Elad Blog 09月25日
科技公司的资本效率
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过去五十年中,每一波技术驱动的公司浪潮中都有高度资本效率的企业。这种效率反映了市场需求和创始人节俭的管理。微软、戴尔、谷歌等公司早期都保持了资本效率。高效资本利用通常源于产品/市场契合度高,竞争少,以及公司运营节俭。在COVID期间,许多科技公司过度扩张,而高效企业则保持低成本。是否融资取决于是否需要原型开发、规模化或外部估值,而非盲目扩张。

💡 高度资本效率通常源于产品/市场高度契合,竞争少,客户愿意支付高价,反映了强烈的市场需求。

📊 成本效率高的公司往往管理节俭,创始人将公司资金视为自己的钱,注重盈利以获得无限发展空间。

⏳ 过去五十年中,许多大型科技公司如微软、戴尔、谷歌早期都保持了资本效率,通过有机增长而非过度融资。

🚫 近年许多科技公司因资金自由流动而过度扩张,而高效企业则保持低成本,避免非核心支出浪费。

🔧 融资时机需谨慎,通常用于原型开发、规模化或外部估值,而非盲目扩张,否则可能错失市场机会。

For the last 5 decades[1], 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:

Why are some businesses capital efficient?

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. Uber needed to raise billions to both scale rapidly and beat out global competition.

    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.

The dangers of capital efficiency

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. Some companies that focus on or hit profitability early then forgo winning the market - their focus shifts too much on maintaining cash flow versus growing faster to take the market.

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.

NOTES

[1] 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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