Stored Hashcash

One of the greatest inventions in the history of computer security is Hashcash. Internet blights like spam and denial-of-service attacks are what economists call “tragedy of the commons” problems. They exploit the fact that it’s free to send email and make web requests. At zero cost, you can have a profitable business even at extremely low success rates.

One way to fix these problems is to impose tariffs that hurt bad actors without hurting good actors. For example, you could impose “postage fees” on every email and web request. Unfortunately, in practice, this is impossible, because you’d have to set up billing relationships between every computer that wants to communicate.

The brilliant idea behind Hashcash is to replace a monetary postage fee with a computational postage fee. In order to send an email, the sender first has to solve a math problem. Legitimate activities suffer an indiscernible delay, but illegitimate activities that require massive volume are hobbled.

Hashcash is a great idea, but cumbersome in practice. For example, the cost imposed on senders varies widely depending on the performance of their email servers. It also hinders legitimate bulk emails like clubs and retailers sending updates to their mailing lists.

The offline analogy to Hashcash is a postal system where senders are required to perform some work every time they want to send something. If you’re a lawyer, you need to practice some law before you send mail. If you’re a doctor, you need to cure something before you send mail. Etc. This of course would be a preposterous postal system.

Adam Smith called money “stored labor“. You do your work and then store your labor as money, which you can later exchange for labor stored by other people. Storing labor in the form of money turns out to be a very flexible system for trading labor, and far superior to the barter system of performing work whenever your counterparty performs work.

So Adam Smith’s version of Hashcash is a system where you get credits for doing computation. You store your computational credits and spend them at your leisure. If you want to send an email, you can spend a little stored Hashcash. If I send you an email and you reply, we’re even. If you send out a billion spam emails, it costs you a lot and undermines your spammy business model. 

There are other important problems that stored Hashcash could solve. Denial-of-service attacks are spam attacks except they happen on HTTP instead of SMTP and the payoff is ransom instead of spam offers. Computer scientists have long believed that pricing schemes could dramatically reduce network congestion. Like every large-scale distributed system, the Internet benefits when scarce resources are efficiently allocated.

It seems plausible that if a system like stored Hashcash were developed, some people would prefer to purchase stored Hashcash directly instead of generating it themselves. A market for stored Hashcash would emerge, with the value being some function of the supply and demand of scarce Internet resources.

So here’s my question: suppose someone invented a way to store Hashcash. It could dramatically reduce spam and denial-of-service attacks, and more efficiently allocate network bandwidth and other Internet resources. How valuable would stored Hashcash be?

The computing deployment phase

Technological revolutions happen in two main phases: the installation phase and the deployment phase. Here’s a chart (from this excellent book by Carlota Perez via Fred Wilson) showing the four previous technological revolutions and the first part of the current one:

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Each revolution begins with a financial bubble that propels the (irrationally) rapid “installation” of the new technology.  Then there’s a crash, followed by a recovery and then a long period of productive growth as the new technology is “deployed” throughout other industries as well as society more broadly. Eventually the revolution runs its course and a new technological revolution begins.

In the transition from installation to deployment, the bulk of the entrepreneurial activity moves “up the stack”. For example, in the installation phase of the automobile revolution, the action was in building cars. In the deployment phase, the action shifted to the app layer: the highway system, shipping, suburbanization, big box retail, etc.

This pattern is repeating itself in the computing/internet revolution. Most of the successful startups in the 90s built core infrastructure (e.g. optical switching) whereas most of the successful startups since then built applications on top of that infrastructure (e.g. search). The next phase should see startups higher in the stack. According to historical patterns, these would be ones that require deeper cultural change or deeper integration into existing industries.

Some questions to consider:

– What industries are the best candidates for the next phase of deployment? The likely candidates are the information-intensive mega-industries that have been only superficially affected by the internet thus far: education, healthcare, and finance. Note that deployment doesn’t just mean creating, say, a healthcare or education app. It means refactoring an industry into its “optimal structure” – what the industry would look like if rebuilt from scratch using the new technology.

– How long will this deployment period last? Most people – at least in the tech industry – think it’s just getting started. From the inside, it looks like one big revolution with lots of smaller, internal revolutions (PC, internet, mobile, etc). Each smaller revolution extends the duration and impact of the core revolution.

– Where will this innovation take place? The historical pattern suggests it will become more geographically diffuse over time. Detroit was the main beneficiary of the first part of the automobile revolution. Lots of other places benefited from the second part. This is the main reason to be bullish on ”application layer” cities like New York and LA. It is also suggests that entrepreneurs will increasingly have multi-disciplinary expertise.

Agency problems

Agency problems” are what economists call situations where a person’s interests diverge from his or her firm’s interests.

Large companies are in a constant state of agency crisis. A primary role of senior management is to counter agency problems through organizational structures and incentive systems. For example, most big companies divide themselves into de facto smaller companies by creating business units with their own P&L or similar metric upon which they are judged. (Apple is a striking counterexample: I once pitched Apple on a technology that could increase the number of iTunes downloads. I was told “nobody optimizes that. The only number we optimize here is P&L in the CFO’s office”).

If you are selling technology to large companies, you need to understand the incentives of the decision makers. As you go higher in the organization, the incentives are more aligned with the firm’s incentives. But knowledge and authority over operations often reside at lower levels. Deciding what level to target involves nuanced trade offs. Good sales people understand how to navigate these trade offs and shepherd a sale. The complexity and counter-intuitiveness of this task is why it’s so difficult for inexperienced entrepreneurs to sell to large companies.

Agency problems also exist in startups, although they tend to be far less dramatic than at big companies. Simply having fewer people means everyone is, as they say in programming, “closer to the metal”. The emphasis on equity compensation also helps. But there are still issues. Some CEOs are more interested in saying they are CEOs at parties than in the day-to-day grind of building a successful company. Some designers are focused on building their portfolio. Some developers are only interested in intellectually stimulating projects. Every job has its own siren song.

One of the reasons The Wire is such a great TV show is that it shows in realistic and persuasive detail how agency problems in large organizations consistently thwart well intentioned individual efforts. The depressing conclusion is that our major civic institutions are doomed to fail. Those of us who are technology optimists counter that the internet allows new networks to be created that eliminate the need for large organizations and their accompanying agency problems. Ideally, those networks recreate the power of large organizations but operate in concert like startups.

The economic logic behind tech and talent acquisitions

There’s been a lot of speculation lately about why big companies spend millions of dollars acquiring startups for their technology or talent. The answer lies in the economic logic that big companies use to make major project decisions.

Here is a really simplified example. Suppose you are a large company generating $1B in revenue, and you have a market cap of $5B. You want to build an important new product that your CTO estimates will increase your revenue 10%. At a 5-1 price-to-revenue ratio, a 10% boost in revenue means a $500M boost in market cap. So you are willing to spend something less than $500M to have that product.

You have two options: build or buy. Build means 1) recruiting a team and 2) building the product. There is a risk you’ll have significant delays or outright failure at either stage. You therefore need to estimate the cost of delay (delaying the 10% increase in revenue) and failure. Acquiring a relevant team takes away the recruiting risk. Acquiring a startup with the product (and team) takes away both stages of risk. Generally, if you assume 0% chance of failure or delay, building internally will be cheaper. But in real life the likelihood of delay or failure is much higher.

Suppose you could build the product for $50M with a 50% chance of significant delays or failure. Then the upper bound of what you’d rationally pay to acquire would be $100M. That doesn’t mean you have to pay $100M. If there are multiple startups with sufficient product/talent you might be able to get a bargain. It all comes down to supply (number of relevant startups) and demand (number of interested acquirers).

Every big company does calculations like these (albeit much more sophisticated ones). This is a part of what M&A/Corp Dev groups do. If you want to sell your company – or simply understand acquisitions you read about in the press – it is important to understand how they think about these calculations.

The time to eat the hors d’oeuvres is when they’re being passed

The efficient market hypothesis is a widely taught financial theory that states, roughly, that under certain generally-held conditions, asset prices are an accurate reflection of the information available at the time. The arguments underlying it are mathematically elegant and have been widely popularized. Its hardcore proponents argue that financial bubbles do not (indeed cannot) exist and that government intervention in financial markets is unnecessary. While efficient market theory is dominant in academic circles, it is very hard to find active participants in financial markets who believe in it. In financial markets – like most complex human systems – the closer you get, the more nuance you discover.

Venture capital markets are perhaps the most inefficient of mainstream financial markets. Complicating factors include: heavy reliance on comparables for valuations, desire of VCs to be associated with “hot” companies, tendency to overreact to macro changes, illiquidity of startup financings, illiquidity of financings for VCs themselves, perverse financial incentives of VCs, inability to short stocks, extreme uncertainty of startup financial projections, vagaries of the M&A market, dependency on moods of downstream investors, concentration of capital among a small group of VCs, the difficulty of developing accurate financial models, rapid shifts of supply and demand across sectors and stages, and non-uniform distribution of accurate market data.

The title of this post is an old venture capital adage (via Bill Gurley) that reflects a hard-earned truth about financing and M&A markets. For social consumer startups, the hors d’oeurves were being passed in the build up to the Facebook IPO. They are being passed now for B2B and e-commerce companies. In the M&A markets, the most extreme example is probably in adtech, where there were waves of acquisitions in ad exchanges (DoubleClick, RightMedia, Avenue A), then mobile ads (AdMob, Quattro), and then social advertising (Buddy Media, Wildfire). If you didn’t sell during these M&A waves, you’re suddenly stuck with lots of powerful competitors and few potential acquirers/partners.

It is common to hear entrepreneurs say things like “I am waiting 6 months to raise money/sell the company, when we’ve hit new milestones.” Of course milestones matter, and companies are ultimately valued based on fundamentals. But along the way you’ll likely need capital and sometimes need to exit, and for that you are dependent on highly inefficient markets.