For the last 5 decades, every wave of technology-driven companies has had amongst them highly capital efficient businesses. The capital efficiency tends to reflect that (a) people really want your product and will pay you for it and (b) the founders are cost-conscious and frugal, and do not overhire.
Indeed, Paul Graham from YC has developed the metric of “default alive” to reflect capital efficiency as a core sort of startup metric.
Capital efficiency has existed in roughly every technology wave. Many of the largest, more important companies in the world started off highly capital efficient. Inded, capital efficiency tends to reflect an especially strong business model (and in some cases founder). Examples include:
Microsoft was bootstrapped in the 1970s and did not raise any venture capital until a round right prior to their IPO, when Bill Gates wanted a VC for his board and that came with strings attached for the VC to invest. The investment went straight into the bank account and remained untouched.
Dell was bootstrapped off cash flow in the 1980s until a similar pre-IPO round.
Yahoo and eBay famously did not touch their early venture capital funding in the 1990s – they were run so lean and profitably that they did not need to raise large sums.
Google raised a single round of traditional venture capital, before doing a pre-IPO round with Yahoo! and others.
Instagram was just a dozen or so people before being acquired by Facebook, while Zapier only ever raised a $1.3M seed round before bootstrapping from then on. These companies were founded in 2010s on.
Midjourney (founded in 2020s) is rumored to be entirely bootstrapped today.
In general there tend to be two drivers of capital efficiency.
Customers will pay (a lot) for the product. The “capital” side of capital efficiency is often a proxy for both product / market fit and an intense customer need. Customers are willing to pay up for a product that is important to them, and there is insufficient competition in the market to commoditize pricing or destroy the category (so the product is somehow differentiated). Pricing is often a proxy for value & differentiation of a product.
The company is run efficiently. During COVID roughly all tech startups lost their way on spending. Capital was flowing freely and teams often rapidly and dramatically over hired, boosted expenses on things non-crucial for the business, and spent wastefully. The most capital efficient businesses tend to be frugal and have a low cost approach to the world. Salaries are lower to help make equity more valuable. The founders and employees of these businesses treat the dollar spent by the business as their own money (which it is, as they are shareholders in the business). They realize that profitability gives them infinite runway and enormous freedom on decision making and future path optionality.
Frugality has felt like a lost art over the last few years – hopefully it is recovered.
When to bootstrap?
Too few silicon valley (or NY or other cluster) based technology companies bootstrap.
If you can grow organically and optimally without hiring a massive team and increasing expenses it is great to do so!
If your company is a cash versus equity business, you should bootstrap.
If your company is growing slowly and will never hit venture scale, you should bootstrap.
When to raise money?
Venture capital is typically used to either:
Build out or prototype something. This may be something inexpensive but for some reason can not be bootstrapped off of customers (e.g. a new SaaS product) or is something capital intensive that may have a giant market on the other side. The later includes things like building rockets for spaceX, or biotech drugs.
Scale something that is working. For example, you want to add sales or go-to-market functions to sell faster/better, or your consumer app is growing like crazy and you want to be able to add more compute to serve users.
You need the valuation for external uses. E.g. M&A or hiring (there are other ways to do this too).
In general, if you are not prototyping / proving something works, or scaling something that does work, you should not raise money.
One could argue that while too many SV/NY/cluster-based tech companies raise money, too few outside of major tech clusters do. In many cities and regions people bootstrap for too long, do not scale quickly enough, or do not think about time to winning in a big market. It is possible that non-cluster tech companies in the US end up scaling fast too infrequently.
Occasionally, you also see an SV/NY/cluster-based company that is growing really well and has turned profitable, and then forgoes building against and winning in their category. Sometimes this is the right thing for founders to do, and sometimes it reflects a lack of know-how, ambition, or aggressiveness. Sometimes, it just shows the founders had a bad experience at a company that scaled for no good reason and ruined the company culture, ability to execute, and products. The wrong lessons may be learned from bad growth and bad execution. It is so rare to actually build something that people care about, that it feels like a shame to not go win when you can – but obviously it is up to each founder and team to chose their own path.
Generically, startups are rewarded for progress per unit time, versus progress per unit dollar (all else being equal within a given burn multiple range).
Hopefully more capital efficiency returns to technology now that ZIRP and COVID policies are (roughly) a thing of the past. Many of the most important companies in the world started in a capital efficient state.
 An emerging meme today is that with AI, the costs of starting a company are lower as you can augment people with AI from day 1. Past variations of this thought has occurred for many macro waves including distributed talent (“talent in India is cheaper, so your company can be too!”), cloud services (“cheaper to not build your own data center!”) and other shifts.
Notably all these shifts have impacted the costs of doing business. However, the impact tends to come with scale and the need for more people intensive operations. I am very bullish on e.g. AI applied to private equity and buyout models.
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