A concept I’ve found useful: executive clock speed — the rate at which the leadership of an organization can make decent (“80/20”) decisions that actually get executed.
Sam Altman used to compare how quickly YC’s best founders answered his emails versus the mediocre ones: “It was a difference of minutes versus days on average response times.” Email latency is a proxy for the founder’s OODA loop, the externally visible signature of internal decisionmaking.
As organizations scale, this becomes the binding constraint. Most decisions are 80/20: the cost of waiting a week for the optimal call exceeds the cost of being slightly wrong now. A slow executive forces the whole org to idle against their queue.
Some consequences of this model:
Co-CEOs are bad.Committees are terrible
Consistency matters more than peak quality
Founder responsiveness is a leading indicator of speed of the whole organization
Competition between different companies is more often decided by one company deciding & executing many more decisions than doing much smarter decisions. This is especially true if it easy to copy (technical expertise) - which is the default in most industries. I think this is often true—but not always.
How to speed up?
Observe: invest in measurement. Reports, dashboards, regular skip-levels.
Orient: filter the measurement intelligently. By default, most executives drown in reports, meetings, emails. Prioritization in what to look at is almost as important as what to decide on.
Decide: usually the culprit is perfectionism or fear. The 80⁄20 call now beats the 95⁄100 call in 90% of the time.
Act: ensure decisions actually propagate. A decision that doesn’t effectively reach the people executing it is identical to no decision. Apparently, Napoleon spend most of the day writing long and detailed instructions to his officers.
Someone (I think it might be Paul Graham) once said that they were always surprised how quickly destined-to-be-successful startup founders responded to emails—sometimes within a single-digit number of minutes regardless of time of day. I used to think of this as mysterious—some sort of psychological trait? Working with these grants has made me think of it as just a straightforward fact of life: some people operate an order of magnitude faster than others. The Manifold team created something like five different novel institutions in the amount of time it’s taken some other grantees to figure out a business plan; I particularly remember one time when I needed something, sent out a request to talk about it with two or three different teams, and the Manifold team had fully created the thing and were pestering me to launch a trial version before some of the other people had even gotten back to me. I take no pleasure in reporting this—I sometimes take a week or two to answer emails, and all of the predictions about my personality that this implies would be correct—but it’s increasingly something that I look for and respect. A lot of the most successful grants succeeded quickly, or at least were quick to get on a promising track. Since everything takes ten times longer than people expect, only someone who moves ten times faster than people expect can get things done in a reasonable amount of time.
Don’t think this particular bit follows from your other points, and think it’s contradicted by other anecdotal evidence about top performers in winner take all fields. If anything making more 80⁄20 decisions increases the number of bangers at the expense of average quality.
Email latency is a proxy for the founder’s OODA loop, the externally visible signature of internal decisionmaking… As organizations scale, this becomes the binding constraint… A slow executive forces the whole org to idle against their queue.
The hypothesis is broadly plausible for at least some organizations, but I want to flag that email latency could be causally decoupled from decision making rate while still being correlated with it. We’d expect a competent exec to respond quickly to a key funder’s emails, regardless of how quickly they make decisions.
Your framing implies that business progress is inevitably bottlenecked by the decision-making rate (“As organizations scale, [the executive decision-making rate] becomes the binding constraint.”). Yet we can think of situations where this is clearly not the case, at least within a span of time.
A small biotech company whose lead is in the middle of a clinical trial is in a “dead zone,” bottlenecked not by executive decision making but by practical or regulatory constraints on the rate at which patients can be dosed and evaluated.
A company that selects an overly complex problem may be unnecessarily bottlenecked by decision-making rate. If the executive had chosen an alternative strategy, perhaps by taking more time on a single, critical upstream strategic decision, they might have been bottlenecked by other factors, such as the rate at which a factory could be built or customers acquired.
Finally, in order to study decisions empirically to test these hypotheses, it would be necessary to operationalize them, which requires defining the dimensions of a decision, establishing the context that lets us evaluate the similarity of two decisions.
For example, is it reasonable to compare the rate at which two different types of companies—Apple and Lego, for example—finalize product decisions? Apple makes a relatively small number of high-tech products, while Lego makes a large number of different toys, including both individual blocks and lego sets. How could we evaluate the question of whether a company that has a similar decision-making demand to Lego makes decisions slower or faster than Lego, and whether or not it leads them to better outcomes?
If we can’t tractably evaluate these hypotheses, then how do we know that the rate of decision-making is more important than quality, or that it’s commonly the binding contraint on progress? Or is the claim more limited: that, ceterus paribus, making decisions more quickly is useful?
Executive Clock Speed
A concept I’ve found useful: executive clock speed — the rate at which the leadership of an organization can make decent (“80/20”) decisions that actually get executed.
Sam Altman used to compare how quickly YC’s best founders answered his emails versus the mediocre ones: “It was a difference of minutes versus days on average response times.” Email latency is a proxy for the founder’s OODA loop, the externally visible signature of internal decisionmaking.
As organizations scale, this becomes the binding constraint. Most decisions are 80/20: the cost of waiting a week for the optimal call exceeds the cost of being slightly wrong now. A slow executive forces the whole org to idle against their queue.
Some consequences of this model:
Co-CEOs are bad. Committees are terrible
Consistency matters more than peak quality
Founder responsiveness is a leading indicator of speed of the whole organization
Competition between different companies is more often decided by one company deciding & executing many more decisions than doing much smarter decisions. This is especially true if it easy to copy (technical expertise) - which is the default in most industries. I think this is often true—but not always.
How to speed up?
Observe: invest in measurement. Reports, dashboards, regular skip-levels.
Orient: filter the measurement intelligently. By default, most executives drown in reports, meetings, emails. Prioritization in what to look at is almost as important as what to decide on.
Decide: usually the culprit is perfectionism or fear. The 80⁄20 call now beats the 95⁄100 call in 90% of the time.
Act: ensure decisions actually propagate. A decision that doesn’t effectively reach the people executing it is identical to no decision. Apparently, Napoleon spend most of the day writing long and detailed instructions to his officers.
Relatedly, Scott Alexander’s ACX Grants 1-3 Year Updates:
Don’t think this particular bit follows from your other points, and think it’s contradicted by other anecdotal evidence about top performers in winner take all fields. If anything making more 80⁄20 decisions increases the number of bangers at the expense of average quality.
Rest seems accurate though.
It might matter more in this case due to being more predictable to others, e.g. reputation for reliability.
The hypothesis is broadly plausible for at least some organizations, but I want to flag that email latency could be causally decoupled from decision making rate while still being correlated with it. We’d expect a competent exec to respond quickly to a key funder’s emails, regardless of how quickly they make decisions.
Your framing implies that business progress is inevitably bottlenecked by the decision-making rate (“As organizations scale, [the executive decision-making rate] becomes the binding constraint.”). Yet we can think of situations where this is clearly not the case, at least within a span of time.
A small biotech company whose lead is in the middle of a clinical trial is in a “dead zone,” bottlenecked not by executive decision making but by practical or regulatory constraints on the rate at which patients can be dosed and evaluated.
A company that selects an overly complex problem may be unnecessarily bottlenecked by decision-making rate. If the executive had chosen an alternative strategy, perhaps by taking more time on a single, critical upstream strategic decision, they might have been bottlenecked by other factors, such as the rate at which a factory could be built or customers acquired.
Finally, in order to study decisions empirically to test these hypotheses, it would be necessary to operationalize them, which requires defining the dimensions of a decision, establishing the context that lets us evaluate the similarity of two decisions.
For example, is it reasonable to compare the rate at which two different types of companies—Apple and Lego, for example—finalize product decisions? Apple makes a relatively small number of high-tech products, while Lego makes a large number of different toys, including both individual blocks and lego sets. How could we evaluate the question of whether a company that has a similar decision-making demand to Lego makes decisions slower or faster than Lego, and whether or not it leads them to better outcomes?
If we can’t tractably evaluate these hypotheses, then how do we know that the rate of decision-making is more important than quality, or that it’s commonly the binding contraint on progress? Or is the claim more limited: that, ceterus paribus, making decisions more quickly is useful?