• The Poverty of Compromise

    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:


  • Monetizing Innovation (Literary Notes)

    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.


  • The Ringelmann Effect Shows Groups Become Less Productive as They Grow

    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.

  • Nobody Wants to Run Their Own Server

    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

  • Distributed Apps Are Centralized

    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.


  • Defadvice Is Text Editor Superglue

    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:


  • Good Explanations Are Hard to Vary

    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:


  • Justificationism Secures Ideas Against Change

    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.

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  • The GUNMAN Project Was the Catalyst for a Digital Arms Race

    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.


  • Trends Are Not Explanations

    Extrapolating from past data points is not an explanation. Building your confidence that something that will happen—like Bayes Theorem—is useful for descrete, observable problems, but fails to reveal the truth. It’s the equivalent of saying “because it’s always been that way” which is a flawed way of reasoning about the world.

    For example, let’s say you are trying to predict the temperature of a beaker water. You start to turn up the heat on a burner and, based on previous data points, you expect the temperature to rise. It correlates well—heat goes up, water temperature goes up. Until it hits the boiling point and the water temperature remains constant. Trends are not sufficient to explain what’s going on here because it doesn’t explain the idea what is truly happening.

    Listen to The Beginning of Infinity part 1 on Naval Ravikant’s podcast.

    See also: