A common pattern for launching products and companies is to start with an invite-only beta. There will be users that sign up that are a better fit or have a more burning need for your product. Giving them a way to skip the line can help you filter for the right kind of user and take advantage of their current attention. For example, “reply to this email if you need to do {important indicator of demand} right away and we’ll bump you up the list”.

The reductionist view of science is that all high-level behavior consists of the underlying lower-level behavior and should be analyzed into components to fully understand. However, good explanations can be self-contained and sufficient without needing an explanation of every low-level detail. For example, you can have a theory of how water boils that doesn’t need to predict movement of individual atoms.

See also:

• Rejecting explanations because they do not explain the lowest-level behavior is like the inverse of parochial errors, going so deep that nothing can be explained
• Multiple explanations at different levels of emergence is not inconsistent and abstractions are real
• The Beginning of Infinity

A reductionist argument against an explanation might be that it is incorrect because there are multiple explanations of the same phenomena. If good explanations are hard to vary, how could there be multiple explanations? This argument doesn’t take into account that multiple explanations can exist at different levels of emergence and this is not altogether inconsistent.

See also:

• The Beginning of Infinity

The real world and it’s behaviors are extraordinarily complex. To theorize and create good explanations necessarily requires some encapsulation of ideas through abstractions. It is possible to understand a phenomena by understanding abstractions and similarly, it is possible to create new explanations by building on top of them.

Knowledge can grow because abstractions are real. It’s not as if every child going to school must learn all underlying knowledge in order to add to it. It’s also not as if explanations are implicitly wrong if all the lower-level behavior is not explained first.

See also:

• Seeking good explanations is error correcting so if abstractions end up being wrong, better ones can be found
• The Beginning of Infinity

The selloff and resulting crash in cryptocurrency prices on Jan 21, 2022, which wiped out over $1 trillion from the major coins, showed that cryptocurrency behaves like any other risky asset in the market. The plummet coincided with a stock market decline, nullifying the point that cryptocurrency is some sort of bastion from traditional markets.

See also:

• Stocks tend to go down when bond yields go up as risk-averse investors can get safer returns
• This cryptocurrency decline could get worse with looming federal funds rate hikes

Economists and financial analysts often assume high interest rates are associated with tight monetary conditions and, conversely, low interest rates are associated with easy money. In reality they indicate the opposite because of the supply side of credit.

For example, with low interest rates, there needs to be willing providers (banks providing loans), but low interest rates implies low returns. That’s why there is a positive correlation between interest rates and actual credit issued—there is greater supply when interest rates go up because of better returns.

Milton Friedman’s interest rate fallacy says that lower interest rates are indicative of “tightening money” rather than “easy money”.

Read Milton Friedman’s Interest Rate Fallacy in Monetary Mechanics.

See also:

• Lowering the federal funds rate causes all asset classes increase in value
• The stock market boom during the pandemic is due to increased savings
• Nobody grades an economist

When two people have competing ideas (let’s call them idea A and idea B), it’s common to compromise somewhere between idea A and idea B (let’s call that idea C). The problem is neither person thought idea C would work to begin with otherwise they would have argued for it. Worse, both sides will harbor some resentment that idea C doesn’t work. Compromise leads to the worst outcome—a third idea that doesn’t work and resentment from both people.

See also:

• Compromise leads to more tech debt since tech debt tends to be how teams and managers compromise
• Vaguely right is better than exactly wrong, the compromise option is likely exactly wrong if neither person argued it

Literary notes from reading Monetizing Innovation.

Product failure is rooted in failure to put the customer’s willingness to pay for a new product at the core of product design.

There are four kinds of failures

• Feature shocks are when there are too many features that don’t deliver a lot of value. They are nice to haves not gotta haves. The root is usually too much inside out thinking. Examples include the fire phone and the useless features like spatial 3D.
• Minivations occur when the product is underpriced for the value it generates. This underestimation leads to what can look like a success, selling out and second hand markets, but they are actually a failure to capture the potential upside. Cost plus pricing can be comfortable, but does not consider total value to the customer. Examples include park assist that was marked up 4x by car companies.
• A hidden gem is when you don’t know what you have until someone else does it. This usually occurs when there is lack of transparency (no one wants to risk new ideas) or when there is a large existing business (e.g. not our culture). Examples include Kodak who had the digital camera a decade before anyone else.
• Undeads occur when a product is an answer to a question no body is asking or the wrong answer to the right question. For example Google glass or Segway.

Having the willingness to pay talk.

Discuss value, don’t need to say pay. There are five methods to help drive the discussion:

• Direct questions: what do you think is an acceptable price? What would be too expensive?
• Purchase probability questions: show the product concept and price and ask on a scale from 1 to 5 how would you rate. If 3 or less lower the price and ask again.
• Most/least questions: show a set of ten features then groups of six and ask what is most valuable what is least valuable? Repeat until you exhaust combinations.
• Build your own questions: give customers a feature list and ask them to build their ideal product. Adding features increases the cost so they need to make tradeoff.
• Purchase simulation: show a product with a specific set of features and a price. Ask if they would buy it and look for their reactions. Show 5-8 combinations. This helps estimate the value and willingness to pay for each feature.

Remember to ask why. Look at distributions not averages.

Segmentation

• Segments should break down the market into different groups on which you can act differently
• Pressure test your findings—are there features one segment wants strongly that the others do not? Can salespeople sort their clients into the segments you came up with?

Product configuration Based on the segments and willingness to pay, configure different configurations to match based on what they value.

You must have the guts to take features away. You must resist giving away value added features to please customers.

Limit the number of offerings to a small number otherwise it’s overwhelming to customers who now need to choose. This also makes each offering more distinct and less likely to cannibalize sales.

Each offering should have less than 9 benefits or 4 bundled products to avoid cognitive overload.

Leaders, fillers, and killers Go through your benefits and features and classify them by segment and by designation of value

• Leaders drive customers to buy
• Fillers are of moderate importance or nice to haves
• Killers are feature that will kill the deal if customers are forced to pay for them (these are usually of little importance except a select few who find value in them)

Good, better, best This is the most common (dropbox style) and it helps sell people on the middle offering because people avoid extremes. Sales people can switch between the better and best offering depending on whether the customer is more price conscious or quality oriented.

If 50% of your customers buy the entry level you are giving too much away.

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An inverse relationship exists between group size and productivity which shows that group effort does not necessarily lead to increased effort from the group members.

In an experiment, a group was asked to pull a rope. As more people were added, the average performance significantly decreased. This seems to show that each participant felt their own efforts were not critical and further studies showed that motivational losses were largely to blame for an individual’s decline in performance.

See also:

• Baumol’s cost disease is similar in that salaries can rise without any material gain in productivity simply salaries of other roles went up.
• Larger informal groups can have other negative consequences such as opaque decision making and lack of accountability.

While remote work is obviously better for reducing exposure to COVID-19 than going to the office, the rapid increase in Omicron cases shows that remote work has little impact on reducing Omicron case counts. Anecdotally, several people I’ve worked with over the last two weeks have gotten it—all vaccinated and working from home.

Of the people that work from home, exposure outside of work is leading to infection. How should remote-first companies keep their workers safe when the risk comes from outside of work?

See also:

• Current vaccines are 33 percent effective against Omicron (more if you get a booster)
• I doubt that “getting sick less often” is top of the reasons for wanting to work remotely e.g. not having to commute for five hours is equivalent to a 10 percent raise and life balance

A popular view of the environment, “Spaceship Earth”, is that the planet provides just the right biosphere to support human life. That is misleading because humans are actually ill suited to living in most places. Take for example living in New York—you would freeze to death come winter if not for shelter, clothing, access to clean water, and food. This is technology that humans created to transform inhospitable environments into systems that support human life.

Because humans can use explanatory knowledge and conjecture to create new and better explanations, humans are the only species that is not bound to a range of environments. We can even live in the harshest environments like the vacuum of space which has no air, is astoundingly cold, and devoid of life.

See also:

• The Beginning of Infinity

From 2017 to 2021 the median size of Series A funding rounds grew from $7MM to $13MM. In the last year, round size increased by 30% ($10MM to $13MM). Other round sizes are increasing too—most notably the median Series D round increased 92% year over year ($52MM to $100MM).

Read Startup Round Inflation Skyrocketed In 2021.

If you sell to startups you should expect churn (they go out of business) and if you happen to catch the next big startup, you’re business model needs to be uncapped to grow along with it. For example, Stripe worked with Shopify when they were a small nascent startup, Checkr worked with Uber, and so on. They were able to grow their business because they grew with the volume of transactions.

From a conversation with Jonathan Ehrlich, Partner at Foundation Capital.

Every financial transaction, no matter how simple or fast, has non-monetary transaction costs. For example, the mental overhead of making a decision “is it worth it?”, even for tiny amounts, adds up. In a world that is fully monetized and filled with micro transactions, the non-monetary transaction costs would be stifling.

Having services available to everyone, paid for by tax dollars, decreases these costs. No one needs to weigh the cost of going for a walk in the park or whether or not they want to flush their toilet.

Read Web3 had better not be Transaction Cost Hell from Noah Smith.

See also:

• Web3 and the tokenization of everything has the potential to greatly increase non-monetary transaction costs
• Would a libertarian corporate charter city have this problem imminently?
• Decision fatigue leads to bad decision making

Richard Feynman said about science that, “The first principle is that you must not fool yourself, and you are the easiest person to fool.”

There are a plethora of ways in which one can fool themselves and strong discipline is required to understand how we know what we know.

A parochial error happens when you falsely believe that something in your narrow view of the world applies more broadly than it does. For example, thinking the seasons everywhere around the earth in the same way as your home town because that’s what you personally experience.

Pursuing good explanations would help correct parochial errors because good explanations are hard to vary. You could refute the explanation that the seasons are the same everywhere right away after meeting someone from Australia and then search for a better explanation to correct the error.

See also:

• The easiest person to fool is yourself and parochial errors are a good way of doing that
• The Beginning of Infinity
• Epistemology

The original idea of the web was that everyone would be both a producer and a consumer. They would run their own server and connect to the servers of others.

It’s difficult to run your own server. You need to figure out how to get it working. You need networking knowledge to connect it. You need to keep it up to date with new versions of software and security updates. You might even need to scale it which requires even more expertise.

What we learned from Web2 is that no one wants to run their own server—even those with the technical skills to do so. We would rather have someone else figure out how to keep it running all the time and pay them to host our website or content.

Read Web3 First Impressions by Moxie Marlinspike.

See also:

• This is a reason why centralized platforms are popular—convenience is king
• Large centralized platforms have outsized power which raises demand for decentralized systems
• Web3 suffers from the same problem, no one hosts their own blockchain and so you end up with centralized platforms all over again

Blockchains are a server technology. They don’t live on the client and things like a web frontend to a dApp can’t perform CRUD operations without a server. While it’s possible to host your own node, in reality nobody wants to run their own server, not even the ones with the technical skills to do it.

Web3 developers building a frontend to their dApps end up using a platform like Infura to provide web APIs that proxy operations to the underlying blockchain. This contradicts the whole point of a being trustless because there’s now a few centralized platforms (private companies) that need to be trusted and relied on.

Read Web3 First Impressions by Moxie Marlinspike.

The process of seeking out good explanations is error correcting. It is tolerant of dissent with a healthy dose of skepticism and distrust of authority. It means that explanations are rejected when they are contradicted by better explanations.

See also:

• Error correction in science can take some time as outdated scientific studies can perpetuate for years
• The Beginning of Infinity

The Emacs advice system lets you modify the code running Emacs in a simple way. For example, if you wanted to change one line in a package you use to do something different or fix a bug before the maintainers release a new version, you can “advise” code to do what you want.

Here’s a recent example from my init.el that wraps a function to fix a bug in my setup:

;; ox-hugo doesn't set the `relref` path correctly so we need to
;; tell it how to do it
(defun my/org-id-path-fix (strlist)
  (file-name-nondirectory strlist))

(advice-add 'org-export-resolve-id-link :filter-return #'my/org-id-path-fix)

See also:

• Another way that Emacs is the ultimate editor building material

A good explanation can not be modified or molded to fit when new information contradicts it. It predicts situations that are both known and unknown. The domain of its meaning and applicability is not yours to specify.

Contrast that to a bad explanation—like a myth of winter caused by Persophone visiting Hades—which can be altered to fit new observations while resulting in the same prediction. It has no error-correcting mechanisms and the myth can always be constrained or expanded to apply to any situation.

See also:

• The Beginning of Infinity shows why good explanations are important to progress and why their reach is limitless

One way to answer “how do we know…?” is to justify one’s belief by reference to an authoritative source or cornerstone of knowledge. This is, in effect, saying “by what authority do we claim…?” which seeks endorsement in order to have certainty. Justificationism as a theory of knowledge therefore resists change (or at least delays in a form of path dependence).

Accepting authority as a source of knowledge also means accepting any other theories that stem from said authority.

Few things—if any—that are true in the absolute sense and the success of science proves that. Simply look at all the things we knew to be true that ended up being incorrect or misunderstood. Then observe all the progress since the 17th century compared to prior human history.

See also:

• Authority of knowledge leads to status quo preserving behavior (someone loses if knowledge turns out to be incorrect) and loss aversion
• From the book The Beginning of Infinity

In 1984 it was discovered that the Soviet Union was spying on communications from US embassies. It was previously believed they only had audio bugs which could be swept for. However, the GUNMAN project revealed a remarkable new form of digital surveillance that used bugged typewriters to intercept plain text communications (typed on physical paper). They later found out this was in practice for the last 7 years.

The impact of the discovery was far reaching. The NSA became an important agency, developing anti-tamper devices. New groups formed to create offensive capabilities. Some would alter argue that this was the catalyst cyber-warfare and a digital arms race.

Read Learning from the Enemy by the NSA.