Is AI changing how quickly startups reach traction?
By Amra Naidoo, Co-Founder and General Partner, Accelerating Asia Ventures
When we were selecting Cohort 13, almost every application seemed to mention AI. It got to the point where saying, "we use AI", told us about as much as saying, "we have a website". It was in the industry tags, the product descriptions and the pitch decks, but the label itself told us very little about whether the business was differentiated, defensible or something customers would actually pay for.
Six months later, Cohort 14 looked different. In C13, 35% of completed applications selected AI in the industry field. For C14, that figure fell to 21%. Normally, I would read that as founders moving on to the next popular label, except AI hadn't disappeared from the applications. It had moved.
Among the companies that didn't select AI in the industry field, 13.5% of C13 applications described AI alongside language about building the product, running workflows or operating the business. In C14, that figure was 24.8%. Fewer founders were calling themselves AI companies, while almost twice as many non-AI-labelled companies were describing AI as part of how the company actually worked. That's a much more interesting change than another round of AI on the cover of every pitch deck.
The companies got younger too
C14 didn't only change how founders talked about AI. The companies themselves were younger.
60.5% of C14 submissions were founded in 2025 or 2026, compared with 45.0% of completed C13 applications. Within that group of younger companies, 46.6% of C14 companies were already generating revenue, compared with 34.4% in C13.
140 submitted applications came from companies that were no more than 12 months old when they applied. 40.0% were already generating revenue and 76.4% reported at least one user or client.
That doesn't mean every new company is suddenly reaching product-market fit within a year. It means more founders are arriving at the early investment stage having already built something, put it in front of customers and collected enough evidence to have a much better conversation. A company might only be six months old, but the founder may have already built and tested several versions, spoken to customers and started generating revenue. A few years ago, getting through that many rounds of learning would probably have taken much longer.
Companies are getting there quicker
I don't think this is a coincidence. AI has dramatically reduced the time and money it takes a founder to build something, test it and put it in front of customers. Our application data can't isolate exactly how much of the traction shift came from AI, but after reviewing hundreds of companies and experiencing the same change inside our own fund, I think it's a major part of the reason.
The important point isn't that we've changed what we look for. We haven't. We still want to see evidence that a founder understands the problem, has built something people want and can turn early traction into a business that grows. What's changed is how quickly some companies are getting to that point.
For a growing share of software and tech-enabled companies, seed capital doesn't need to pay for the first version of the product anymore. Founders can often get there themselves. The capital, and the support around it, should help them work out which customer to serve, build distribution, make revenue repeatable and create an advantage that doesn't disappear when somebody else gets access to the same tools.
That's also why I don't think the conclusion is simply "AI companies perform better". The shift isn't consistent across every business model. The clearest change appears in younger B2B2C companies, where 52.9% of the C14 group were generating revenue compared with 35.5% in C13, and the median reported user or client count rose from 20 to 100. Younger B2B companies also moved forward on revenue stage, but their client counts were close. The B2C companies were almost unchanged on revenue stage and the C14 group reported fewer users.
The better conclusion is that AI is helping more founders get to evidence earlier. What they do with that evidence is where the investment question begins.
I've been living this inside our own fund
I wrote earlier this year about building Accelerating Asia's data infrastructure. I'm not a developer. Like, by any means. But I used AI to build an investor dashboard, an interactive portfolio page, an automated reporting system and a fund deck connected to live data. A few years ago, that would have needed engineers, a data analyst, a product manager and a budget we didn't have. The building got dramatically faster and cheaper.
The hard part didn't disappear. I still spent most of a year cleaning and reconciling the underlying data, and I learned the very hard way that generative AI will make financial information look beautifully consistent even when it's wrong.
That experience is one reason the C14 applications stood out to me. AI can compress the time and resources needed to get to a first product. It can't tell you whether the customer problem is worth solving, whether the data is reliable, whether the distribution works or whether the business becomes more defensible as it grows.
The tool changes what a small team can attempt. It doesn't remove the work that makes the result trustworthy.
A working product is only the beginning
There's an obvious upside to meeting founders later in their learning and earlier in the life of the company. Instead of debating a concept, we can talk about what customers did, what they ignored, what they paid for and what the founder changed in response.
There's also more noise. If a capable team can build a convincing product in weeks, the existence of the product becomes a weaker signal. A polished demo doesn't tell us whether the founder has proprietary insight, whether the customer keeps using it or whether somebody else can reproduce the same thing just as quickly. Product used to be evidence that a team could build. Increasingly, it's the beginning of the conversation rather than the proof point that settles it.
The questions we've always asked become even more important:
Would the customer still pay if the underlying model changed?
What has the founder learned from real usage that a new competitor wouldn't know?
Does the product become harder to replace as more customers use it?
Is AI changing the economics of the business, or only making the demo easier to build?
What is the company doing that wasn't possible, practical or affordable two years ago?
AI doesn't lower the investment bar or change what we're looking for. It means more founders can get to that bar faster, and we need to be more precise about where the actual advantage sits.
What this means for Fund 2
The application data gives us an early view of how company formation is changing across South Asia and Southeast Asia. The selection process lets us compare those companies across markets and business models. The 100-day accelerator then gives us a close view of what the founders do after the investment, when the feedback becomes harder and the decisions start to matter.
That combination matters more when products are faster to build. Access to AI tools isn't scarce. What matters is finding founders using them to solve a valuable problem, working out what is genuinely different and then helping the company turn early evidence into a repeatable business.
I don't think AI makes early-stage investing easy. It's helping some founders reach real evidence more quickly, which gives us more to assess when we meet them. What we look for when we make the investment hasn't changed.
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* Carta Q4 2025 VC Fund Performance. US benchmarks used as Asian fund comparables remain limited.
About Accelerating Asia Ventures
Accelerating Asia Ventures is an independent accelerator and venture capital fund investing in early-stage startups across Southeast and South Asia. Founded by operators, the organisation is committed to supporting founders with capital, credibility, and a long-term community.
For interviews, data requests, or portfolio introductions, contact: team@acceleratingasia.com
