What if the only moat is domains where it’s hard to judge (non superficial) quality?
Code generation, you don’t see what’s wrong right away, it’s only later in project lifecycle that you pay for it. Writing looks good to skim, is embarrassingly bad once you start reading it.
Some things (slides apparently) you notice right away how crappy they are.
I don’t think it’s just better training data, I think LLMs apply largely the same kind of zeal to different tasks. It’s the places where coherent nonsense ends up being acceptable.
I’m actually a big LLM proponent and see a bright future, but believe a critical assessment of how they work and what they do is important.
Yes, during the 2000's there was the "mashup" fads. People creating companies around mashing data from one service to another. Like putting Craigslist listings on a Google Map.
And guess what, all those mashup companies didn't last a couple of years. Because they didn't have a direct access to data.
This is heavily context dependent... There are plenty of situations where everyone knows the relevant factors, it's who has possession of land, resources, people, etc.
> Which brands do people trust? - Which people do people of power trust?
These are often at odds with each other. So many times engineers (people) prefer the tool that actually does the job, but the PMs (people of power) prefer shiny tools that are the "best practice" in the industry.
Example: Claude Code is great and I use it with Codex models, but people of power would rather use "Codex with ChatGPT Pro subscription" or "CC with Claude subscription" because those are what their colleagues have chosen.
I feel like algorithmic/architectural breakthroughs are still the area that will show the most wins. The thing is that insights/breakthroughs of that sort that tend to be highly portable. As Meta showed, you can just pay people 10 million to come tell you what they're doing over there at that other place.
Hasn't this been proven true, many times now? Just look at the difference between ChatGPT 3 and 3.5, for example (which used the same dataset). That, and all the top performing models have large gains from thinking, using the exact same weights.
And, all the new research around self learning architectures has nothing to do with the datasets.
Companies always try to make it seem like data is valuable. Attention is valuable. With attention, you get the data for free. What they monetize is attention. Data is a small part to optimize the sale of ads but attention is the important commodity.
I feel like the the data to drive the really interesting capabilities (biological, chemical, material, etc, etc, etc) is not going to come in large part from end users.
It's the other way around. You gather user data so that you can better capture the user's attention. Attention is the valuable resource here: with attention you can shift opinions, alter behaviors, establish norms. Attention is influence.
Corruption is the only moat. Oligarchs can buy anything and funnel attention and money into it, creating financial success for shareholders despite poor leadership, zero social responsibility, suboptimal ideas and execution (see: Tesla)
Just commit fraud repeatedly while owning the people who run DoJ, easy peasy, no amount of attention or cash flow can displace that.
Code generation, you don’t see what’s wrong right away, it’s only later in project lifecycle that you pay for it. Writing looks good to skim, is embarrassingly bad once you start reading it.
Some things (slides apparently) you notice right away how crappy they are.
I don’t think it’s just better training data, I think LLMs apply largely the same kind of zeal to different tasks. It’s the places where coherent nonsense ends up being acceptable.
I’m actually a big LLM proponent and see a bright future, but believe a critical assessment of how they work and what they do is important.
Also as foundation models improve, today's "hard to solve" problems become tomorrow's "easy to solve" problems
And guess what, all those mashup companies didn't last a couple of years. Because they didn't have a direct access to data.
- Which brands do people trust? - Which people do people of power trust?
You can have all the information in the world but if no one listens to you then it’s worthless.
These are often at odds with each other. So many times engineers (people) prefer the tool that actually does the job, but the PMs (people of power) prefer shiny tools that are the "best practice" in the industry.
Example: Claude Code is great and I use it with Codex models, but people of power would rather use "Codex with ChatGPT Pro subscription" or "CC with Claude subscription" because those are what their colleagues have chosen.
inb4 "then why do Meta's models still suck?"
And, all the new research around self learning architectures has nothing to do with the datasets.
Companies always try to make it seem like data is valuable. Attention is valuable. With attention, you get the data for free. What they monetize is attention. Data is a small part to optimize the sale of ads but attention is the important commodity.
Why else are celebrities so well paid?
I feel like the the data to drive the really interesting capabilities (biological, chemical, material, etc, etc, etc) is not going to come in large part from end users.
Just commit fraud repeatedly while owning the people who run DoJ, easy peasy, no amount of attention or cash flow can displace that.