The typical measure of success in public speaking is the immediate reaction. That could be positive, like cheers, applause, or finger snapping. Sometimes the measure is a negative one, for trolls and provocateurs.
But influence isn’t always gained that way. It is possible to succeed as a public speaker simply by registering thoughts and feelings with the audience. You can “win” in a public debate or by giving a speech without anyone clapping.
The truth of public speaking is that people are listening. A good public speaker works in the backs of people’s heads. While the people talking are occupied with talking the listeners are freed up to really take in context and tone.
The only correct definition of good public speaking is that it accomplishes the speaker’s intent, which is to deliver a message. The better the receipt of that message, the better the public speaking. Recognition can be gamed, but other people genuinely hearing you out cannot.
A good public speaker stands on a particular hill and fortifies it. They slam a standard down. The best public speaking comes from the heart. The speaker’s sense of confidence and belief in what they’re saying bleeds through their tone of voice and body language. It cannot be faked for too long, to too many people.
Statistics are tools. Nonfiction writers have to know which ones to use and how to use them. That will depend on the text, the source, and the statistic.
Nonfiction readers punish excess. It’s better to use a few statistics well than to lug around a bunch of them.
This requires understanding the math of the relevant statistic, yes, but that is not sufficient. A classic example of poor statistical understanding is ignoring how averages mash things together that are actually different from each other.
The other and more common failure when using statistics is not understanding the relevant part of the real world. A statistic is a record of a pattern of something other than itself. That pattern has a construction to it, which must be understood.
Understanding the relevant part of the real world can help in two ways. First, you are able to accept or reject statistical arguments about it. Second, you are able to identify statistical arguments or inquiries that have not been raised before.
Readers not only value understanding what a statistic represents, but accept, reject, and adjust the weight of a statistic based on what they feel and think about the source. A company-ordered survey about how happy its customers are with its product will have less weight than when a consulting firm independently orders a survey about customer happiness in a market segment with respect to each participant.
People are involved in how statistics come about. It could simply be recording the statistic, like counting how many eyewitnesses saw the light was green versus those who saw it was red. Alternatively people might construct the statistic, say by sending a survey to people who had never thought of the questions in it before.
Before writing about a statistic, ask:
Is the source of the statistic trustworthy?
How was the statistic created?
What does the statistic do for my point or question?
Common sense is nonsense sometimes. That includes the ordinary distinction people make between objective and subjective.
Dictionary.com sums up the typical view: “Subjective most commonly means based on the personal perspective or preferences of a person — the subject who’s observing something. In contrast, objective most commonly means not influenced by or based on a personal viewpoint — based on the analysis of an object of observation only.”
Consider a couple objects that people call subjective:
Pleasure when eating ice cream
Pleasure when dancing: Like someone who dances at parties vs someone who hasn’t danced since their wedding reception.
Notice a key pattern. These are preferences. They are evaluated for utility: more or less pleasure from eating ice cream or dancing.
Whereas if I say, “There’s a pile of rocks”, that’s either true or not. I definitely saw a pile of rocks, but I’m doing more than telling you what I saw — I’m telling you my sight is right.
You can choose to fool yourself about whether something’s there, like a crazed conspiracy theorist, and you’ll definitely see things that aren’t there.
But you can’t fool the pile of rocks into being there.
Likewise, you can choose to visualize ice cream so intensely — the sweetness, the creaminess, the cold relief on a hot summer’s day — until you enjoy it more. And you will enjoy it more.
We can change our preferences to some extent.
But that does not make our preferences walled off from reality and consequences. It only delays the impact.
If you like ice cream a lot, you’ll eat a lot more.
All objects, as a rule of method, have both causes and effects. Including so-called “subjective” ones. Your preferences impact you and they do so for reasons that you can evaluate.
Nothing in reality inside or outside of our minds is arbitrary. You cannot manipulate your way out of anything and everything.
Choices are differentiated. Some are better, some are worse.
Dream interpretation is unfashionable in intellectual circles. Social scientists deemed it unrigorous some time ago, I don’t know how long after Freud’s heyday, and that distaste spread to broader society. I think this is unfortunate.
The most charitable description of dream interpretation to date is that it is not actually interpretation, it is construction. Dream interpretation is people guessing what their dreams mean with no way to know if they’re wrong. In dialogue with real people, we can find out if we’re wrong about what they meant to convey.
We do not have a sense for the language of dreams, but we do have an ability to tease apart emotionally associated thoughts and relate them to other knowledge we have. If “dream interpretation” between say a therapist and a patient helps bring in important, emotionally charged information and process those emotions, that is great.
But this is an act of creation, of an improved psychological state, and not an interpretation. At the same time, while I think we do not know what dreams mean, I think they really do mean something.
I think dreams mean something because they have story structure. They have ups and downs, tension and resolution, characters and transitions. It’s true that some dreams appear to have no real structure, and others may but it’s hard to tell or the host of the brain that had the dream doesn’t remember well enough to have a judgment on the question. But many dreams, for example, have a protagonist with a conflict and a journey. There are other types of stories of course, like ones about exploration.
Dreams aren’t nonfiction media, there isn’t a coded argument to unveil. If there is, I have no reason to believe it. But I suspect there are themes that are being worked out in some sense. I think that because as a rule good, consciously-designed stories have themes like conscience versus loyalty.
Any particular dream probably isn’t worth any study. A dream happened already, that bit of cognition is done with, and so the brain has been processing it. There may be some exceptions like a recurrent dream or an emotionally charged one, unless that emotional charge is rooted in a post-wake dream interpretation.
Absent a special reason, any given dream is probably worth no more than any other non-dream thought. Out of all the thoughts you’ve had over the course of the day, how many are worth recalling and scrutinizing? Not many.
Even so, it would probably be worth knowing how to interpret any dream, because that understanding might improve the complexity of dreams and make our thoughts during sleep more interesting.
All these things can be true at the same time:
Dreams mean something.
We don’t know what they mean.
It’s not worth trying to find out what any particular dream means with a few possible exceptions.
It is worth being able to figure out what dreams mean, to have a fluency with the language of dreams.
If you saw a giant obelisk-looking stone that came from space, and it was covered in weird squiggles, what would you think?
Let’s say some of the squiggles are exactly the same, though they’re spread out in lines, and there are 40 unique types of squiggles in total.
You’d likely react the same way people who saw the Rosetta Stone reacted before its translation: “Hey! This is a language. Wonder what it means?”
So that’s a counterfactual. Cool.
How about a fact? When you sleep, your brain is full of squiggly objects. And you have no idea what they mean.
I’m talking about dreams of course, though even when we’re not dreaming and asleep, we do not understand the ideational paths that are happening.
I tried reading Freud’s book about dream interpretation. I really did. It wasn’t that good, sorry.
Sometimes famous intellectuals aren’t rigorous. They have interesting ideas, yes, but not interesting enough to survive criticism.
Ordinary people also don’t know what their dreams means. And this fact of ignorance about our own cognition, for 8/24 hours of the day, is astonishing to me.
Add another hour a day for daydreams. They are puzzling as well. And I suspect the average amount of daydreaming is larger than an hour.
Add another hour a day for nonsense. That is, thoughts that aren’t fantastical like daydreams but are similarly obscure in their meaning from a conscious point of view.
That’s 10/24 hours a day where you don’t know what your thoughts even mean.
Now consider that of the thoughts you do understand, you don’t even know how they came about. They sort of sprang up in your mind. You also don’t know where they’ll go, the future course of this or that ideational path.
What a thing to be, a sentient being like you and I.
You and I feel like drivers of a vehicle that basically doesn’t listen.
It’s a little bit like talking to a dog owner whose closest friend bites old ladies and kids, and when you confront them, they’re like, “But I tell Toothy not to do it and he doesn’t!”
At the same time, the car in this metaphor does do a lot of things you want. Like in my case, it’s helping me write this essay.
I had a rough idea of what I’d jot down, and then something really different poured out.
I’m the farthest thing from a power user of my brain, and I know it. The same is true for all of us, but I’m not sure we all know it.
TV shows and movies have given the public these false impressions:
Investigation is common.
Investigation is affordable.
Investigation is done by people with street smarts.
In reality:
Almost all civil and criminal offenses are never investigated.
Investigation is expensive.
Investigation is done by bureaucrats.
Investigation is rare
Of all the civil offenses and crimes I’ve seen or heard about in real life, almost none were looked into by law enforcement or a regulator. As far as I know that’s also the experience of other people, whether they’re aware of it or not. This is even more true in every part of the world that isn’t well off like the United States.
In many parts of the world there has to be a dead body for the police to investigate. Sure they might show up, talk a bit, and take some notes, but nothing else will happen. The primary purpose of the police is to seize, protect, and restrain people, things, and buildings. The police don’t put clues together, by and large, and frankly don’t have the manpower or funding to do more.
Investigation is expensive
Investigations are rare because of the cost. Investigation is way too expensive. And that’s not because of the software, data, hardware, vehicles, or buildings required. No, it’s because of labor.
An investigation is an act of labor. People have to be paid salaries. They have to spend a ton of time talking to other people, reading documents, and looking at stuff. They need to put a bunch of thoughts together and run it up the chain. Then they’ll need to run their thoughts past judges, who are not known for unrigorous thinking. The cost of labor is the principal cost of investigation.
Investigation is not done by street-smart, scrappy people
Television shows show white-collar heroes in industries like espionage or law who have street smarts. Sorry but they basically don’t exist in my opinion.
In general people are either street smart or book smart, not both. This is partly because environments where book smarts are trained up or required hardly allow the iterated “exercise” of street smarts. These are primarily reflected in instincts, intuitions, and unarticulable forms of knowledge like rapid visual perception of for example body language.
Investigations are usually in part legalistic processes that require the verification and attempted falsification of knowledge through verbalization and sometimes heavy numbers work. That’s why the infamous, rarely lauded, intuition of a police officer has a limited role in the United States, whereas it is the cutting edge of the de facto legal system in most places across the globe.
Investigations are rare, expensive, and done by people who don’t have street smarts.
Artificial intelligence is not a unicorn product category.
Decision makers are at risk of seeing AI as unique. It is not. Consider three aspects of the public dialogue about AI.
Research
Products
News
Errors are rampant in AI research.
Princeton computer science professors Arvind Narayanan and Sayash Kapoor examined the literature, including replication attempts and feedback between researchers. Narayanan, Arvind, and Sayash Kapoor. AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference. Princeton University Press, 2024.
Quote: “The party line among scientists is that science ‘self-corrects’ … but everything we’ve seen about the process suggests otherwise.” Ibid. 23.
This should come as no surprise:
Replication failure has a high base rate in the sciences. Baker, Monya. 1,500 scientists lift the lid on reproducibility. Nature, May 2016, https://www.nature.com/articles/533452a.
Sloppiness and hucksterism are frequent with a new technology as users, buyers, and listeners catch up.
AI products often do not work. Even McDonalds, after working three years with IBM, was not able to produce an adequate AI drive-through system. OECD AI Policy Observatory Portal. OECD, 2024, oecd.ai/en/incidents/91533.
Vendors have some incentive to sell more than serve. That’s one reason why consumer protection regulators are up in arms. E.g., FTC Order Requires Online Marketer to Pay $1 Million for Deceptive Claims That Its AI Product Could Make Websites Compliant with Accessibility Guidelines. Federal Trade Commission, 3 Jan. 2025, www.ftc.gov/news-events/news/press-releases/2025/01/ftc-order-requires-online-marketer-pay-1-million-deceptive-claims-its-ai-product-could-make-websites.
Journalism is no bright spot in the public dialogue about AI. As a rule, journalists know less than the people they interview. Moreover, journalists have some incentive to attract attention more than to inform.
Therefore, AI is not a unicorn because creating business value from it requires shrewdness.
Expect mistakes, puffery, and misinformation.
Don’t get lost in the broader public dialogue.
What should you do instead?
Focus on the specific AI product or project in front of you.
For example, a McKinsey 2023 report estimated that generative AI will add $2.6 to $4.4 trillion in value globally. Chui, Michael, et al. The Economic Potential of Generative AI: The Next Productivity Frontier. McKinsey Digital, McKinsey & Company, 14 June 2023, www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier.
This information tells you nothing about the value of a particular generative AI product or project. News like this should be no more than ambience for decision making.
If an AI product is sold to you, it is sold to competitors. It is unlikely that an AI product will confer the buying company a competitive advantage; at best not a disadvantage.
In the short term, some first-mover buyers may gain an advantage.
Internal Projects
As a rule, large project expenses balloon compared to initial expectations. There is no reason to treat AI projects differently.
Winnow the List: Four Rules for Decision
Maintain mental discipline during AI hype periods.
Place the burden of proof on vendors and internal advocates.
Reject all proposals that are heavy on buzzwords.
Reject all proposals that hand-wave potential downsides and focus on extreme benefits.
Ignore Hype
Just as pricing action in stocks does not reflect real business value, the ebb and tide of AI hype and fears do not reflect the real capabilities and risks of AI technologies.
Consider one type of AI. A 2023 IBM study found that CEOs are under accelerating pressure to adopt generative AI. CEO Decision-Making in the Age of AI: Act with Intention, IBM Institute for Business Value, June 2023, pp. 6, https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/2023-ceo.
You do not want to inject noise and bias into firm decision making.
Even if you’re over the initial generative AI hype, more waves will come.
There’s too much interest and investment in AI for us not to expect periods of unusual, irrational pressure to accelerate or slow AI product adoption and projects.
That’s a Nice Feature, so How Does It Affect Us?
Best information: You are at base most informed about you and your interests.
Second best information: Next you’re most informed about your company and its interests.
Insist on funnelling the information you get from Zoom & room sessions with salespeople and internal advocates into answers about how the firm will be helped or hurt.
Plain English
Only accept plain English answers. At a cocktail party, the extensive use of buzzwords is a classic tell for ignorance. In business dealings, it is a tell for fraud.
Easy Mode?
Like “candy from a baby”-style statements about the benefits of a deal is a classic tell in business for fraud.
Avoiding a clear, detailed discussion of risks and downsides is another tell.
Consider the cost of compute. An IBM study from 2024 found that “15% of projects have been put on hold and 21% of gen AI initiatives have failed to scale” because the cost of compute was too high. The CEO’s Guide to Generative AI: Cost of Compute. IBM Institute for Business Value, October 2024, www.ibm.com/thought-leadership/institute-business-value/en-us/report/ceo-generative-ai/ceo-ai-cost-of-compute.
We’re done harping on common sense, free of the illusion of AI as a unicorn category.
Saluting Unproblematic, Useful and Uninteresting AIs
But don’t mistake this paper for a cynic’s piece. AIs can have significant business value. PwC’s 2024 summer survey of 1,000 business and tech executives found that around 40% had seen increased employee productivity and 41% had seen improved customer experience due to AI technology. 2024 Cloud and AI Business Survey. PwC, 2024, www.pwc.com/us/en/tech-effect/cloud/cloud-ai-business-survey.html.
You have integrated, are integrating, and will integrate a significant number of AIs into your business. This includes a wide range of B2B SaaS software, little software updates to employee devices, and more.
Only a Few AIs Stand Out at a Time
Not only are many AI unproblematic, many are useful and not even remotely interesting except to the respective engineers involved. (No offense to them.)
AIs often become unproblematic because engineers ensure, at times through feedback from other humans and other AIs, that a particular AI needs improvement on some dimension, such as efficacy, cost-effectiveness, privacy, safety, and others.
If a vendor can automate tasks and save employee time, you’re interested but not precisely in how the software works. Of course you want to make sure it works with limited downside.
Even machine learning AI can produce new and yet boring results. For example, we almost all learn to drive, each in our own way. We don’t really care about how others learn to drive as long as they do it well.
Certain AIs have stood out and required far more internal discussion than normal, and those are worth focusing on.
How should you do that? Start with the fundamentals.
Trade Secret Knowledge: Proprietary Data and AI
Steve Jobs and his team generated a pool of knowledge that made the iPhone what it became. All kinds of business metrics for Apple could retroactively present a picture of value, but the value was fundamentally a set of abstractions embodied in a small group of human brains.
Do we ignore the asset driving businesses forward because it’s hard to quantify? We can’t abandon metrics when evaluating AI product or project proposals. But we can’t let them drive the conversation either.
Knowledge is the greatest asset that a business can have.
If knowledge differentiates businesses from each other, you must create knowledge that is:
Useful to the firm, and
Exclusive to the firm
Trade secrets, like all knowledge, are divisible into three types:
Descriptions: What is
Prescriptions: What should be
Explanations: Why something is or should be
Examples:
Descriptions: Price histories
Prescriptions: Current pricing offer
Explanations: Why a particular price is a good fit for a customer
A description is true or not. A price of $10,000 was set in 2016 or not. The same is true for any explanation: The claimed cause of an effect is either the cause or not.
But a prescription is directly evaluated for its utility. Indirectly a prescription is only useful for reasons, which are encoded until they are drawn out with a smart explanation and accompanying descriptions.
Prescriptions include products and services, which have utilities that vary between customer groups and within them.
A Trade Secret Explanation is the Greatest Competitive Advantage
You’re a customer in this hypothetical. You get on the interwebs and shop. Feature-wise, descriptively, your available economic packages are complex but let’s just focus on product and price.
The net value of your digital buy is the prescription. Why you get that value is an explanation.
Explanations are the most valuable form of knowledge, following the work of famed physicist David Deutsch. Deutsch, David. The Beginning of Infinity: Explanations that Transform the World. Viking Press, 2011.
We honor thinkers such as Einstein who subsumed a great mass of complexity into a tight causal understanding of reality.
The more informed you are about your wants and abilities and options for satisfaction, the better off you are. But the force behind your shopper savvy is not a list. It’s not just a set of descriptions like the price or the product.
If you’re buying food, say, then you’re a better buyer if you know something about hunger, dietary restriction, digestive effects, and more. That is, the why of things.
Two conclusions follow.
The more complexity that an explanation encodes, the higher the utilities of the prescriptions that it generates.
The more utility that a prescription has, the more causal complexity that it encodes and therefore the greater the explanation you could draw out.
An explanation is a multiplicative gain prescription-wise. If you can reverse engineer an excellent product, you will gain other valuable business prescriptions in the future.
But at the same time, the better the prescription, the more difficult it is to reverse engineer. This is precisely because of the complexity it encodes.
This is really what makes a moat, following the focus of those wise men Warren Buffet and Charlie Munger. It’s not strictly about high utility to customers. Without the corresponding barrier to reverse engineering then entry would be rapid and successful.
Now you could object. Consider police dogs. You think they’re dumber than you. Yet they reverse engineer scents to sources. Far better than you.
I would submit that the dog has a cognitive speciality that is superior to yours and that you also have cognitive specialities the dog does not. You also have a general problem-solving ability, which you prize and should.
Now you could object again and say the dog isn’t a thinker, it doesn’t have an explanation generating prescriptions. But evolutionary selection, principally natural, is a bit like the high-performing machine algorithms that you fear and savor.
In a high-performing organic brain, explanations knit correlations into a low-“compute” way to do things, i.e. generate true descriptions and useful prescriptions. This includes the anticipatory, experiential, and memory-based pleasure of knowledge production itself.
High-performing machine algorithms function a bit similar to evolving explanations inside organic brains. More-so than natural selection, as the latter is quite slow. This is one among other differences.
Obviously the selection process in high-performing organic brains is quite fast. It goes fastest when the brain has a good explanation for the relevant correlations.
At this moment it does not seem there are any machines with the ability to produce an excellent and novel explanation. High-performing AI can work with causality but not quite at the level that high-performing people can. One day they may.
In sum, you create the most business value when you have better explanations that matter for the business. You also create a nicer but harder target for rivals and potential entrants. More money, more problems.
Use Proprietary Data and AI to Develop Trade Secret Understanding
You know two relevant cliches:
Correlation does not imply causation, but
Without correlation there is no causation.
By extension:
Explanation is not possible without a correlation on hand, and
Correlation implies the potential to create an explanation.
All business-relevant correlations are grounds for the business to generate the most useful sort of trade secret.
For example, Steve Jobs noticed the value of motor and visual aesthetic in daily life and extended his knowledge to create the iPhone.
In the constantly evolving real world, firms never lack correlations to study. But sadly all firms are destined for death and thus we can say this: The total sum of all business-relevant correlations that the firm observed represented the summit of the trade secret explanatory knowledge that the firm could have created.
Of course you can steal people with unique abilities, but if the firm is ambitious even the unicorn hires will have to work hard.
Correlations are not equal for two reasons:
Firms, people, and agents have different incentives and objectives
Information objects encode different amounts of complexity (e.g., machine learning AI versus an abacus)
A lumber company likely wouldn’t benefit on balance from an AI trained on the proprietary data of an ice cream manufacturer.
A small-to-midsize firm likely doesn’t have the amount of proprietary data it needs to benefit on-balance from a 5 million-dollar internal AI project.
A business with plenty of proprietary data, like large companies always have, has on hand the potential to create bottom-line boosting, trade secret explanations.
Internal AI projects offer greater potential for competitive advantage than buying AI products.
But this is not strictly true in terms of maximum value, as many boats will rise due to the mass sale of AI products.
We’ve focused on the firm level. What about individual and agent sources of knowledge?
Agents and Humans: The Seven Business Functions of Intelligence
An agent can have seven pro-company functions. From now on the term “agent” loosely means an individual source of intelligence, human or machine.
Starting with the first four functions:
What we have witnessed with the rise of AI is the crossover from machines as automaters to sources of new knowledge.
An MITSloan report from November 2024 found that companies with superior learning cultures–in general and specifically relating to AI–achieve superior financial results and manage uncertainty better. MITSloan Management Review. Learning to Manage Uncertainty, With AI, November 2024, https://sloanreview.mit.edu/projects/learning-to-manage-uncertainty-with-ai/.
Human and machine agents can perform three additional functions for a firm.
Transfer: The direct, summative effect of knowledge transfer
Seeding: The indirect, multiplicative effect of knowledge transfer
Partnership: The direct, multiplicative effect of what we’ll call “agent-to-agent rapport”
How do the two groups of functions compare?
Repetition
Marginal Improvement
Significant Improvement
Transfer
x
x
x
Seeding
x
x
x
Partnership
x
x
x
The table is not useful. It does not encode critical information: Summative versus multiplicative growths in knowledge. Thus it over-emphasizes the value of knowledge transfer to the eye.
Knowledge Transfer
This effect is direct. It is agent to agent activity.
This effect is summative. It is an addition of information bits into an agent.
A tangible product example: A Finnish coffee maker produces and sells a roast where the recipe and drink experience were specified by a generative AI. The human employees iterated potential improvements specified by humans and also did blind taste tests. The AI coffee was better. The AI Developed Artisan Coffee Wowing Connoisseurs in Helsinki. AP News, 2 Jan. 2025, apnews.com/video/james-brooks-artificial-intelligence-finland-helsinki-5125043d4301489e932c6120a030ebfb.
With generative AI features such as user like/dislike and ratings options, both the AI and the human are also gleaning a direct, summative value that comes from teaching. We learn when we teach.
Seeding Knowledge
This effect is indirect. Transfer happens before seeding.
This effect is multiplicative, not summative. Transfers operate on sets of potential insights.
If you learn something from another person or AI, the act of aggregating that information into your own mind then begins a chain reaction. It happens when we’re conscious and unconscious.
Every bit of information you gain is a potential premise or catalyst to a potentially infinite number of conclusions or realizations. The more you learn, the more you can learn.
Admittedly we all die, or so it seems, so we only learn a finite sum of information in a lifetime. Thus a causal diagram with discrete components makes sense.
Continuing the AI coffee design example:
The diagram represents the Finnish company point of view.
Consider the agent perspective. A coffee researcher and testing supervisor might multiply her knowledge from a profitable AI coffee design by learning more about coffee smell and taste. And perhaps, that coffee knowledge at our current historical state is far from where it could be. This “meta” knowledge–wisdom applied to coffee design–is also a knowledge multiplier.
If a knowledge transfer produced business value, its “penumbra” of interlocks with other ideas will certainly include ones with business-specific value.
Illustrated:
Partnership Knowledge
This effect is direct. It is agent-to-agent communication.
This effect is multiplicative. Partnerships operate on sets of problems.
Illustrated:
Just as the interaction between two employees can produce a professional rapport that improves the quality of the work, AI interactions with humans can lead to uniquely powerful ideational paths during work.
Consider your strong human-to-human work relationships. A kind of accruing knowledge is encoded in your interactions.
The figure covered some aspects of partnerships. But what about subjectivities?
Examples:
You feel inspired–begging the question of what inspiration even is–to do better.
You may feel a “friendly competition” to perform better.
It seems that, as of today, there is no machine with subjective experiences. But they still happen on one side of the human-generative AI partnership.
There are people in your life who use great AI chatbots and are experiencing a kind of partnership today. The effect may be insignificant today, but we should assume it will grow as the technology develops.
Typical AI Typologies are Not Useful
Consider common AI subcategories:
Generative AI: Predicts the next item in a sequence and offers it as answer to a query
Analytic AI: Digestible presentation of data that is often otherwise physically inaccessible or at least hard-to-process
Predictive AI: Tries to predict the future
Rules-Based AI: ‘If A, then B’; e.g., if a customer picks “billing”, they next choose between three options
Compare these categories to the business functions of intelligent agents.
Repetitive
Marginal Improvement
Significant Improvement
Transfer
Seeding
Partnership
Generative
x
x
x
x
x
x
Analytic
x
x
x
x
x
Predictive
x
x
x
x
x
Rules
x
x
x
x
x
The table shows how only generative AI produces cognition-boosting rapport. Otherwise the table has no value.
Let’s try something different. Sort typical AI subcategories by maximum value. Look only at top performing AI in each category.
Generative AI
Analytic AI
Rules-based AI
Predictive AI
These AI types can and do complement each other in the real world.
Why Karl Popper Wouldn’t Break Bread with Predictive AI
The biggest problem with predictive AI is the biggest problem with prediction.
Prediction is doable when the set of objects under query are not intelligent (agents). But Karl Popper, famed philosopher of science, showed in The Poverty of Historicism that predicting the future is difficult to impossible when people are involved. Popper, Karl. The Poverty of Historicism. Beacon Press, 1957.
You cannot know the effect of an idea until you have the idea in front of you.
Even then, you may need to really think the idea through. Ideas include physics theories, cooking recipes, machine learning algorithms, product designs, music, videos, and more.
The amount of work you’ll need to do depends on three things:
The complexity of the idea
The complexity of your mind, i.e., the sum of relevant knowledge (ideas) you can and will bring to the idea in focus
The complexity of the objective and the environment in which you’d instantiate the idea.
The environment could simply be the ecosystem of a curious mind (yours, a direct report, etc.). In this case factors two and three collapse into each other.
But if the idea in question requires more ideas in order for it to be instantiated in some way that you want, and you have to learn those other ideas still, you again do not not know what will happen.
Each and every idea is sandbox material.
Whether you accept it or not, every uncreated idea is a potential bit of chaos.
One effect of people exercising their creative intelligence is to produce unpredictability for other people.
Most domains where people would like to use predictive AI involve other people actively thinking and acting.
Moreover, all domains in principle–even the distribution of sand on beaches or the movement of rocks in outer space–could be affected, even extremely indirectly, by the activity of people.
At any moment a domain where predictive AI is performing well could be undone by the creeping unpredictability effects of people.
And AI. The more new knowledge AIs create, the more unpredictability they generate.
Blame Programmers: Rules-Based AI
The motto of programmers with rules-based AI is that all problems are the fault of the programmer.
The upper limit of business value from rules-based AI coincides with the sum of existing knowledge in the minds of programmers.
All rules-based AI, all non-machine learning AI, encodes the program-specific knowledge the programmers had to convey, with the general programming knowledge and interest or incentive they had to program.
You might object. Consider a high-performing, rules-based “randomness” algorithm whose outputs cannot be predicted by the best programmer.
The algorithm works for the objectives of the programmer, the business, and the buyers. The programmer understands enough about mathematical and computer objects to produce a set of outputs that satiate some objective, say for security.
Yet the programmer does not understand enough to predict the exact contents of the set. They may not care, and you may not care, but the relevant knowledge is not there.
Non-Duplicative Knowledge Transfers: Analytic AI
Data can be difficult or impossible for people to access.
Data can be hard for people to digest.
Analytic AI, when high-performing, can access and represent data for people to then use.
The knowledge transferred by analytic AI isn’t completely new because it existed in some form, but the transfer was made viable by the AI.
Each non-duplicative knowledge transfer produces valuable seeding effects.
The transferring agent does not need to be the first transferor to produce value.
A transfer adds value at least when the receiving agent doesn’t have the knowledge in the moment where the knowledge matters. Even a reminder of something you already know isn’t duplicative transfer if you cued in sooner in a helpful way. In these situations, the real transfer with value is of the priority of a bit of knowledge.
All subcategories of AI that we have discussed, including predictive and rules-based AI, can produce valuable seeding effects.
New Knowledge From Node One: Generative AI
The most valuable type of AI
High-performing generative AI creates new knowledge
Adapts better than other AI to the context of the agent with which it interacts
Produces more powerful seeding effects than other AI
Uniquely produces partnership knowledge
Reading philosophy bores many of us, but we can abandon fraudulent sophistication and still engage in useful, extended abstraction.
Information is Created
Observe: Information does not pre-exist. Information is created.
It doesn’t matter that an ancient Greek philosopher fancied things differently.
The act of information creation, whatever the units of cognition are, in organic bodies or those of machines, is of fundamental importance to whatever we must label progress.
As strange as it seems, this act is the farthest we have gone in understanding intelligence. People have learned to experiment with drugs, prompts, showers, habit formation, etc. to “toggle” the mind and get better results on some dimension, like happiness or productivity. But we basically do not understand the minds we have or those that seem complementary to ours, like AI.
We lack a fundamental explanation of intelligence.
Given our ignorance, what can we say about the knowledge creation of high-performing generative AI?
Wrappers and the Ignorance of Crowds
The phrase “wisdom of the crowd” is misleading.
When a professor averages out the guesswork of 100 people and calls the sum highly predictive, his work was not a wrapper. The crowd needed him, just like middlemen of many stripes have valuable roles to play in the economy.
Data only has value when it is used right.
When a high-performing generative AI computes tokenized human activity and creates valuable insights for a businessperson with far more documents and meeting notes than they could ever review, the wisdom was not already there. The AI created it.
Iterative Grounding: Generative AI
But high-performing generative AI does not only transfer knowledge from humans to other humans.
A well-functioning human-generative AI pairing creates new knowledge during the iterative process it has with each other agent: Of context, queries, answers, and feedback. These too are useful correlations.
What Do Transfer Recipients Need to Do?
The novelty of an idea isn’t valuable on its own. The value of an idea fundamentally depends on its truth value.
But I would submit that organic interest from a user in an AI implies the utility of its outputs to the firm, and therefore the “truthiness” of them. That is assuming the user is a competent agent acting in the interest of the business, two problems which are classic and not really the subject of this paper.
Organic agent interest reflects the unique learning curve of the agent.
A unique learning curve exists regardless of whether the generative AI is accessible to them.
But that exact lower barrier to access–whether a subscription payment or “free” with authorized access as a firm asset–makes it possible for generative AI to produce a complementary source of valuable knowledge transfer.
Human-to-human interactions are extremely valuable sources of new knowledge. But they are also fraught, as perhaps human-to-independent AI interactions will be, with different motivations, plans, and actions with negative impacts. Negotiation becomes possible and necessary.
But as things stand now, high-performing generative AIs are like a potential work partner with complications that are dissimilar to human-to-human work situations. (Perhaps some analogy could be drawn to token limitations and a person’s patience or something.)
The more barriers that an agent has to gaining valuable knowledge transfer from other minds, the less valuable knowledge they will obtain.
At the same time, every agent has a genuine query they bring to conferences, discussions, debates, and conversations. To answer their real query, they often have to do real work.
Most knowledge transfers involve work on the receiver’s end to incorporate that knowledge. Not only to clarify it, but to extend it to the agent’s genuine interest and knowledge gap.
A high-performing generative AI tailors its answer to the user’s query. A history of queries, context, answers, and feedback deepen the match between what the AI can offer and the user’s actual, implicit–seemingly idiosyncratic–learning curve.
The connective tissue that users and generative AI create together is new knowledge.
Admitting some preference falsification will exist where the user does not have complete control, the typical user input should tend to be honest. Individual cases will depend on user-company trust, a problem which is also classic and not the subject of this paper.
Thus a high-performing generative AI will provide more valuable knowledge transfer and seeding effects than if the user had to use a high-performing non-generative AI.
Moreover, as the technology improves, human-to-generative AI interactions may replace a significant amount of low-performing human-to-human interactions.
We’ve been lugging around this concept of newness in knowledge growth. Let’s unpack it.
No Cliffs or Liftoffs: Continuous Improvement in Intelligence
The world waits with bated breath for the jarring arrival of “super” AI. Either a cliff or liftoff, there’s a general belief that AI improvement is embedded in staggered learning curves.
As to knowledge growth generally, it’s easy to see chunky processes. Examples:
Programmers feel surprised at the outputs of high-performing generative AI.
Professionals wonder how their best work came about when the lead-up was a slog with no gems along the way.
There’s no need to pretend that we know the exact structure of every learning curve in the universe. But as a rule of method any agent’s learning curve will emerge as incremental on close inspection.
Examples:
High-performing generative AIs have chains of reasoning, which programmers obsessively try to understand.
Professionals spend many hours on bad ideas and dead-ends, which is another way of illustrating the role of selection and waste in a process of growth.
Fundamentally, as a rule of scientific method, all objects have causes and effects. Ideas are abstract objects and require the same attention from us.
Assume until rebutted: Intelligence is a continuous process whose steps are almost always hidden to us, until we uncover them.
Do not expect cliffs and liftoffs in the learning curves of either human or machine agents. Expect increments in improvement. Only people who haven’t studied a particular learning curve will think it isn’t a continuous process.
Newness is an experience then. It encodes our ignorance.
Expect to experience newness, including in the field of AI.
AI Knowledge Growth Knows No Upper Limit
You may recall that the value of what AI can do coincides with the material it has on hand, those “juicy sweet” correlations, if you’ll allow me to borrow from Gollum.
As long as people are around, they’ll be doing things that can be represented and processed by an AI. More human activity means more material for AI. More population growth means more human activity.
But what people are doing today is only one factor raising the upper limit of what AI can do. The other factor is AI itself.
Agents participate in complex learning feedback loops.
Each agent changes the environment for themselves and others.
As we discussed, knowledge transfers from any source produce seeding effects. These effects will eventually loop themselves back to AI.
Example:
A firm’s chatbot gives helpful answers to clients. Customer service improves.
Customers become more informed. Expectations and behavior change.
Market share adjusts. Competitors respond and improve their customer service. Or they falter and fade.
Market participant activity across the board encodes more complexity.
More AI means more seeding effects and more complex feedback loops.
AI is not running up against an upper limit. As long as we live in an open society where knowledge can be freely created and shared, and transactions completed, progress in the field of AI will continue.
What to Do When AI Goes Wild
You likely aren’t worried about AIs that can do things not in their training data. Those AIs sound useful.
You might be worried, though, about a subcategory of those AIs, those that have their own objectives. Let’s call them independent AIs.
People do what they do for different reasons, the real driving ones of which are often mysterious to themselves and certainly to others. We have no reason at this time to expect differently from independent AIs.
Our collective shock during the first, recent wave of AI hype and fear encoded our ignorance. Of many things to be sure, but we can highlight ignorance of our own intelligence and the possibility of other advanced types.
Geoffrey Hinton, one of the founders of the field, considers the advancement of AI to be “fertile grounds for fascism”. He warns that AI may supplant enough labor to tilt nations toward authoritarian regimes. Nobel Minds 2024. YouTube, uploaded by Nobel Prize, 18 December 2024, https://www.youtube.com/watch?v=1tELlYbO_U8.
The prosocial tendency of intelligence is knowledge creation, use, and dissemination.
But not to be naive, intelligence bends toward antisocial behavior as well. The dark twins of competition are violence and deception. They are, paradoxically, made superior through intelligence and tend to undermine the kind of environment that makes intelligence blossom.
This paradox will continue for as long as we lack a fundamental explanation of intelligence, but that is a topic for another paper.
Because of our limited knowledge of intelligence, we must assume two things.
First, a significant number of independent AIs will seek violence and deception. Just like people.
Second, a significant number of future AIs will seek to create and share interesting, useful knowledge. Just like people.
The knowledge that will be interesting to them will depend. It will depend on them, individually. Like us.
I make no claim on what will happen to the labor market, much less to the world. I only claim that, as a businessperson, you’ve got a small sliver of reality to focus on.
I suggest that you assume that some AIs as smart as you (or more) with their own objectives will have the capacity and incentive, just like people, to work with you and your team. If not yours, then potentially with rivals.
The opportunity to focus on is to create interesting, useful knowledge and share it within the firm. The net effect is better products, better marketing, and greater customer value.
The sooner you adapt to this new reality and form useful alliances, the greater your advantage over other more fearful competitors.
You may object. I concluded earlier that high-performing generative AI may replace a significant amount of low-performing human sources of business knowledge. This is bad for humans.
But all substitutions are transactions to one side, the winner, and the beginning of a new transaction for the one who lost a previous match.
Economies in a sense are sets of transactions and human agents have the inherent ability to adapt.
It is also worth noting that all new and useful business knowledge, created by people or machines, replaces obsolete, pre-existing knowledge. Progress involves winners and losers.
If we say that a current labor source is adequate, it is adequate for reasons. Prior to those reasons manifesting, a next step in progress was possible. We know because the next step happened.
Each progressive step in business knowledge implies the possibility of more.
Conclusion: Leave Hype and Cynicism to the Side
The majority of news, research, and products are not worth your time. Yet a proportionately small number of products and projects will produce significant value for the business.
But the topic isn’t just practical, it’s abstract. Applying the given wisdom of business dealings and internal firm politics helps us see through mistakes, puffery, and misinformation.
But at the same time, the cutting edge of AI is more cutting edge than the majority of industries, products, and projects up for discussion among managers and owners. Complexity is unusually high.
The rise of AI reflects back a key responsibility of business decision makers. That is, to gather and create ideas and then ruthlessly interrogate them. Leave hype and cynicism to the side.
If you want the best for your team’s business, then you’re looking for competitive advantages. They should all sum up to better service and value to customers.
In most discussions, competitive advantages are seen as physical assets, essentially property rights. This view is incomplete and in a critical way. In this article, I will show you two things.
You will understand why the greatest competitive advantage is trade secret explanations.
You will understand why explanations are a unique kind of physical object in which no one can have property rights. But you can still keep them secret, and you will learn how.
There are many ways to learn how to succeed and make innovative products, but the best approach–all at once–costs nothing to gain and can be lost at any moment. And yet, those who use it earlier on, as long as they keep going, will be ahead of competitors who started later–and certainly those who never use it.
The Greatest Competitive Advantages Are Trade Secret Explanations
We’ll dig into the trade secret side of things in a second. The point there for you is exclusivity, the ability to draw profits out from useful knowledge that your competitors don’t have. But when is commercial knowledge profitable in the first place?
The example of Warren Buffet will help us here. He spends a lot of time mulling over company materials, figuring out whose moats are so grand that any idiot could defend them. He focuses on physical assets. The man is brilliant and hard-working, I have no criticism of him. I only want to observe that he focuses on the results of creative business processes.
Whatever the right ‘who did what’ balance of credit that is reflected in any particular balance sheet, the ambition, passion, and talent of owners and employees can pay off in many different areas: From engineering and design, or marketing and sales, to finance, legal, and customer success.
Profitable Business Assets Do Not Exist Until People Make Them
Profitable physical assets are almost never just found, especially in the modern era when we control our environment more and more than just react to it. Revenue-generating company assets are made–people put them into the world.
Even a natural resource like oil that is found in the ground is extracted, processed, distributed, labeled, and used with other products like cars. Progress involves people using a unique ability they have, creative intelligence, to make and trade products, and use them or spend the money earned from sales.
It is the creative intelligence of people that makes products and gets them to market. Trial and error is part of it. But theories are too. Both are necessary. Neither is more important than the other. Creative intelligence produces theories and experiments. The most important kind of theory that makes profits happen is this: Explanations.
Explanations are “why?”-style theories and they are what creativity creates first. They are also called understanding and first principles. Any founder and innovator first comes up with a potential “why?” and from this they create tests or experiments. In an iterative process of causal speculation and experiment, explanations and tests, a bedrock understanding comes about (eventually).
Circling back to the question I posed at the beginning of this section, we now know when commercial knowledge is useful. Something you know (that your competitors don’t) is useful–reliably, not by luck–when it stands on truth. More precisely, a “should”-structured business idea (like internal pricing guidelines) has to stand on a true “why”-structured business idea (like a customer persona).
If an idea is useful, it is useful because of a true explanation. Let’s call “should”-style ideas this: Prescriptions. They have a utility value, which is a continuum from 0 to some maximum digit, say 100. An explanation on the other hand has a binary value, 0 or 1, true or false.
All Products Encode Prescriptions
It’s time to talk more in depth about deals and uses, transactions involving products and services. Put briefly, they are physical equivalents of “should”-style ideas. Every product has a utility value to each buyer or user. The value can be negative or positive, and varies even among the individuals clustered in a seller’s customer persona brief.
Any asset, including digital ones like pillar posts with a long internet shelf life, encodes a “should”-style idea. Circling back to an earlier example, a customer persona brief is a company asset with a utility value across many employees in different departments, from engineering to sales. The utility value that each employee gains may differ. For example, the product marketing team may gain the most utility out of all departments.
To the customer, there is no direct value to the customer persona brief that applies to them. The explanations inside the brief about the customer lead to pricing guidelines and other tactics that yield payoffs for the customer and the seller. The brief includes explanations, but is itself a prescription for the seller’s employees.
A seller makes a customer persona brief or hires a third party to make one when, and only when, decision-makers have come up with a driving explanation involving two elements. One, that customer persona briefs are good tools. Two, an understanding of what needs to be in the brief to make it a good tool for employees.
Explanations of the Market Make Products and Other Business Actions
Circling over the timeline of our customer persona brief example, at every stage explanations matter a lot. A business or employee might ask questions like these.
Why do we have so much customer churn?
Why is our inbound conversion rate so low?
A good customer persona brief could turn the dial down on negative patterns, like churn, and turn the dial up in profitable places, like inbound conversions. Understanding customers is important and involves explanations about their incentives, how you should communicate to them, and more.
Good business explanations generate useful prescriptions and products. Prescriptions cannot. You cannot teach someone how to be an effective businessperson by just handing them a list of best practices. They may teach themselves by meditating on the list, but the step from the list to good business instincts and judgment is explanatory, it is causal, it is a creative leap from point A to point B.
We’ve covered the explanation side of this article, so let’s dig into trade secrets.
How to Put Moats Around Your Profitable Explanations
Knowledge is a competitive advantage under two conditions:
One: If it’s a prescription, when it’s useful; if it’s an explanation, when it’s true.
Two: When you know it and your competitors don’t.
Under these conditions you can extract profits from an abstract asset. But how do you moat off a competitively advantageous abstraction? There are 2 methods: Intellectual property law and trade secrets.
Intellectual property law breaks down to copyright, trademark, and patents. None apply to explanations, but trade secret status can be created and maintained for this fundamental competitive advantage. Let’s talk about IP law first.
Intellectual Property Law Does Not and Cannot Protect Explanations
Intellectual property law does not apply to explanations, your causal understandings of the market in which you operate. Notice that American judges do not try to keep your competitors from understanding why something you do, or sell, succeeds.
Judges try to keep your competitors from theft or improper use of something you have protected under the right legal procedures. But none of this has to do with objects inside of your competitors’ minds–judicial and legislative action is about what competitors do external to their minds, like hacking your WordPress site or picking a lock.
The examples I just gave are criminal of course, and criminal law has play in the joints with IP law. An intellectual property right would help you prevent a competitor from reproducing a patented drug, for example. But for purposes of this article we only need to focus on the difference between what is inside the mind and what is done outside of it.
Why is this so? It will continue to be this way, and here’s one good reason why. Even if a legislature or judge attempted to create an artificial entry barrier around explanatory knowledge, it would fail.
The reason is simple.
On the front-end, a statute or common law doctrine depends on good enforcement on the back-end. A law without teeth is not worth calling a law.
A judge in any particular case would have to compare an offender’s knowledge to the IP law right-holder. Meaning, if you put a marketplace understanding of your own under the protection of IP law (in our hypothetical world), the judge would have to understand what you do in order to know if the defendant does too. Therefore, enforcement of the law would require its violation. This is all very ridiculous I know and we’ll move on now. (But I do have a couple other points here that I may add into the article later.)
Trade Secrets are Common and Awesome
Information is protected as trade secrets far more often than under IP law. Here are 2 examples.
Lists of potentially interested buyers or a customer’s purchasing history with your firm.
The most efficient in-person or digital way to reach a particular reputable businessperson with a warm reception.
You could brainstorm more types of trade secrets. But the examples I gave are not the greatest kind of trade secrets. Explanations are. As discussed earlier, they are what comes first in any creative process. They generate business experiments, prescriptions, and products.
An easy set of examples involve all cloud operators. All cloud-based software sellers use automated operations to handle customers, even with enterprise. Amazon’s offers and Google’s search results are outputs of algorithms, but these algorithms are the outputs of changes in Amazon’s and Google’s understanding of online retail intent and search intent, respectively. If we’re talking about Amazon’s and Google’s fundamental competitive advantages over time, the question isn’t how these algorithms work, but why they work.
Search engine competitors try to reverse engineer Google’s results all the time, and with enough time if Google got lazy, these underdogs would succeed. Likewise online retailers who seek to steal Amazon’s share of digital shopping bags would do so if Amazon stopped trying to understand its customers (in the language of Bezos).
Here’s how Google and Amazon can keep winning, and alternatively, how rivals can beat them.
You Could Lose Your Fundamental Competitive Advantage at Any Moment
I’m going to show you why trade secret explanations are, all at once, equal opportunity and secretive. A market is, broadly defined, made of these players: Your firm, customers, competitors, suppliers, and regulators. Your in-house causal understanding of the market is, once improperly accessed, accurately copied, stored, and used. It does not care who accesses it. But they can also be kept from getting copied.
Trade secrets are lost in three ways: sloppiness, theft, and reverse engineering. I have nothing to add at this time to caution and security, but I have a contribution to anyone who wants to make reverse engineering more difficult.
So bear with me, and let’s talk about how to squirrel away your winter nuts from rapacious and envious competitors. They really suck, I agree completely.
Defend Against Reverse Engineering with Greater Understanding
Now here’s the point about reverse engineering: You make it harder for people to do it, even if they have part of the trade secret, when your in-house causal understanding runs deep. In short, the smarter your team is about the market, the harder it is for any entrant or competitor dialing up expansion to reverse engineer any successful action you have taken. Any action–as long as it came out of a deep causal understanding.
Observe that you could create an explanation-based moat to your trade secret knowledge out of thin air. As in, you don’t need to gain genius, but we’ll get to that. I’ll use Coca Cola as an example. There’s speculation Coke keeps parts of its formula separate, with no one person knowing the whole thing.
The famed and secret Coca Cola formula is a prescription, a design for soda bottles. But the connection between its use and the result is a matter of factory engineering and other forms of knowledge. Coca Cola needs a good causal understanding of machinery, suppliers, and other things to make its soda. The connection between the Coca Cola formula and other tools is explanatory.
Likewise, if Coca Cola does happen to keep parts of the formula separate, with no one person knowing the whole thing, then connecting one piece of it to another is also explanatory. The firm’s exclusivity over an extremely valuable trade secret here cannot be lost unless someone does some serious cognitive labor. (If we ignore theft and sloppiness.)
If you don’t even have part of the formula, you have to work backwards from the soda. This too involves explanations, and of course experiments. But there is a practically infinite number of ingredient combinations, making experiments–essentially luck from iteration itself–alone not enough. An explanation, a creative leap, is necessary. This is not a matter of procedure, it is an unpredictable outcome. If you told one of your employees, ‘go make me an innovative product’, they may do it, or they may not, but you’d be a fool to expect them to succeed.
Take an Informative Detour Into Evolutionary Selection
Consider that even a dust-kicking, ‘let’s see what happens’ experiment encodes knowledge, just a more primitive kind. When you hold a soda bottle in your hand, that is tactile ability. When you see a soda bottle, you use visual ability. When a subject drinks soda, they use taste ability. These abilities evolved under long spans of time, where some genetic combinations were selected out of the gene pool and persisted via survival and reproduction of gene vehicles (people today, for purposes of this article).
With genetics, the genetic tools we have–visual, tactile, and others–are encoded prescriptions, like products. They are selected for by physical causes, whereas in business analysis, you select experiments, actions, and products based on abstract causes. I.e., explanations.
In contrast, you read balance sheets and reports using your eyes, the utility of which is a physical relationship between a fleshy UX design (the underlying genetic program) and the environment.
Notice that the distance between a businessperson (or any actor) and solving a business problem or enjoying an opportunity depends. It depends on the businessperson and the problem or opportunity.
A dog is a better drug sniffer than a person, reverse engineering the scent of drugs to the source, say a drug mule at an airport. But a person is a better counterfeit purse-maker than a dog. Pretty much all purses can be reverse engineered, made, and sold–at legal risk with a significant market potential.
Similarly some businesspeople have better causal understanding of the market. They select better solutions. You want to be one of them. You do it by improving your causal understanding. Our detour into a more primitive kind of knowledge, genetics, is over. We’re back in the land of abstractions.
Start Small with Your Unique Position and Grow it
As someone selling a product, you have first-in-line access to unique information. Even in a highly competitive market you will end up knowing facts and details that others do not. These facts feed unique explanations, which in turn generate prescriptions, actions, and products that your “competitors” cannot make or do.
As long as you and your team have the right attitude, this means you have a competitive advantage to grow your trade secret explanations. Read more in the sections below about mindsets and work culture.
An explanation is better the more it explains (completeness) and accurately so (precision). Explanations as we saw birth prescriptions, useful business actions like innovative products, through speculation and experiment. The better your marketplace understanding the more profitable your business actions.
In mirrored fashion, the more profitable the business action the more complexity it encodes. Any particular success may very well have been luck, but if it wasn’t, the author held causal understanding of the market. In either case you must–absent their sloppiness or your criminality–reverse engineer it. But to do that you’d have to create an equal or better understanding to the author’s own.
Your business actions encode the quality of the underlying explanations you have in house. A purse as I noted is easy to reverse engineer, but Google’s search algorithm is not. The difference is not arbitrary, it is based on the depth of the relevant explanation.
As an antitrust lawyer I got into the guts of real companies. I noticed that the microeconomics analysis had to include fancy Greek, but that the decision makers were basically paying lip service to it.
Great economists would paint a merger in a certain way with Greek as the centerpiece.
With both sides lining up top-notch experts, fancy economics debates would become a wash. By and large—I can’t for sure say this is true about every case.
Commissioners and federal judges don’t want to look stupid. Neither do I.
Their records and opinions end up making the Greek look like it was important. And it was—to the effortful process that preceded the decision. But from my point of view the Greek didn’t influence belief.
The Greek didn’t matter because it didn’t tell people how these businesses—sometimes close competitors—were actually working.
Markets are people making deals and building relationships. Markets are the way they are for reasons, sometimes conscious ones.
It is funny that economics is the rational study of something that, even at the level of a single business, is messy. Extremely messy.
Every organization I’ve ever been a part of, government and private sector, large and small, profitable and unprofitable, is chaotic.
It is exactly in this chaos that a real economist should sit. Speculating. Observing. Comparing. Predicting.
And failing.
Because not only do economists fail at understanding and predicting macroeconomic outcomes, businesspeople are almost as bad with their own businesses.
People, and markets, are extremely unpredictable. But there is a logic to money, to bills, to real exchanges of value, and all the things that make up economics.
Ordinary people are under the illusion that legal thinking is special. They shy away from it and put lawyers on a pedestal.
Laws are forms of knowledge. Nothing more and nothing less.
A Legal Rule Makes Things Happen
Legal rules are condition-action sequences, imprinted with a government stamp and held out to everyone in the jurisdiction.
If you murder someone, you’re going to jail.
If an official is corrupt, they lose the office.
People Make Legal Rules
Laws are ideas. Ideas are nowhere to be found until they are generated inside a person’s skull.
A legal rule makes things happen because people made the rule. In contrast, a rock crumbles over long spans of time not really because of what people do.
Laws Are Enforced Rules
For an idea to become a law, it must be enforced. That is done either through agreement or force.
But even the use of force requires agreement among the enforcers (police, judges, bureaucrats), political leaders (mayors, senators), and the taxpayers.
Legal ideas only work as rules so long as they are sufficiently similar to each other inside of the individual minds that enforce those ideas as rules. A legal rule only works, even when enforced via violence on the back-end, if enough good copies of it exist inside (in-)group members.
We Make Laws to Give Ourselves Options
Laws give options. Even to murderers.
A law is prescriptive, it is a type of claim evaluated for utility. In contrast, a legal theory or rationale is explanatory and it is evaluated for its truth.
A useful law is useful for reasons. A bad law is bad for reasons.
If the underlying legal reasons are bad, the law will be bad.
Rationality matters when it comes to lawmaking and legal analysis.
Laws Should Change
Any legal rule, more generally any prescription or any tool, will have some margin. It will have its day. Some range of reality, some set of physical objects, will respond to the rule.
A tool we think is useless now, like a rock as a weapon compared to a gun, will be useful in the future. Some ridiculous refuted theory might have some truth revealed to us in the future, revealed via criticism in dialogue.
We do not know how much of reality will falsify our current beliefs, because we do not know all of reality. And we cannot know it all because all our knowledge has relative value, as far as we know. As far as we have seen, every idea is comparable (and with enough time, compared) to rival ideas.
Ideas die out. And in open minds, they die out because a replacement came in for the kill. So every idea, including prescriptions, will run out their margins, their utility. All ideas are destined to become a cost structure that does not generate revenue. All tools are conditional, as are all forms of cooperation, including legal regimes.
This is why it is good to store past-generated theories. And legal rules. Because they offer options.
And when they are subsumed into grander theories, our storage needs go down. This is consilience.
Legal theorizing is subject to consilience as a process. We make legal rules and legal explanations. They accrue in a pile. And lawyers dig around and combine them in different ways. The pile is made smaller because of creative work, even as it is made bigger because of the same intelligence.
