What's working for YC companies since the AI boom

I have 15 years of hardware development experience and I would stay far, far away from anyone in the VC space.

I simply don’t believe that anyone in VC is capable of aligning their own incentives to the timescale that a hardware based business requires to show a return.

There appears to be a pattern. Unmet need is identified: "I want ChatGPT -- but able to read PDFs" or "I want ChatGPT -- but able to do research and produce lengthy reports." Startup gets funding for this and, if they're lucky, releases a rough beta that leans heavily on the OpenAI API. Two months later OpenAI launches a better, much more polished and seamless version, which is integrated into ChatGPT itself.

I had briefly considered forming a Legal AI startup (going so far as to download the fulltext of every legal ruling ever made in the US -- something like 400GB) but then o3/DeepResearch got so good that it became apparent that there'd be little point.

Steve Hsu claims to have solved hallucinations in a customer service context, which might be the only "startup-type" idea that has a head-start over the giants.

This makes sense. The entire engineering/tooling field is so gonna change. Picking a winner makes isn't really possible. Most people are just starting to solve real problems with it and starting to build patterns that are not complete nonesense. But it will still change a lot

> “AI for X” verticals are surprisingly narrow.

I think that makes sense too. Those were a significant part of the initial hype. A lot of people promising that they'll take a "generic" LLM (which you all have seen how already smart that is) but now train it specifically on parenting, or trivia, or your emails, or your help center. It's a service type that will continue to exist. Perhaps it needs to tailor to a specific enterprise scenarios to gain traction as a startup. Though the need for these companies to manage the privacy concerns of the customers with their ability to inspect and look at the data and clean it might not be fully solved yet.

> Reducto - Reducto is an AI-driven API that specializes in converting unstructured documents like PDFs and images into structured data.

This is an example of the type of companies where "extracting LLM relevant context from X" and are relevant for any company doing the "AI for X" schtick or enterprise doing AI development on their own. This company is specifically about PDF and images, but we probably gonna see others that are for videos, archives, isos, msoffice docs, and even the ultimate holy grail of "universal binary => very rich structured data" API.

> Developer Tools & Infrastructure

The picks in this category are the most perplexing to me.

This was trickier 18 months ago, but every major LLM provider has solid support for this now. You can just drop an API call to Google, OpenAI, etc. your existing pipeline. What am I missing? Maybe the selling point was batch, but all LLM providers have a batch product now too.

    classification_response = requests.post(
        "https://platform.reducto.ai/extract",
        json={
            "document_url": f"jobid://{job_id}",
            "schema": {
                "type": "object",
                "properties": {
                    "document_type": {"type": "string", "enum": ["W2", "Passport", "Other"]}
                },
                "required": ["document_type"],
            },
        },
        headers=headers,
    )

    a) what are the growth rate when measuring customers which are *not* YC companies?
    b) what is the churn rate for that same group.

It might prove to be their downfall.

I was pretty shocked that of 275 companies in the Winter 2023 batch, only 12 have received Series A deals. Granted, I know a huge part of that is that the VC environment has just collapsed due to the end of the ZIRP era, but those numbers at least sound pretty brutal to me.

b) Everyone needs to stop perpetuating the YC lie that they invest in the best founders and they just happen to want to do AI. It's rubbish and insulting because it implies that only young, male, SF-based founders can be the best. Instead it's clear that YC has been aggressively pushing AI which makes sense given they are a significant investor in OpenAI.

What does he/she mean by tooling in this context?

Later section talks about developer tools so not that by the sounds of it. So tooling around inference maybe? Pretty sure ollama is in YC and that's surely "tooling"?

B2B opportunities are inherently easier to identify during early-stage evaluations due to clearer revenue models and problem-solution alignment.

YC’s network amplifies B2B growth by directly connecting startups to other batch companies as potential early adopters or customers. I found it interesting that https://www.ycombinator.com/companies/legora (former Leya) is using Reducto. If I get page 9 in the pitch deck correct: https://www.pitchdeckinspo.com/deck/Reducto_02c1f2af-3fa2-4a...

Early-stage B2B startups require less capital and shorter timelines to demonstrate product-market fit compared to B2C.

YC’s founder archetype—technical, execution-driven, and efficiency-focused - naturally gravitates toward building scalable B2B solutions.

Thanks for pointing out Reducto! I added it to my market overview: https://idp-software.com/vendors/reducto-ai/

TLDR

The IDP market remains a massive and growing space. There will be a new segment of the market for simple cases that do not need domain expertise, validation, and integration. Generic Document AI tools, so-called AI wrapper, provide easy wins for basic input extraction / categorization and splitting tasks.

Operational complexity, on-premises, integrating directly with enterprise infrastructure and domain-specific validation across fields mean different workflows require specialized handling. I think this is why Hyland, Abbyy and others can compete with the market, event the tech stack lagging.