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Lessons from YC AI Startups

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Re: Lessons from YC AI Startups

#61

Post by intended »

noduerme wrote: post #34

Most of my dev work is in logistics for a very staid industry which doesn't even make this list, that would have a difficult time finding a use for LLMs, because most of the day-to-day jobs involve manual labor. That said, I've been asked "how can AI help us" quite a bit (the unsaid ending to that sentence being, "...help us lay off workers"). Just as a thought experiment, I've considered the pros and cons of automat…

An LLM based tool is very good for any case where 1) User is an SME 2) Generated output can be verified by user easily.

After that its just a gradual creep into LLM ops and madness. Speaking from the other side of that descent into madness.

As obvious as it may be, production LLM tools work on your data. You can't simply use an external benchmark to verify if your tool works for your use case. You will always have to build evaluation processes.

I'd say there are 2 type of tests you will end up running.

1) Statistical Tests - AKA good old ML. 2) Semantic Tests - Here be dragons.

Semantic tests break down further based on HOW you are using the LLM. (Categorization, Summarization)

The issue with Semantic testing is the amount of human effort. Its more akin to setting up exams and evaluating answers. Also your student may be tripping randomly.

Categorization - you can simplify it down to almost ML workflows. Summarization ? That takes effort to verify.

Re: Lessons from YC AI Startups

#62

Post by paulsutter »

noduerme wrote: post #34

Most of my dev work is in logistics for a very staid industry which doesn't even make this list, that would have a difficult time finding a use for LLMs, because most of the day-to-day jobs involve manual labor. That said, I've been asked "how can AI help us" quite a bit (the unsaid ending to that sentence being, "...help us lay off workers"). Just as a thought experiment, I've considered the pros and cons of automat…

In logistics, I would suggest LLMs as an assistant to workers. The worker would still make the judgements, but an LLM could fill out forms, suggest alternatives, remind of unfinished tasks, or help look up information such as the right contact person at customer or vendor Xs. These features mostly reduce tedium, but keep judgement in the hands of the person

Future LLMs should gradually become more powerful, and any work on such an assistant today will be good preparation for more powerful assistants

Vertical specialties like logistics are in fact the BEST place to use LLMs, quoting from the article:

"The most interesting (and likely valuable) companies are the ones that take boring industries and find non-obvious use cases for AI. In those cases, the key is having a team that can effectively distribute a product to users, with or without AI"

Re: Lessons from YC AI Startups

#65

Post by sinman »

malux85 wrote: post #32
baq wrote: post #28

Earlier quoted context omitted.

Kind of mind blowing. A light in the tunnel for the reproduction crisis? What specifically have you trained your coding LLM on? Is it lora or something more advanced? Have you created a corpus by hand specifically for training?

Yes, created by hand, lots of techniques are required to get a well running system, GoT (graph of thought) RAG (retrieval augmented generation), Ko detection, dynamic problem decomposition, and a few more techniques I have invented but dont really have names for. Its also quite a bit more complicated than the simplistic answer I gave before because you have to do things like experiments in abstraction laddering to ge…

Random question for you: How feasible is it for someone with a software/ML background to get into this space? I'd like to get into learning a new domain through ML, but it feels very daunting.

Re: Lessons from YC AI Startups

#66

Post by brandall10 »

SkyMarshal wrote: post #52
malux85 wrote: post #32

Earlier quoted context omitted.

Yes, created by hand, lots of techniques are required to get a well running system, GoT (graph of thought) RAG (retrieval augmented generation), Ko detection, dynamic problem decomposition, and a few more techniques I have invented but dont really have names for. Its also quite a bit more complicated than the simplistic answer I gave before because you have to do things like experiments in abstraction laddering to ge…

That’s too bad, did YC give you a reason?

YC doesn't give reasons.

Re: Lessons from YC AI Startups

#68

Post by ekanes »

Gerardo1 wrote: post #54
epolanski wrote: post #42

Earlier quoted context omitted.

It really sounds like AI is a bubble if people ask how they can cram AI into their workflow rather than coming up with real problems they want to automate. I suspect that a very large number of applications that could've been "normal" programs are going to be AI'd with no clear pros but many cons.

I keep bringing this up, but this launch HN post about Roundtable: https://news.ycombinator.com/item?id=36865625 still shocks me. It's a company that uses AI to produce survey results. I'll let you read their pitch/description and decide for yourself, but I think it's very fair to say that this is a service to fabricate survey results to validate whatever idea it is you had beforehand. But even side-stepping that, th…

> an individual's project to provide an AI therapist and while people here and there did mention the cons of having a program provide medical treatment, the overall sentiment wasn't at all negative.

Sorta. You're forgetting to compare it to an alternative. Compared to a licensed therapist that you can find, schedule, travel to and afford... for some people an imperfect something is better than the nothing they have now.

Re: Lessons from YC AI Startups

#69

Post by corry »

jaynate wrote: post #8

Odd that the first two categories of AI startups were AI infrastructure. Reminds me of blockchain to some degree. Would have expected to see more vertically focused solution on the top 4 list. Eg, transportation, oil & gas, agriculture, etc… all huge markets.

I don't disagree, but two reasons we don't see them (yet):

(1) much harder to launch as some amorphous vertical AI - like, what would a 'Transportation AI' look like? Versus targeting a specific workflow within it - "AI copilot for truck drivers".

(2) The large incumbent software platforms that already are powering the day-to-day workflows are THE companies to implement AI first and most accessibly. So the likely winner of the vertical AI race is whatever incumbent platform is already in the highest % of companies or powering the highest % of workflows.

Re: Lessons from YC AI Startups

#70

Post by debacle »

jaynate wrote: post #8

Odd that the first two categories of AI startups were AI infrastructure. Reminds me of blockchain to some degree. Would have expected to see more vertically focused solution on the top 4 list. Eg, transportation, oil & gas, agriculture, etc… all huge markets.

It's startling how much transportation has already been optimized, and agriculture + AI is a natural pairing, but John Deere isn't a startup in the traditional sense. They've said they will have fully autonomous fleets by 2030.
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