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We are going to quickly reach the point where most of these AI startups (which do nothing but provide thin wrappers on top of public LLMs) aren't going to be ne

by paxys 3y ago

We are going to quickly reach the point where most of these AI startups (which do nothing but provide thin wrappers on top of public LLMs) aren't going to be needed at all. The differentiation will need to come from the value of the end product put in front of customers, not the AI backend.

layble 3y ago
Sure, in the same way SaaS companies are just thin wrappers on top of databases and the open web.
- imjonse 3y ago
  You will find that a disproportionately large amount of work and innovation in an AI product is in the backing model (GPT, Mixtral, etc.). While there's a huge amount of work in databases and the open web, SaaS products typically add a lot more than a thin API layer and a shiny website (well some do but you know what I mean)
  - tomrod 3y ago
    I'd argue the comment before you is describing accessibility, features, and services -- yes, the core component has a wrapper, but that wrapper differentiates the use.
wongarsu 3y ago
The same happened to image recognition. We have great algorithms for many years now. You can't make a company out of having the best image recognition algorithm, but you absolutely can make a company out of a device that spots defects in the paintjob in a car factory, or that spots concrete cracks in the tunnel segments used by a tunnel boring machine, or by building a wildlife camera that counts wildlife and exports that to a central website. All of them just fine-tune existing algorithms, but the value delivered is vastly different. Or you can continue selling shovels. Still lots of expensive labeling services out there, to stay in the image-recognition parallel
- pradn 3y ago
  The key thing is AI models are services not products. The real world changes, so you have to change your model. Same goes for new training data (examples, yes/no labels, feedback from production use), updating biases (compliance, changing societal mores). And running models in a highly-available way is also expertise. Not every company wants to be in the ML-ops business.
- HeatrayEnjoyer 3y ago
  The dynamic does seem to be different with the newer systems. Larger more general systems are better than small specialized models. GPT-4 is SOTA at OCR and sentiment classification, for example.

by paxys 3y ago

layble 3y ago

Sure, in the same way SaaS companies are just thin wrappers on top of databases and the open web.

imjonse 3y ago
You will find that a disproportionately large amount of work and innovation in an AI product is in the backing model (GPT, Mixtral, etc.). While there's a huge amount of work in databases and the open web, SaaS products typically add a lot more than a thin API layer and a shiny website (well some do but you know what I mean)
- tomrod 3y ago
  I'd argue the comment before you is describing accessibility, features, and services -- yes, the core component has a wrapper, but that wrapper differentiates the use.

wongarsu 3y ago

The same happened to image recognition. We have great algorithms for many years now. You can't make a company out of having the best image recognition algorithm, but you absolutely can make a company out of a device that spots defects in the paintjob in a car factory, or that spots concrete cracks in the tunnel segments used by a tunnel boring machine, or by building a wildlife camera that counts wildlife and exports that to a central website. All of them just fine-tune existing algorithms, but the value delivered is vastly different. Or you can continue selling shovels. Still lots of expensive labeling services out there, to stay in the image-recognition parallel

pradn 3y ago
The key thing is AI models are services not products. The real world changes, so you have to change your model. Same goes for new training data (examples, yes/no labels, feedback from production use), updating biases (compliance, changing societal mores). And running models in a highly-available way is also expertise. Not every company wants to be in the ML-ops business.
HeatrayEnjoyer 3y ago
The dynamic does seem to be different with the newer systems. Larger more general systems are better than small specialized models. GPT-4 is SOTA at OCR and sentiment classification, for example.