What's working for YC companies since the AI boom
117 comments
·May 30, 2025A_D_E_P_T
neom
I sit on an AI evaluation committee for a huge law firm (It's just a regular old consulting gig) - we get so much inbound from (mostly kids) folks trying to build wrappers for some aspect of legal workflow, but behind the scenes thomson reuters is slowly adding everything they're going to need to software they have been using for 10 years now.
ck_one
In many fields there is no moat. It’s an execution battle and it comes down to question: can the startup innovate faster and get to the customers or can the incumbent defend its existing distribution well enough.
Microsoft owns GitHub and VSCode yet cursor was able to out execute them. Legora is moving very quickly in the legal space. Not clear yet who will win.
jgraettinger1
> Microsoft owns GitHub and VSCode yet cursor was able to out execute them
Really? My startup is under 30 people. We develop in the open (source available) and are extremely willing to try new process or tooling if it'll gain us an edge -- but we're also subject to SOC2.
Our own evaluation was Cursor et all isn't worth the headache of the compliance paperwork. Copilot + VSCode is playing rapid catch-up and is a far easier "yes".
How large is the intersection of companies who a) believe Cursor has a substantive edge in capability, and b) have willingness to send Cursor their code (and go through the headaches of various vendor reviews and declarations)?
neom
A business is nothing more than defined, established, repeatable systems and processes. That is the difficulty with tooling businesses.
justinbaker84
Can confirm. One of my best friends is a senior engineer at thomson reuters and they are focused on that.
eh-tk
Have they published anything yet? Would love to read
1vuio0pswjnm7
"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."
Has the download now been deleted
Will it be shared with others
500GB/1TB of storage is not expensive
Were any permissions obtained prior to download
eddythompson80
They do exist, but not in the simplistic "AI but for X" you described. The article even explicitly says there were zero of those companies in that round.
You can see "AI startups" examples there, A company that manages your business outbound communication with lots of AI features. AI powered code generation for business operations, Accounting services with lots of AI features, business finance software with lots of AI features
belter
This is the way. Use predictable logic and the control of automation with AI as augmentation. So not AI on the control loop, but AI as a helper. Who manages to find solutions that will find the sweet spot of this setup, will be the next success.
keiferski
Yes, they do, because ultimately software UI is what gets regular people to use things.
To a technical user there may be little/no difference to you between prompt engineering into a chat box vs. clicking a button with premade text slots.
But to the average non-programmer, a chat app like ChatGPT is somewhat pigeonholed into the chat format, and so use cases that don’t lend themselves to this interface will be outcompeted by specific apps that do.
btown
We should never forget the infamous 2007 Dropbox comment! https://news.ycombinator.com/item?id=9224
skydhash
The exchange was quite nice and thoughtful compared to the usual AI conversations we have on this site.
- This is the best AI tool ever!
- Does it fix the reliability issue?
- Not now, but wait 6 months, because that’s when better models will be coming out
g9yuayon
> Do "AI Startups" even make sense?
I'm doubtful. Remember when Google said their strategy was AI First? Baidu too? I'm old enough to remember that the criticism then was along the line "AI is technology. What problems do you want to solve?". The line of thinking seems still relevant to me today.
CyberRymden
I think it absolutely makes sense. ChatGPT's strength is how generalised it is as a tool, but openAI will never able to adapt the platform to every single use case. You can absolutely use it to learn a language for instance, but a great AI language learning platform needs a better tailored UI, it needs all kinds of non-AI functionality around it like idk a spaced repetition system, it might need to integrate into other platforms, and good prompting to be effective. AI isn't the product itself, but a component to try to solve a problem. And honestly I wish more startups focused less on simply "AI" and more on the problems it should solve.
If for nothing else openAI won't be able to market itself for every single use case, and so long as people aren't using chatGPT for some use case (even if it could perform the task) there's still an opening.
idiotsecant
But any use case that gets large enough and makes money will be absorbed by openai direct, based on the market developed by the startup. OpenAI is using the Amazon model. Let someone else spend the money figuring out which market segments are profitable, then steal them with their inherently better access to the platform.
MarkMarine
There are use cases that don’t align with OpenAI/Anthropic’s business model, which is to always use more tokens to get better. As the models improve they become way more expensive, with order of magnitude increases in price. I think there is a lane for detecting what can and should be deterministic after doing it a few times, and making it concrete so you don’t just burn tokens every time.
simianwords
Its a good point and it comes back to why Google can't take up projects that other startups are working on.
Google has all the technical infrastructure, talent and everything to make something like AirBnB, Docusign and hell even intellij. Why not?
A_D_E_P_T
Thing is, OpenAI/Anthropic/etc. are demonstrably taking up those ideas. There actually were (are?) AI PDF reader startups and AI research assistant startups. (And AI coding startups, AI video startups, image analysis startups, etc.)
"AI startups," if they make sense, seem to have a very short shelf-life. They're either overtaken by the continuing improvement in LLM context windows, or, if there's a real and general unmet need for what they offer, the giants will tend to integrate it.
jkukul
Google actually did make their own Docusign, it's called eSignature [1] and it was built into Google Workplace
simianwords
There’s a specific reason why google doesnt promote a Docusign like product even when they have superior technical abilities.
It probably comes down to the fact that code is not that crucial but all the other non technical aspects like distribution, supplier relations and marketing that makes a product.
Maybe LLM wrappers turn out to be that way. The model may not matter but the distribution and customer relation etc would matter more.
scrollaway
And in true google fashion, it only works with Google accounts; if you send a signature request to a non-google account, it says it's sent but does not work...
OtherShrezzing
Because of a mix of comparative advantage and opportunity cost. Google as an entity absolutely dwarfs those other companies, and competes at that scale. Airbnb’s annual revenues are lower than Googles annual r&d spend. Google’s “wins” need to move the needle on a $2tn valuation, and an Airbnb size win doesn’t do that.
matt_s
This is also the case in the past with companies like Xerox and why didn't they do X with Y that came out of PARC.
There's only so much product bandwidth a company can take on that makes sense, look at the graveyard of Google products.
lenkite
Because everything they attempt is inevitably compared to Google's ad business - which makes everything else look like a starving beggar.
dyauspitr
> going so far as to download the fulltext of every legal ruling ever made in the US -- something like 400GB
Where can I find this?
fc417fc802
PACER exists at the federal level. Otherwise you have to piece it together from each jurisdiction yourself, defeating any anti-scraping measures in the process. Unless someone happens to have made such a dataset available via torrent at some point?
A_D_E_P_T
Start here: https://com-courtlistener-storage.s3-us-west-2.amazonaws.com...
The "opinions" are what you want.
These are huge files heavily compressed, so they're quite difficult to handle.
koolba
Why are they huge? Is it just PDF overhead? The opinions themselves should just be some finite number of pages of text no?
PeterStuer
Maybe he refers to something like https://law.justia.com/
xorcist
I see a pattern with AI companies. They always try to solve a really hard and not very useful problem. It's the same as with self driving car companies ten years ago: If you believe self driving tech is ripe for commercialization, the reasonable thing to do is something capital intensive and a special case where the technology most likely to succeed. For instance, heavy trucks automatically following others in formations for long drives. Saves gas, money, and potentially personnel.
There is a clear business case and buying large trucks is already a capex play. Then slowly work your way through more complex logistic problems from there. But no! The idea to sell was clearly the general problem including small cars that drive children to school through a suburban ice storm with lots of cyclists. Because that's clearly where the money is?
It's the same with AI. The consumer case is clearly there, people are easily impressed by it, and it is a given that consumers would pay to use it in products such as Illustrator, Logic Pro, modelling software etc. Maybe yet another try in radiology image processing, the death trap of startups for many decades now, but where there is obvious potential. But no! We want to design general purpose software -- in general purpose high level languages intended for human consumption! -- not even generating IR directly or running the model itself interactively.
If the technology really was good enough to do this type of work, why not find a specialized area with a few players limited by capex? Perhaps design a new competitive CPU? That's something we already have both specifications and tests for, and should be something a computer could do better than human. If an LLM could do a decent job there, it would easily be a billion dollar business. But no, let's write Python code and web apps!
dpflan
AI allows for exquisite demos, demos that tantalize the audience into thinking of the infinite potential of the technology, that stunning vision expands and expands until the universe of potential overwhelms the dreamer into a state of terminal fantasy. So it is always a solution looking for a problem. There are cases where the two meet more realistically and a valuable impactful company develops it.
joshuajooste05
Agreed, the agents people are building are not solving the real issues.
The other thing people have been trying to do is build general agents e.g. Manus.
I just think this misses the key value add that agents can add at the moment.
A general agent would need to match the depth of every vertical agent, which is basically AGI. Until we reach AGI, verticalized agents for specific real issues will be where the money/value is at.
pegasus
That's exactly the approach to NLP which these super-successful LLMs are contradicting. They are generalists who can best with ease customized software developed over many years in all the subfields of NLP.
zdw
> heavy trucks automatically following others in formations for long drives.
Congratulations, you just reinvented the railroad.
rogerrogerr
The railroad can’t have individual cars break off from the line to go to arbitrary warehouses, stores, and residences.
The railroad is an amazingly low cost way to move tonnage, if you’re going from a place where the railroad stops to another place where the railroad stops. There aren’t really companies that _could_ be using rail and aren’t.
But it just isn’t cost effective in many cases once you add in last-mile costs. If we built more rail (politically infeasible), you might see more usage but ultimately you still suffer from needing at least one locomotive per train.
zdw
I'm assuming this is a north america centric viewpoint - there are plenty of places in europe and asia where rail is far more common and has societal/political favor.
Solving the last mile by having stores that get shipments near a local train station that serves both cargo and passenger needs, and using kei trucks for small local deliveries is definitely a different set of tradeoffs.
overfeed
> The railroad can’t have individual cars break off from the line to go to arbitrary warehouses, stores, and residences
So the hypothetical trucks can't handle freeways but can self-drive on much more complex urban and suburban roads?
xhkkffbf
Hah. Sort of. But the big difference is the railroad doesn't let anyone else use it. A regular road can support cars, trucks, truck convoys and maybe even bikes or pedestrians. A railroad can support trains.
pona-a
Do they, pragmatically speaking? High-volume cargo traffic quickly wears down the asphalt and causes regular jams, a bike lane unseparated from cars is a safety hazard enough large enough to push many potential riders off the road, and most morning commutes would be better served by well-developed public transit.
One EMD SD70ACe locomotive moves over 10,000 tonnes of cargo using 1,300 L of diesel per 1,000 km. The equivalent 286 trucks would consume 107,250 L, while needing 55.8 km of a single-lane highway, compared to the 2.16 km freight train.
Similarly, the average US car has 1.5 passengers per ~30 m² of space, so 20 m² per person. An average bike is about 2 m² per person. A typical trolley car holds ~160 passengers per 200 square meters, so 1.25 m² per person. A tram reliably moves at 60–80 km/h on interurban routers, or 30 km/h in urban centers with frequent stops, a considerable improvement over San Francisco's 16 km/h by car for last mile.
eddythompson80
> Zero LLM evaluation, observability, or tooling companies in the Series-A data.
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.
lubujackson
0 consumer products is wild. I know SaaS has taken over from a bang for buck perspective, but this seens like a too-narrow approach by YC.
throwanem
A too-narrow approach after Apple beefed it? Nobody knows how to bring AI to market yet but OpenAI, Anthropic, and Google. Long shots are one thing, but all the ideas I've heard for b2c AI so far are mostly more like pipe dreams. Look for a Zynga play once the field starts opening up for that in maybe a year or so, would be what I'd try to do.
BoorishBears
"after Apple beefed it?" ... what? Apple's inability to improve their OS is somehow an indictment of B2C AI offerings in a general sense?
You seem unfamiliar with the space, there are plenty of players outside of OpenAI, Anthropic, and Google bringing AI to the consumer space: https://a16z.com/100-gen-ai-apps-4/
Consumer AI is arguably doing better than enterprise where 99% of the spend is poorly scaling undertakings that don't deliver on even 1/10th of their cost.
throwanem
I wasn't really counting horny chatbots and nudify apps, fair. But I also don't have a book in the space to talk.
PeterStuer
B2C has gone mostly 'free' which means it is either relying on shady business models, sensitive to regulation enforcement and so a risky investment, or a numbers game which requires significant upfront investment with a 'hit' business model return.
In both cases backing 1 company with significant investment is not rational.
keiferski
Levels. I think we are somewhere between 2 and 3?
1. YC startups target consumers. (B2C)
2. YC startups target businesses. (B2B)
3. YC network becomes large enough that startups can exist purely to serve other YC startups. (B2YC)
4. A new accelerator is launched which aims to fund YC companies that serve other YC companies. (YC4YC)
5. ?
Mostly joking, but I do sometimes look at the social media accounts of people in YC / Silicon Valley and wonder if they are living in an increasingly insular world. I think they would benefit from stepping outside of that into the greater world economy more deliberately.
spiderfarmer
Most AI money will be made outside Silicon Valley. If a company can save millions by spending thousands on AI, who profits the most?
biccsdev
0 consumer products is just a symptom of the current state of the global economy
ljf
It is also a sign of where something is in its cycle - when engines were first invented they laboured in mines originally, then moving traction engines/tractors, then trains - it was a long time before the average person owned an engine for their own use.
sokka_h2otribe
This was largely driven by the efficiency and fuel density.. so like, the mines specifically were coal mines which flood and need pumps, but obviously have unlimited coal. The engines/tractors were competing against oxen and horse, and it's hard to get a lot of power out of them. The trains had an easy time to carry lots of coal due to their nature.
I'm not actually sure where tractors fit in. I haven't heard of them in the equation early on. I think at some point they were probably viable, but I never heard of a coal powered tractor (maybe there were some). I suppose tractors could leave piles of coal and stuff if they needed to by the fields.
Ozzie_osman
One way to interpret this might be that in consumer products, it's easier for incumbents to add AI to improve an already well-marketed product than to build and market one from scratch.
danenania
Yeah, and I think it’s also simply that inference with strong models is expensive.
OpenAI is lighting boatloads of money on fire to provide the ChatGPT free version. Same with Google for their search results AI, and perplexity which has also raised a lot. Unless you can raise a billion and find a unique wedge, it’s hard to even be in the game.
You can try to use small cheap models, but people will notice that free ChatGPT is 10x better.
Ezhik
I guess consumer products are a niche to be filled either by the giants or by random people with a dream and a bit of SwiftUI knowledge.
BoorishBears
How is it YC's fault the consumer apps failed to raise a Series A?
I personally have a consumer AI product that had 3 competitors get into YC, and they just didn't perform very well:
- One has so little distribution the only sign of life in the last 3 months was that they updated their landing page.
- Another released a disappointing app, didn't really iterate on it, and eventually pivoted into being a legal AI answering machine after that flopped.
- The third took down their app shortly after YC and pivoted to a content creation site for YT channels... then randomly let their site start going down, ignoring the customers, and doesn't seem to be doing anything anymore.
Meanwhile some competitors that didn't get into YC are now at 7 figure MRR (I'm at a measly 5 figure MRR). So it's not like the space these apps were in is as disastrous as these comments are making them out to be: YC took a chance and unfortunately these teams just weren't the right teams.
shivbhatia
Hard to beat the chat interface when it comes to consumer products if you ask me. Pre-AI I often wished I could just talk to an application rather than try to figure out how the buttons the developers had chosen to wire up mapped onto what I was trying to achieve.
econ
It look like cli to me.
You would rather have a thing that solves a specific problem in a completely reliable way. An application that knows what you want to do because there is only one thing to do in the universe. AI can write it but never be it.
JumpCrisscross
> Hard to beat the chat interface when it comes to consumer products
How about it just working? No need to ask. The way a great assistant just makes the things you need and want happen.
mritchie712
I don't want to sound like the infamous dropbox comment, but isn't Reducto just an LLM function call?
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,
)adamsuskin
> Obviously, if one of these funds gives you a term sheet, it’s a no-brainer.
It isn't a no-brainer. Many founders in the new era are weighing bootstrapping, seed-strapping, and VC money without a clear answer.
If you can see a path to growing MMs of revenue with little need for staff, you may just not go do a follow-on round.
yesimahuman
Just raising as much money as you can when you can from even great firms is how so many founders get themselves in big trouble down the road and reduce their exit optionality or introduce deathly signaling risk. But if you can take lots of secondary to derisk and build that nest egg I guess have at it
null
tzury
To measure what's working for B2B YC companies, especially over the course of two years period you need to answer the following questions:
a) what are the growth rate when measuring customers which are *not* YC companies?
b) what is the churn rate for that same group.
hn_throwaway_99
I am curious about how this compares to past years.
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.
saubeidl
YC is going all-in on a technology with no proven business value, driven mostly by ideological desire.
It might prove to be their downfall.
tlb
When a technology has proven business value, it's too late for seed investment. Existing companies are usually better at commercializing tech with proven business value.
The economic purpose of seed investors is to take on technology-market risk. It's a necessary part of the economy, since the only way to find out if a technology has business value is to have companies build things with it. Without investors willing to take on that risk (and lose frequently -- more than half the time) there'd be only incremental technology progress.
atleastoptimal
Is HN’s continual downplaying of AI’s impact and economic potential against all evidence just the new version of the mentality that precipitated the famous Dropbox comment? The comments in anything AI related on this website are so predictable
blibble
> Is HN’s continual downplaying of AI’s impact and economic potential against all evidence
there's quantitative evidence in this article
unfortunately it doesn't support your position
atleastoptimal
If by quantitative evidence you mean that very few have made it to series A, that doesn’t preclude the overall economic impact or potential of AI, solely that its a crowded field where the exact niches haven’t been ironed out yet. This is the same for all nascent industries, but to assume such implies it is all a scam with no economic viability feels more like wishful thinking than a reasonable inference from trends.
simianwords
didn't YC research fund OpenAI itself?
threeseed
Yes. And now they have a massive vested interest in drawing startups into the space.
cushychicken
zero… hardware startups in the Series A data
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.
msgodel
I think it's a good thing. I've personally never been a huge fan of the culture that developed around VC (which sounds a little funny coming from someone who's been on Hackernews for ten years I guess) so it means there's a nice little niche for people who want to go a little slower.
Put on some ambient music, grab a trackball, do some CAD, don't think about equity.
bgnn
I second this. I've worked for several early stage HW start-ups (ASIC development). VC backed ones ended up in a weird state of not being able to move from proof of concept to production because at that point the VCs were out of patience and wanted returns for their money. Having spent 10s if millions and now, 2-3 years later, requiring even more didn't align with what they were used to. This ended badly for these companies, all with great potential. Lucky ones could get acquired for a meaningful amount.
Bootstrapped companies were much better but they lacked the capital to develop their own products, so they were often reliant on one big customer. Growth was slow but more organic.
jsheard
> VCs were out of patience and wanted returns for their money. Having spent 10s if millions and now, 2-3 years later, requiring even more didn't align with what they were used to.
Those numbers seem quaint now compared to OpenAI's "can we borrow $1B, actually $10B, hang on we need $50B, sorry we meant $500B, also we might need $7T" investment death spiral. Maybe when this is over VCs will be glad for how cheap and low-risk hardware development is, relatively speaking.
bgnn
Yeah but HW isn't sexy if it's not for AI or quantum computing. I hope their behaviour changes.
ai-christianson
But hardware is super capital intensive, right? So the right kind of investor could add significant value if they were aligned.
frainfreeze
> But hardware is super capital intensive, right?
Depends on what hardware, who is developing it, and for who. Proof of concept is almost always cheap enough to make. Beauty of HW is that you get PMF straight away - even if you have the worst completely broken product, but it it brings value to someone, they will pay for it. From there you can bootstrap, take on credit etc. Capital intense part can wait - refinement, certifications, patents, packaging, documentation, mass production etc. This is of course all under assumption that the core team knows how to build everything, if you're outsourcing in the prototype phase then you're probably toast anyway.
> So the right kind of investor could add significant value if they were aligned.
I always perceived value of investors to be everything but the money. If you need just the money then get a loan.
bgnn
That's often potential customers. It's common to have other HW companies invest in HW start ups. Unfortunately there are not many good VCs for HW development. Even the ones marketing themselves as much don't like the meager returns in 5 to 10 years.
ai-christianson
Might be a ridiculous question (I'm a software guy,) but is it at all possible to go the other way and increase the velocity of shipping and iterating on hardware to make it fit into the standard VC timelines?
cushychicken
True in theory.
Not in practice.
JumpCrisscross
> the right kind of investor could add significant value if they were aligned
Those investors rarely brand themselves as VCs. To their portfolio companies or LPs.
null
threeseed
a) The next two years are the reckoning for a lot of these AI startups. In the enterprise space everyone was being pushed to trial AI products to see whether they can deliver the ROI that was being marketed. Newsflash: it hasn't. And many of these startups will see serious churn.
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.
gizmo
Other than the "request for startups" YC publishes YC doesn't push founders to start a specific type of business. AI is simply where the opportunity is (or is perceived to be). YC (and everybody else) understands that most AI enterprise startups will fail, as you point out. The gamble, as always, is that a few startups in the current batch will get huge.
mirkodrummer
They dont push founders but surely they allocate more on AI startups, so founders put the magic word X with AI or AI Driven in their products for the same reason
threeseed
> YC doesn't push founders to start a specific type of business
Of course it does.
Founders look at YC batches, see that it is 99% AI companies and are then forced to also go in that direction if they want the benefits of the accelerated YC path.
And YC deliberately chooses founders with AI companies because they have an investment thesis that is different from "request for startups". Garry Tan has been a massive e/acc fanboy since the beginning and genuinely believes that AI in every use case will advance humanity. And the partners all align with this.
This is all inarguable because amongst the tens of thousands of applications there are surely many amazing non-AI companies. Is this implication that they are all worse than what was selected in the batch ?
pera
You are being downvoted but I think you are correct: I'm not sure I could name a single company that came out from YC in the past 10 years, which makes think that YC was essentially pg, and I'm not a fan of him but he was obviously a very talented business person and had a really good eye for startups.
YC's leadership nowadays seem to lack of any kind of vision and just follows whatever shiny tech is currently in vogue, which is a huge red flag for accelerators.
bravesoul2
When you read people's experience with AI for building/coding I get the feeling we are still not quite there on a lot of things, but when we get there you can just buy direct from Costco (I.e. OpenAI etc.)
Sell your AI hot potato quickly!
nkotov
Regarding Series A - since the YC deal has increased to $500k, there seems to be no pressure to quickly get a Series A when you have just enough to figure out if what you're building actually works and since the tools have gotten so good, you're not under pressure to spend it quickly either.
Do "AI Startups" even make sense?
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.