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Does value accrue at the protocol layer or the application layer? Value creation is not the same as value capture. The prevailing thesis has been one of Fat Protocols (2016) / Thin Applications (2020).

But large vertically integrated companies may have just enough to make it worthwhile, because they can hold on to and use more of the benefits of new discoveries that smaller firms would not be able to capture, even with robust intellectual property protections.

Historical success with labs has involved blends of different expertise, for example the team of physicists, metallurgists, and chemists who developed the transistor at Bell Labs.

Labs, compared to university researchers, also maintain a constant link with delivering value and, ultimately, profitability.

Prof. Arora and collaborators think this return to R&D is driven by fears of a new wave of anti-tech antitrust enforcement: Google and Facebook invest in research because buying it through acquisitions has become more difficult legally. But the case for the opposite is just as strong: they attract antitrust ire because their internal investment has paid off and they have taken huge shares of various markets on the back of it. In this opposite story, a small recent return to R&D labs would come down to the long term effects of the relatively weaker antitrust enforcement seen since the 1980s.

It’s obvious that a scenario where Xerox is paying scientists to do research that ultimately mostly benefits other firms, potentially even competitors that help to put it out of business, could never survive. Similarly, the tension between managing scientists with their own pure research goals in such a way that they produce something commercially viable, while still leaving them enough latitude to make important leaps, seems huge. But these problems were always there in the model.

In the absence of large firm innovations we now have an innovation system where start-ups and small teams, whether private sector or academic, do most early stage innovation. These teams then sell their work to larger ventures, enabled by the patent system, are acquired wholesale, or more rarely scale up, funded by venture capital, to become large businesses themselves.

By 2006, 6% of the awards were going to firms in the Fortune 500. The great majority of these awards are now being won by federal labs, university teams, and spin-offs from academia. The lone inventor is back.

In the years since my first reading of Principles, I've found there to be a difference between what is useful and what is factually accurate. (For philosophy nerds, this is the difference between the ‘pragmatic’ theory of truth with the ‘correspondence’ theory of truth.)

Dalio's book is very much oriented around what is useful, not necessarily what is objectively true. T

The method that reality uses to teach you its rules is that it causes you pain. Your partner dumps you; the job you desire rejects your application outright.

Principles presupposes that we cannot change the hand we're dealt, but we can learn to play it well.

Don't get hung up on your views of how things should be, because you will miss out on learning how they really are.

When I began to look at reality through the perspective of figuring out how to really works, instead of thinking things should be different, I realised that most everything that at first seemed “bad” to me—like rainy days, weaknesses, and even death— was because I held preconceived notions of what I personally wanted.

Pain + Reflection = Progress. Therefore, Dalio argues that you should learn to see pain as a signal for reflection. It is too much to ask for most of us to recognise pain during the event that causes us pain. But it's enough if we can look back on periods of pain in our recent past as opportunities for us to grow. Therefore: learn to go to the pain rather than avoid it.

Don’t worry about looking good — worry instead about achieving your goals.

The tricky thing about life decisions is that what is good as a first-order consequence might be bad as a second and third-order consequence.

My previous company was stuck in the ‘SME-loop’, because we found it difficult to stop customisation work (immediate money!) in favour of proper general product development (scalable business model!).

Don't blame bad outcomes on anyone but yourself.

My point is simply this: Whatever circumstances life brings you, you will be more likely to succeed and find happiness if you take responsibility for making your decisions well instead of complaining about things being beyond your control.

My perspective is that good execution mostly comes from experience and skill, not from simply trying harder. This is why investors often bias towards people that have worked in hyper-growth startups in the past. If you don’t know what “good” looks like, you will have a hard time replicating it and might prematurely conclude you have a bad plan when actually you just didn’t execute it well enough.

The problem is of course that you have no idea what the risk-adjusted return is, because at the end of the day you’re pulling numbers out of your ass, and the framework doesn’t give you any leverage to come up with better numbers. All it does is quantify your prior beliefs, but what we really want is a method to come up with new, better beliefs.

The output of a system is determined by the bottleneck. If you want to improve the output, you need to attack the bottleneck until it no longer is the limiting factor, and something else is. Rinse and repeat.

The Theory of Constraints tells us if we want to improve our system, one of these steps is going to be the critical “limiting factor.” In other words, no matter how much we improve the other steps, it won’t make a big difference because all the improvements are being held up by the bottleneck.

It’s beyond the scope of this article to get to the bottom of exactly what Every should do to get more people to read our articles, but the reason I wanted to show this is to illustrate the type of thinking you can do about your own business, using the Theory of Constraints.In a way, this is where strategy meets execution. Deciding what to execute better on is a strategic decision. It’s also an art. There is a ton of uncertainty, so it’s better to move fast and try things than to debate endlessly.

In Dropbox’s case, their version of success early on was actually a fairly different business than B2B enterprise sales. The things you need to do to make the product better for businesses would actually make it worse for consumers. The entire product philosophy might have to change: more options, more settings, more complexity.

Software companies founded today are competing less with pen and paper than with other Internet-first incumbents. Put another way, as happens in every maturing industry before it, Internet company revenue will become zero-sum. As a corollary, the time between founding years of software startups and their competitive incumbents is shrinking:

When the ecosystem-level diseconomies rival the company-level economies of scale – “first to scale” may become “first to fail”. Unit economics matter more than ever. Carefully measured growth will win.

Relative to the R&D-driven growth of early Internet companies, SG&A will become the primary growth vector in the 2020s.

Paul Graham tweeted that “a visitor who walks around and is impressed by the magnitude of your operation is implicitly saying “Did it really take all these people to make that crappy product?”” But this observation is fixated on a world where R&D dominated Internet startup headcount, and small teams were preferable.

As Alex Danco highlighted in his recent article Debt is Coming, it is clear that recurring revenue securitization – the notion of selling your future ARR bookings at a discount – is the future. The biggest barrier to adoption is cultural: the stigma that “venture debt is like a delicious sandwich that only costs ten cents, but occasionally explodes in your face” is deeply tied to the predatory reputation of old-school venture debt lenders. Companies like Pipe and Clearbanc are already starting to destigmatize securitization, and it will only become more culturally normalized in the coming years.

Once Sand Hill Sachs exists, it will become clear that VC dollars should be reserved for R&D, not S&M or G&A.

For startups taking R&D risk in new technological areas, the founding team may look like something we can’t pattern match to historical successes. Maybe it’s a scientist in his garage who escaped the tendrils of academia. Or your first hire for the founding team is no longer your college roommate, but an expert in your startup’s industry.

There certainly will be $10 billion dollar companies started within segments slow to adopt technology: legal tech, construction, agriculture, and mining are all prime candidates for massive new technology entrants. But new $100 billion dollar outcomes are less likely to come from pure Internet companies.