Knowledge collapse is the paradox where increasing access to certain types of knowledge actually harms understanding.

If you visualize public knowledge as a collection of viewpoints that fit a normal distribution, the “collapse” is when the head and the tail drop off and the center is amplified.

A recent paper found that a “discount on AI-generated content generates public beliefs 2.3 times further from the truth.” While the paper centers on LLMs, there could be many explanations for the phenomenon.

See also:

• Another explanation is that convenience is king and information that shapes ones views will tend to follow what’s convenient to access
• AI puts a higher premium on unique knowledge

Business strategy should concisely answer who the most import customers are, how to attract them, and what the entire company must work towards to win that market.

From What makes a strategy great:

Great strategies accomplish this with the following characteristics:

• Simple: Reshapes complexity to be manageable and actionable.
• Candid: Dares to spotlight the most difficult truths.
• Decisive: Asserts clear decisions and accepts their consequences.
• Leveraged: Magnifies strengths into durable competitive advantage.
• Asymmetric: Defeats uncertainty with higher upside than downside.
• Futuristic: Solves for the long-term.

Putting this into practice is difficult so I find that it’s useful to write a draft and then ask the list above as questions:

Is the strategy simple? People don’t expect much from simple ideas. Anything too complicated is a sign that a lot of things have to go right in order for it to work (execution risk). Elegance is a heuristic guide to truth.

What difficult truths does it reveal? Confronting the most challenging parts of the market, the customer persona, and the state of the world is important—at some point contact with reality must be made—better to be considered now than when putting the strategy into practice.

Is it decisive? If it sounds too wishy-washy it probably is. When will you decide if not now?

Does it compound in an obvious way? Look for elements of the strategy that build on itself and compound for long periods of time keeping in mind most of the benefits occur at the end. There also should be room to make many bets that have out-sized rewards with small effort.

Is it long-term and ambitious? Look for meaningful changes in the world if you are wildly successful and what must be true in order for that to happen. Build on that to take it one step further.

The big orange cones with steam coming out of them in Manhattan are from the leaking network of steam pipes that heat commercial buildings. The 142 year old system of pipes sometimes breaks and the steam shoots up from underground. The stacks help direct the steam up rather than fogging the streets and sidewalks.

Read Orange Steam Funnels Are a New York Symbol. What Are They For? by the NY Times.

See also:

• The steam system is a shared heirloom and on a scale that would be difficult (maybe impossible) to implement today
• Steam used to heat office buildings that was generated by cleaner energy is an example of an inter-generational partnership, even if it wasn’t conceived that way

A theory of how plants cooperate comes from research of how fungus connects tree roots together into common mycorrhizal networks (CMN). Scientists have found evidence that important nutrients are shared over CMN and can connect multiple species of plants together.

Some researches take this theory further. One idea is that a “Mother tree” supports it’s seedling kin by sending resources to them preferentially. Another idea is that plants cooperate, which conflicts with evolutionary biology which centers on competition.

A recent article in Nature Vol 627 discussed a recent review of the research and calls into question several of these ideas, most notably doubting the explanation of a “mother tree”. Evidence for the wood-wide-web are not as clear as the headlines may make them appear.

See also:

• In human networks, all bridges are weak ties
• It might be that multiple explanations at different levels of emergence—the mycorrhizal network is part of a lower level phenomenon that is not inconsistent with higher level explanations like “trees share with each other”

In a two party system you never want both party A and party B buffering at the same time because it creates artificial delays in the system.

As an example, let’s take a common setup between two teams and a ticketing workflow.

The ops team doesn’t want to bother the engineering team so they wait until they think there is a lull before reporting an issue. The eng team triages requests coming in into tickets and then the team prioritizes tickets before assigning and completing them.

The two teams are both buffering and the time it takes to resolve an issue is now the sum of:

• The time it takes to notice the issue
• The time waiting to report it (buffer)
• The time to make a ticket
• The time to prioritize a ticket (buffer)
• The time it takes to resolve the issue

With both sides buffering, an unpredictable amount of time is introduced into the system causing delays. In this example, it would be much more efficient to report issues immediately.

Now imagine several teams in a complicated system where tickets are passed and prioritized between them. Each stage is buffered, adding more time to the system, and decreasing throughput.

(This is also why I don’t think teams should introduce ticketing too early because it’s too easy to double buffer!)

See also:

• Automation reduces marginal cost of nonautomated tasks
• Conway’s Law shows us these communication structures leak out into what companies build
• Automating cooperation decreases the cost of coordination

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Between 2008 and 2021, the market was operating under zero-interest rate policy (ZIRP). That changed in 2022 back to historically normal interest rates (5-6%) set by the Federal Reserve.

Interest rates drive investment behavior. Low interest rate periods lead to risk taking for long-dated returns (venture capital, moonshots). High interest rates lead to short-term decisions which value revenue over growth.

For many founders, their entire career happened under ZIRP.

What adjustments need to be made?

Founders should probably not model or emulate the success of companies in the last 10 years. Many investments did not appear bad during ZIRP and malinvestment doesn’t become apparent until later. As Warren Buffett said, “Only when the tide goes out do you discover who’s been swimming naked.”

For example, it’s very unlikely a company like Uber or WeWork would happen in our current economic environment. Blitz-scaling seems less viable when the cost of capital is much higher.

Getting stuck between environments is existential for a startup. With the changing interest rate came suppresed valuations and founders might not clear the bar for their current stage and be unable to raise more capital. Many down rounds, layoffs, and closures happened as a result.

Building a large durable business was always important, but it wasn’t always essential. It used to be easier to make a lot of money without building a business.

Founders need to more tightly control expenses to keep up with expectations. Efficient growth with smaller teams is much more valuable now.

Of course there are counter examples. AI founders and investors clearly feel AI businesses are different as evidenced by the enormous amount of investments being made in the space.

I received a one question feature survey from Screen Studio and I thought it was the best survey I have ever received. I’m guessing the response rate was high because it was simple and relevant their user’s needs (which feature I want them to build).

Here’s the email:

To: me Subject: Future Screen Studio Features Body:

Hello Screen Studio users!

We would love to know which feature you would like to see next in Screen Studio. We value your time, so this survey has only ONE question.

Click here to take the survey

Your input will help us prioritize items on our roadmap.

Thank you! Screen Studio Team

The link goes to a Google form with one question (as promised), a list of features with a short description where you can only pick one option.

When employees lose their sense of agency they start to look outwardly as the source of their problems and solutions.

It’s easy to accidentally create and environment of learned helplessness. You can force people come to you for an answer rather than try to figure it out on their own first. You can recognize and reward people for status quo preserving behavior rather than solving important problems.

This is dangerous to a business because it leads to a culture of cynicism, a feeling that nothing will change. Once people start believing that, it becomes true.

The parable of the six foot man who drowned in the stream that was five feet on average is a reminder to plan for the possibility of low ends when building a portfolio. The average can be misleading because there can be large swings that still average out to something you might mistakenly believe is survivable.

An example Howard Marks shared in an investor memo was about commercial real estate investments that, on average would pay off handsomely but if the market turned, would be disastrous. As the economy shifted from a low-interest rate environment, many of these investments are in danger even though, at the time, they looked like reasonable bets.

See also:

• An average is a trend, not an explanation
• Investment strategies that only work in a bull market
• Zero interest rate policy (ZIRP)

Contempt undermines any rational argument you might make to convince another person of anything. People are not dumb, they can sense when the underlying tone is disparaging. The message gets lost in some interpretation that sounds like “you’re a bad person because you don’t believe my idea” which leads to defensiveness.

The way to convince people is to care. Genuinely caring about what the other person thinks and says can’t be faked. Nor can one’s objectivity and sense of fallibility. When you care, you naturally become more open to receiving other ideas (not necessarily believing, just not outright rejecting them) and so does the other person.

Next time you are trying to convince someone of something important, ask yourself, is this coming from a place of contempt?

Productive engineers should write one pull request per day. One pull request per day creates forward momentum. Forward momentum leads to progress. Progress is how teams overcomes their biggest challenges.

The reaction I get from engineers when I tell them they should write a PR per day is usually one of incredulity.

What about something that takes more than a day?

Big things can be broken down into smaller things. Breaking things down encourages rigorous thinking—if you can’t figure out what the next step is, it’s unlikely you understand the problem well enough.

Won’t this lead to people gaming the system, writing code without substance?

In a high trust environment, this seldom happens because the most effective people care a lot. Trust between managers and ICs is a prerequisite for high-performing teams, but even without that, it’s easy to spot this behavior and intervene.

Another way to prevent this behavior to avoid making the count of PRs a target (Goodhart’s law). It’s a guideline and should prompt discussion.

In the single-player scenario, fears of others gaming the system don’t matter. Do you want to be a high-performing individual contributor? It’s hard to do that without being productive.

What about staff engineers that need to spend a lot of their time talking to people, planning, and researching?

The unit of progress might be different for a staff engineer but the spirit is the same—make progress every day. Have you ever worked with a good staff engineer who isn’t productive?

What if the codebase is too complicated to write a PR per day?

Having worked in a very large and complicated codebase before, I deeply understand this concern. At the same time, some of the most productive engineers I’ve worked with in these organizations far outpaced 1 PR per day. Sometimes knowing that it is possible is enough. Sometimes it will prompt the right questions: what knowledge am I missing, what makes this difficult to contribute to, what’s holding us back?

See also:

• You could reduce this idea further to no zero days and getting started as a productivity hack
• Groups become less productive as they grow
• Sometimes the thing making everyone less productive is actually the speed of decision making

Interest rates are a technology that allows people to transact across time. Without the concept of interest, it’s only possible for two parties to transact with what is immediately in front of them, in that very same moment.

For example: two people could barter apples for cows at a marketplace with no problem, but what if the seller of apples wanted to use the cows for one month? How should the cow owner be compensated?

Over the course of a month, the cows have value that the owner forgoes. Maybe the apple seller will reimburse for the milk the cows produce during that time, but can only pay for it once the cows are returned. Why should the cow owner wait a month for money they could have been receiving right away?

To make it more enticing, the apple seller agrees to pay more than the value of the cows over that period. The apple seller paid interest—the price of time. Now they can transact over time and not just when they happen to both be at the market with their goods.

See also:

• Alcohol is technology for cooperation
• Most compounding interest benefits occur at the end
• Interest rate fallacy

Anthropologist Marilyn Strathern summarized Goodhart’s law most succinctly saying, “When a measure becomes a target, it ceases to be a good measure.”

If a measure becomes a target, it creates incentive to hit the target. This perverse incentive leads to behaviors that can be counterproductive.

Author Edward Chancellor neatly summarizes why interest rates are so important, “Interest rates are the price of time.” Since time is a consideration in every financial transaction that makes up the economy, interest rates are a foundational concept to understanding behavior.

I heard this in a podcast about Easy Money, the most recent memo from Howard Marks.

See also:

• The interest rate fallacy shows us how availability of credit is associated with interest rates
• Lowering the federal funds rate causes all asset classes increase in value
• When the price of time changes it large impact on the economy so the best way to protect against the downside by enjoying the upside

Astronomers found that Mimas has an ocean underneath it’s surface by observing and modeling it’s orbit. They found a backwards precession in the moon’s orbit meaning the shape of the elliptical orbit rotates backwards. It was previously believed the core of Mimas was solid, but if that were true, the orbit would not have a precession. After additionaly modeling, the only explanation is that it must be filled with water under the surface.

Fascinating!

From Nature Vol 626.

A study of over 100,000 people in England found that found that cognitive deficits from COVID-19 infection (memory, reasoning and executive function) were similar between those whose symptoms resolved in less than 4 weeks and 12 weeks but were larger in those who had unresolved persistent symptoms (long COVID).

IQ loss from COVID-19 infection:

Read Cognition and Memory after Covid-19 in a Large Community Sample.

See also:

• List of studies about Long COVID
• Cognitive slowing in people diagnosed with long covid
• 14% of US adults have had long covid

In Money AI Bubble, the author argues that the market is in an AI bubble. Dumb money is pushing stock prices up despite any real improvement in their businesses, and this will eventually lead to losses. As the author contends, most of this is actually an Nvidia bubble.

The AI bubble isn’t due to advancements in software but advancements in hardware. We are riding on the coattails of hardware (GPUs) which have real costs/materials/environmental impact. (I saw the connection from a comment on HN).

The algorithms (software) are getting better, but the long pole is the training, the enormous storage, and compute needed to create LLMs. The amount of money, infrastructure, manufacturing, environmental impact etc., needed to build large-scale generative AI is staggering (and being gobbled up mostly by Nvidia). This also makes development prohibitive to only the largest and best funded companies (leading to a VC bubble?).

Our understanding and explanations of intelligence are no closer to the knowledge needed to create the most useful applications of artificial intelligence (self-driving cars, scientific research). What if we didn’t get closer to AGI? What if we just found what’s possible with a particular kind of fast parallel computation made possible by years of advancement in hardware?

I watched an interview with Susan Rogers, sound engineer for Prince, and she described Prince’s prolific production process. “His music would come out like a sneeze,” she said.

Not only is Prince the most productive musician of all time, the music came to him all at once and then it was done. Another interview with Peggy McCreary, another sound engineer that worked with Prince, talked about how he would record an entire song in a day and most tracks with the first take.

Prince’s process was remarkable when you compare it to modern recording artists who meticulously record take after take to achieve perfection. Prince would leave the mistakes in.

Maybe our creative processes need to be more like a sneeze—suddenly and all at once.

See also:

• When you have the inspiration, act right away
• A different approach is Apple’s constant iteration and critiques described in Creative Selection
• A cathedral for creation a bazaar for growth

Institutions end up perpetuating the problems they aim to resolve, either intentionally or unintentionally. For example, TurboTax addresses the issue that taxes are too complicated for individuals to handle independently and then lobbies Congress to hinder efforts to simplify tax preparation for individuals.

This same dynamic occurs within larger organizations as well. Teams devoted to solving a problem may resist a superior solution that emerges, fearing that their role will become unnecessary.

See also:

• We often see status quo preserving behavior because change is painful
• Is this another kind of tragedy of the commons?
• US workers are increasingly working for larger enterprise companies
• Managerial capitalism makes startups possible
• Peter Principle
• Baumol’s cost disease

One way to measure product market fit is to look at the time it takes between putting a product out in the market to the first $1MM in ARR.

Lenny’s Newsletter says 1 year from launch to $1MM ARR is considered good, 9 months is great.

Of course, there are plenty of examples of bad companies growing quickly and collapsing just as fast, but it’s a useful measuring stick, especially when you look at graphics like this:

See also:

• SaaS revenue per employee benchmarks for B2B companies
• Invest in lines not dots

According to a report by Common Paper of 1,000 businesses, commercial agreements take a median of 3 to 4 days to sign depending on the size of the company. Enterprise companies take longer (as expected), but the amount is only 40% longer than SMBs.

Based on personal experience, this feels way too low.

A collection of benchmarks for B2B businesses (mostly relevant for early-stage SaaS).

• Metrics
  • Churn
  • Time to sign
  • Revenue per employee
  • Burn multiples
  • Sales win rate
  • Time to first million ARR
• Fundraising
  • Time between Series A and Series B is 31 months on average
  • SaaS benchmarks for Series B
• Valuation multiples
  • Startup multiples fell from 100 to 39 times ARR in 2023
  • Revenue multiples fell from 24x in 2021 to 10x in 2022

If you are running a B2B business, you need to pay attention to anecdotes more than data.

Companies tend to become data oriented about their customers as they get larger. It becomes unwieldy to think about individual cases so they are summarized into statistics and trends.

This is a bad thing when a small number of customers are going to end up being the most valuable (which is the case for most B2B businesses). If, for example, you dismiss problems because “only a small number of users have it” you might miss an important insight from your best or, someday your best, customers.

This is distinctly different from B2C businesses where users have less variance in their value to the company like a social network. Those businesses, necessarily need to think about the nominal user. However, applying similar techniques and adopting a similar approach to B2B products is deeply flawed.

I borrowed some of these ideas from A Business State of Mind on the the Colossus podcast.