The New Fundraising Playbook: Seed-Strapping
Why AI is fueling seed-strapping; when it works and when it doesn't
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Hi friends,
Lately, there has been a lot of discussion among founders and investors about a renewed interest in “seed-strapping”. While this approach has always existed, we're seeing a dramatic increase in founders choosing this route, especially as AI makes it easier to scale with less capital.
Let's dive in and explore exactly what seed-strapping means, why it's become possible, and whether it's truly a sustainable model for building enduring companies.
What is seed-strapping
“Seed-strapping” is when a startup raises just one early funding round (usually a pre-seed or seed in the range of $500K-$4M) and then relies solely on revenue to fuel further growth. Unlike the traditional venture path, seed-strappers skip the constant fundraising but instead rely on the revenue and more specifically the profits on the revenue they generate to grow their business post achieving product market fit.
Think of it as bootstrapping with a head start. You get enough runway to prove product-market fit and achieve profitability, then you never look back.
But why the renewed interest in taking this route? The answer lies in how AI has changed the economics of scaling software companies.
How AI is fueling seed-strapping
Two powerful trends have made seed-strapping more achievable than ever:
A. Strong early product-market fit
AI tools have dramatically accelerated the path from idea to revenue. Teams of two to four engineers can now ship sophisticated products in weeks instead of months or years. In addition, the time-to-value of AI products can be quite quick and their ROI quite high, meaning that products built relatively quickly can be sold quite early, both through bottoms up motions as well as to businesses which all have mandates and deep interest in adopting AI.
Thanks to this rapid customer adoption coupled with faster build times, some startups are hitting millions in revenue with much less upfront capital.
B. Doing more with fewer people
Even post product-market fit, AI can help reduce the capital needed to scale a business significantly. AI can automate repetitive tasks across various departments such as customer support, sales and marketing. Coupled with the strong ROI on AI products in R&D functions such as engineering and design, startups can even continue to scale much faster with small teams.
’s Lean AI Leaderboard highlights dozens of companies that have grown to 10s of millions of revenue burning under a few million and with 20-30 people. These metrics were extremely rare if not almost unimaginable just a few years ago. While many of them may not seed-strap in the definitional sense, seeing what is possible with small teams and amounts of capital encourages other to potentially go that route, at least initially.Who's actually seed-strapping
While the term has become popular relatively recently, there are a few examples of the phenomenon going back:
Zapier raised just $1.3 million in 2012, reached profitability in 2014, and is now valued in the billions, thus far not raising any other primary capital.
Calendly raised a modest $550k seed round in 2014 and operated profitably for years before eventually raising a very large $350 million “Series B” at a $3 billion valuation.
While not quite seed-strapped, Veeva raised $7M in seed and Series A money in 2007 and 2008, before not raising again until their IPO in 2013
Recent AI-native startups like Aragon (10M runrate, <1M raised) and Jenni.ai (10M runrate, <1M raised) and Pump (15M runrate, <5M raised) are all examples which so far have scaled to double digit millions of ARR and only raised one round so far, although of course they may decide to raise another round in the future.
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Why seed-strapping is appealing and risky
The allure of seed-strapping to founders is quite clear:
Founder control: Less dilution means founders retain more equity
Operational discipline: Without deep pockets, companies stay lean and more disciplined
Strategic optionality: When your growth is revenue-funded, you control if and when you raise additional capital opening up a broader range of outcomes in which everyone involved with the company does well as well as paths for the companies (remain profitable and distribute dividends, etc)
Hiring: For certain people, seed-strapped startups offer less risk and potentially more stable work environments. The below comment on Hacker News also highlights how it can be appealing to prospective hires since it de-risks companies in terms of runway and certain macro factors.
At the same time, the risks are real, and it may only work in certain industries and markets.
Losing out to better-funded players: This is perhaps the biggest risk of seed-strapping. It’s very possible that a better funded competitor raises more money and leverages that to hire more people, build a better product, be more aggressive on GTM and crush the seed-strapped competitor. Parker Conrad (CEO at Rippling) encapsulated this danger well:
Source: Twitter May put a ceiling on growth: Ultimately, if a company is operating in a large market and can deploy capital in a way that the return on that invested capital is high, it's in the interest of the company to invest additional capital. Being dogmatic about seed-strapping can sometimes restrict the ability of the company to grow as quickly as it could be or grow as large as it could have been otherwise.
Hiring challenges: Without splashy valuations and large equity pools, seed-strappers may struggle to recruit talent in certain cases, particularly depending on the talent it needs.
Limited liquidity: Early investors (and founders and employees for that matter) may find themselves stuck as it relates to liquidity.
The reality is, seed-strapping isn't foolproof. It's powerful but comes with meaningful trade-offs.
When does seed-strapping work
Seed-strapping works best when:
Early product-market fit: You can hit meaningful revenue inside a year, so the seed cheque stretches until the business funds itself.
Low-cost distribution: Product-led growth, usage-based pricing, or self-serve onboarding keep CAC small and scalable.
High margins and fast payback: Cloud delivery, 70%+ gross margin, and sub-12-month CAC payback turn revenue into working capital.
Fragmented or niche markets: Multiple winners can thrive, so depth of execution beats speed of fundraising.
It's a poor choice for:
Winner-take-most markets: Marketplaces, social graphs, and broad horizontal platforms reward whoever scales fastest.
Heavy upfront spend: Hardware, biotech, deep infra or regulated fintech need large R&D or compliance budgets long before revenue.
Capital as a trust signal: Seed-strapping can be difficult when buyers or talent look for companies with strong balance sheets, visible reserves for support, and the ability to fund continuous innovation.
Outsized ambitions in tight races: When the market is hot and every incremental dollar can be reinvested at a high return on invested capital, a larger war chest usually beats thrift.
The "skip-the-A" variant
There's another intriguing variant of seed-strapping emerging: the "skip-the-A" strategy.
Here, startups raise their pre-seed/seed round but then stay lean enough to bypass the traditional Series A altogether, jumping directly into larger rounds (like a Series B or C) at higher valuations typically in the double digit millions of ARR.
These rounds may still be labeled as a Series A when they happen, but look more like a growth round (Series B/C) in their size and valuation.
Garry Tan from Y Combinator points out how this is becoming more common:
Calendly mentioned above famously executed this playbook, and it's becoming increasingly appealing to AI startups with explosive early revenue where given a combination of factors such as strong ROI, explosive prosumer adoption, they can scale to 8 figures in ARR very quickly.
8. Closing thoughts
I believe seed-strapping is a real option worth considering for some AI-native startups. But like all strategies, it's neither perfect nor universally applicable.
Ultimately, whether seed-strapping thrives or fades will depend on how effectively founders navigate these trade-offs. The winners will be those who correctly identify which path matches their business model. I don’t think it threatens the typical venture model soon since thus far the very best companies have continued to raise capital, but may change the dynamics of what future rounds look like and exit ownerships in many cases, as it represents a sign of the idea that companies can accomplish more and more with less and less capital.
Revenue per employee in AI startups might be very high but it obscures the inference costs that scale linearly with customer use. Hard to charge customers enough to recoup those inference costs. These startups end up burning cash in spite of the high revenue per employee number. And they are easily copyable by competitors and face the threat of absorption by the underlying model.