doesn’t exist today, building a startup around it means betting on model improvements that are on someone else’s roadmap. You don’t control when or whether the capability arrives.
I love this. I think there's a tendency to extrapolate past performance gains into the future, while the primary driver of that (scaling) has proven to be dead. Continued improvements seem to be happening through rapid tech breakthroughs in RL training methodologies and to a lesser degree, architectures.
People should see this as a significant shift. With scaling, the path forward is more certain than what we're seeing now. That means you probably shouldn't build in anticipation of future capabilities, because it's uncertain when they will arrive.
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 compensating, it was enhancing.
The shift was noticeable. We went from "how do we work around this limitation" to "how do we build the best experience on top of this capability." That's MMF in practice.
The timing question is real though. We started building before MMF fully existed for our use case. Some of that early work was throwaway. Some of it became the foundation. Hard to know in advance which is which.
Product-market fit has a prerequisite that most AI founders ignore. Before the market can pull your product, the model must be capable of doing the job. That's Model-Market Fit. When MMF Unlocks, Markets Explode (legal, coding...)