Product Management: Syntax and Grammar

Even in an era of AI (perhaps even more so than in prior regimes), I really do believe that a strong shared language with syntax and grammar can help product managers communicate more precisely in order to ship higher-quality products and continue to grow our set of capabilities quickly.

This is the canonical essay about the co-evolution of system design + org design - from that we derive concept of organizational nouns (e.g. teams) and system nouns (e.g. components).

Then we have the concept of organizational verbs (e.g. initiatives) and system verbs (e.g. capabilities).

Initiatives tend to be offensive (they are creating new components + capabilities) and can be staffed by dedicated teams (pods) or people from many different component-oriented teams (squads). That's why they are oriented around CapEx...whereas OpEx is more defensive and tied to maintaining the existing set of components + capabilities. Including a couple diagrams below that I think are helpful showing how development, packaging and marketing fit together with the organizational structure of pillars / teams / pods and work breakdown structure of initiatives and epics to create a roadmap of milestones which connect units of scope (what) with staffing (who) to produce dates (when).

Different elements of the end-to-end system architecture

How the organization reflects and directs the system

Full talk on product architecture from the Denver Product summit: link

As Committees May Think!

Earlier, I wrote about how a discretized set of fundable investments could be converted into a graph (the tech tree), and then refined into a list (the roadmap) consisting of a sequence of active and future workstreams (link).

From the famous “How Do Committees Invent?” Conway’s Law indicates that there’s always a relationship between the organizational design and the system design. But similarly, there’s a relationship between that and the way work is laid out - the system, the organization, and the work are all connected - and must be designed in an integrated fashion.

As new work gets prioritized (especially in a iterative, decentralized and evolutionary design organization), workers end up being split between “offensive formations” which are oriented around new initiatives (often working on new components of the system which don’t yet exist) and a “defensive formations” which correspond with already-developed aspects of a system.

Thinking further on the topic, it's interesting to me how many of the common visualizations end up being different representations of three fractal entities: an organization, a system and a collection of work (who, what, and when). Many of these visualizations are monads or dyads, but I've never seen a compelling triad - probably because two-dimensional projections of a three body system are necessarily reductive.

• An organizational chart: Shows people and their reporting relationships, often struggling to represent the inherently cross-functional nature of leaf-level working teams in a more matrixed EPD organization.
• A roadmap: Shows a sequential list of projects, generally ordered sequentially in a notional representation of logical time but disconnected from wallclock time.
• A gantt chart: Shows a piece of a roadmap arranged with high-level work items swimlanes with incremental milestones and lined up with absolute wallclock time.
• A kanban board: Shows low-level work items 
• A team planning chart: groups work items by person, showing their key priority (ideally using a quadratic layout to show decreasing fidelity (1 week, 1 week, 2 week, 1 month, 2 month for a full trimester view).
• An system architecture diagram: Shows the relationships between different components of a large technical system.
• Dataflow diagram: shows how data flows between different elements of a technical system.
• An information architecture diagram: Shows the relationship between different interface elements, often related to a site map but with more visual complexity.
• Concept diagram: shows the relationship between different concepts which show up across the UI + API in different components (both services and interface elements).
• A responsibility matrix: showing assignments of people to projects or system components, often with a role (responsible, accountable, consulted, informed, owner, DRI).
• A technology tree: shows a zoomed out version of a roadmap

Common hierarchies for each level include:

• Organization: Person, Team, Group, Business Unit
• Work: Initiative, Project / Epic, Story / Task / Issue
• System: Subcomponent, Component, Component Family?

But it's interesting, because there really isn't a dynamic way to see the state of these three in a unified way someplace. Just sorta fascinating to me, because it's not a complex system, but because it's fractal, human program / product / project managers + engineers + systems engineers + architects try to keep it all together.

It's also fascinating that while code compiles into build binaries, it does not compile into system or work diagrams, though obviously there is huge amounts of source code metadata which could be used to construct these.

Apps and Maps: Using the iOS Developer Ecosystem to Attack Google

The fundamental challenge in consumer mapping is that there is no perfect map - different users need different maps at different times.

The same problem existed with PCs and smartphones - Microsoft and Apple both created App Stores and development frameworks to provide users with the ability to customize their device.

Over the past few releases, Apple has been opening up the iOS ecosystem to allow individual apps to push data into central Apple-owned plugin points in order to support cross-app interoperability and improve the multi-app user experience. Features like Apple Wallet, Apple Health, and Live Activities all allow apps to deliver app-specific context into specific areas of the operating system and provide more seamless experiences.

So far, Apple Maps has mostly approached consumer mapping the way Google did, implementing a single lowest common denominator basemap and allowing applications to embed that base map inside of themselves. But as an ecosystem, there isn't a good way for apps to push application-specific content into Apple Maps. Supporting this would allow users to customize their own map dynamically and would prevent the need for Apple as an organization to maintain a single perfect master map to met the needs of all users - especially across different regions.

Simply by installing apps, users would get dynamic layers enabled on their maps so that specific places could get highlighted based on the application suite the user had chosen to install.

Imagine a world where a user could toggle between vector layers provided by Chase, American Express, Marriott, Hyatt, or Airbnb to choose a hotel when thinking about booking a trip. Or between Resi and OpenTable to see restaurants that had open reservations. Rather than relying on the integrations that Google or Apple had developed centrally, Apple Maps could automatically populate with all of the layers corresponding to the apps the user had already installed - customizing your iPhone’s Map would be as simple as installing an App ("Share with Apple Health"). And rather than having to switch between a bunch of different apps when planning a trip or meeting up with a friend or landing in a new location, app-specific context could be surfaced spatially, dynamically pushing more detailed information to users based on zoom level and live activities.

The core of this is two features: an extension of Live Activities called Live Layers which would allow an activity to represent moving objects + routes on the Apple Maps canvas, and a feature called App Layers for pushing POIs (or possibly basemaps) into the Map Canvas. The most extreme version would let users actually subscribe directly to basemaps, eliminating the need for Apple to maintain a central basemap and pushing everything into the Overture ecosystem - at this point, Apple Maps would simply provide scaffolding for spatial appmakers.

Imagine seeing your Uber car, DoorDash delivery and your husband’s shared Lyft ride all converging on a friend’s house for a birthday party! Imagine having your Lime scooter automatically populating with your hotel address, or using Handoff to send data directly from desktop / web to your iOS device for easy navigation.

Now imagine this as context for Apple Intelligence - allowing Siri to answer a whole host of critical “who, when and where” questions based on the live information flow into the on-device spatial intelligence engine. Imagine landing in a new airport, trying to get to a tight connection - maybe your United App could provide turn-by-turn Apple Maps directions based on LOD which was being dynamically injected by the United airport map. Or asking a HomePod when the food was arriving while you’re trying to prepare for a dinner party. Or using your Apple Watch to order a car to the airport while you’re frantically packing for an international trip. The background context being injected into a central Map is effectively identical to the user’s mental context - key information about their life which is critical for answering their most important questions.

From a developer perspective, iOS would be able to provide gazetteer elements which synced across apps, GERS-focused advertising (“AdSpots” vs “AdWords”) for spatial apps to bid on to drive app downloads, and to push users into a more app-friendly environment where incentives are more aligned (vs Google where the goal is to own the end-to-end experience and take a large cut via referrals and ads). It could be a big way to get the industry on board with trying to shift users en masse off of Google Maps.

Meetings are the Dark Matter of Enterprise Cybernetics

We are going through is a transition period - from human reasoning to machine reasoning; given the previous revolution from human computing to artificial computing, I guess you could just say that the broad ~100 year arc is from human intelligence to artificial intelligence and just call it an AI revolution [1].

As such, I've been thinking a lot about how to use AI in the context of an existing business to streamline internal operations.

And while pieces of the business feel tractable based on applying AI to existing systems of record or operational processes, automating large swaths of a business feels too hard. Too much reasoning is illegible to machines - because it happens during meetings.

Meetings are the dark matter of the enterprise - the vast majority of "context" about what's happening at a business is transmitted verbally, and needs to be represented digitally. So a key part of transforming between the analog business to the digital business is about making meetings legible to machines.

That's why I think Granola.ai is going to be the Killer App™ for the next generation of enterprise operating systems - I'm only testing it out right now in a personal capacity (it's currently quite limited from a security perspective and not enterprise-ready), but it's the first application I've used that really feels like a step-change for personal productivity. Chat apps are nice for search, and I think they will continue to be useful. But they still feel like work - the experience for most LLM chat apps is still relatively similar to a better search engine (you swivel chair to it, do stuff, then swivel back). Granola is the first app I've used that's non-zero sum with my time; it inhabits the same time as I do, and makes that time more productive. I can't explain it exactly, but using Granola feels like - oh yeah, this is going to be ubiquitous in 5 years. Maybe it doesn't win the category (will be a battle), but this category is going to be the first non-chat Category of LLM-powered apps.

The problem is that in its current form, it's just a tool. It's going to be picked up ubiquitously and let people do better meetings - what it's NOT going to do is automate away meetings or massively accelerate operational productivity.

That's where the Ontology comes in - because right now, Granola is just a generic application. The opportunity is to use the Ontology to turn it into a Platform. Today, Granola has a basic templating system with generic out-of-the-box templates for specific meeting types. By applying a decision-centric data model to it, you could map meeting types to object types, and then include functions and actions in the UI where it currently provides some generic out-of-the-box actions (send email, list action items).

So now you've created a virtuous cycle - you use Granola to capture meetings, those meetings become data about the actual reasoning and decision-making in the enterprise, and then the meetings can be automated, orchestrated, agentified...ontologized.

Today, there's a feature in Granola that creates "action items" - imagine if each of these action items corresponded with an Ontology Action Type - with the right ontology, the Action Items from a partnership kickoff call could all be invokable Ontology Actions - one to create a Jira ticket for reviewing API docs, one for initiating a Legal ticket to get a partnership set up, one for scheduling a kickoff meeting, and one that automagically created a Slack channel. And they don't need to be actions yet - just having the Meeting <> Action Item mapping for all meetings of a given type lets you begin mining the plaintext action items to create semantic action types (and an Action Type <> Prompt Hint mapping so that future Action Items could be translated into invokable Ontology Actions via semantic search).

At this point, the UI would transform the text into a clickable bullet automatically orchestrating an entire system of action which could be kept in sync as more meetings occur and actions move through a Markov chain of semi-formalized state changes - "okay, you decided to transfer Phil to the Mobile team - let's kick off the process of talking to Phil, confirming the transfer, and then registering this."

To automate decisions, they need to be formalized and given that most decisions happen in meetings, this means that automating decision-making will require making the meeting context accessible to machines. Over time, this will enable us to progressively titrate decision-making authority from the human to the machine as humans shift from being decision-makers themselves to being the makers of decision-making machines.

[1] It's only tangentially related, but I do think that this Eric Schmidt talk was an interesting read about the steam to electricity transformation, which might be a good historical analogue to consider.

Planning as Statetime Manipulation

"All happy families are alike; each unhappy family is unhappy in its own way."

[1,1] [1,0] [0,1] [0,0]

Successful large technical systems represent a bounded region of a near-infinite space of possible configurations.

Unlike purely mechanical systems, which are defined purely by a limited set of physical dimensions, the dimensionality of LTSs is highly abstract, often consisting of intricate couplings between sub-systems and nuanced relationships between both the human and artificial agents who perform actions (who have agency) in the context of the broader system.

Making large-scale changes to these systems is the responsibility of the organizations which steward their evolution. Traditionally, this is done by conceptualizing a vision, developing a strategy to achieve that vision, breaking that strategy down into a plan, and then executing the plan.

The words we use to describe this activity, which is the core activity that all system developers (and therefore all modern leaders) must excel at, are anchored in skeuomorphism - they are loanwords from a prior era in which the largest systems (armies, cities, factories) were primarily physical entities.

In our cybernetic age, though, these words are mere analogies.

Modern system design is not just about manipulating space; it is about manipulating state - and that means that the most critical aspect of system design is figuring out how to represent a given system's state. What is the right way to conceptualize it? What set of words, diagrams and quantitative metrics should be used to describe the space? How to balance accuracy with simplicity? How to evaluate different representations?

Put another way, the first step of manipulating state is making a map.

Maps are an ancient tool for representing physical space; schematics and blueprints are a more modern tool for doing the same. The tool for representing state space hasn't really been developed yet - in most organizations, it's most commonly represented in Excel, often for the purpose of financial reporting and budgeting. But I think that the more appropriate tool is probably a variant of the ontology system we developed at Palantir, and the process of constructing an ontology is akin to cartography.

Mathematically, this map, or ontology, is effectively a manifold - a low dimensional representation of the high dimensional space. By definition, this description is lossy - the map is not the territory, all models are wrong etc. Pragmatically, though, some models are better than others - they can be manipulated more productively.

In this endeavor, it's fascinating how many words we use derive from this notion of a map. Vision is about describing a particular region of state-space in enough detail to communicate it broadly. Strategy is often described as "charting a course" to that future vision and avoiding barriers. A milestone, which literally derives from a physical stone placed along a one-dimensional road or path, represents a discrete position within state-space. A single team's roadmap may represents a vector connecting multiple milestones while the organization's roadmap is a zoomed out version representing all of the vectors actively being manipulated by the organization.

These analogies are helpful, but they miss the key point. Unlike a traditional map, which represents 2D projection of our 3D globe, our map is purely imagined. It does not exist in any absolute form. Of course, this is loosely true in cartography as well. The NYC subway map is designed to show subways and warps the region for aesthetic and practical purposes. But spatial cartography is always anchored in physical reality - the territory is at some philosophical level observable, while ontological cartography is a more unbounded exercise (see also Cartographic Grounds: Projecting the Landscape Imaginary for a whimsical overview of cartographic creativity).

To conclude, our modern software systems are highly dimensional; but our mechanisms for understanding complex systems have evolved based on our need to understand lower-dimensional, predominantly physical systems.  

This doesn't make software easier or harder to design and manage - it makes it different. Hardware is hard because doing it is hard; software is hard because defining it is hard. Software has eaten the world, but our ability to conceptualize and visualize these cybernetic human-computer symbiotes has lagged our ability to build them. At some visceral level, we do not understand what we have created. This is not uncommon; the dance between theory and practice is rarely linear.

But the conclusion is clear - in an era where theory has gone out of fashion in science, it's more critical than ever in business. The idea of a map only gets you to the starting line - creating the right map is the foundation of everything else in a modern business.

Anti-agglomeration Policies

 The entire developed world is facing an extremely acute housing crisis; as agglomeration effects drive more global talent into a smaller number of international supercities, local incentives bias towards increasing real estate values. These incentives are in direct conflict with a clear national and international interest in increasing the rate of technological development, as real estate extracts an increasing amount of value from the innovation created via agglomeration.

Historically, technology development was widely distributed - the unique geographic features of distinct regions and the high cost of transportation allowed individual cities to leverage their unique natural environment to facilitate the development of localized supply chains and innovation hubs. Toledo was a glass city, producing windows and windshields for Detroit; Pittsburgh's access to iron and coal made it a natural place where steel could be produced.

However, with the rise of container transportation, global air travel and widespread internet access, goods, people and information are now able to move across space much more quickly. As a result, the physical barriers to agglomeration have been largely eliminated - the natural result is a drive towards consolidation around a smaller number of winning cities.

This effect is particularly acute in America, a country accustomed to boomtowns and their inevitable busts. But it is not limited to America. Innovation-driven capitalism naturally optimizes for agglomeration as density creates the human connections which fuel the development of new ideas and creation of new products and services.

One obvious solution is to examine the spatial distribution of major government functions. The United States is a federal republic; our government does not need to be geographically localized in Washington. By intentionally picking new locations for critical government agencies, policymakers could redirect their employees to communities which need investment. Much like universities can create geographic gravity wells around college towns, moving large swaths of the federal government into states would create similar hubs - helping agglomeration revitalize cities across the country that have been left behind by our increasingly globalized logistics systems and airborne travel networks.

It should be easy to organize a dedicated lobbying effort to rally Congressional support to eliminate all executive presence in Washington, DC. The Department of Agriculture could move to Des Moines; HHS could move to Cleveland; the Department of Energy could move to Houston; the Department of the Interior could move to Bozeman. Put each agency close to a city which already has an affinity for that kind of work, but which has lost population over the past 50 years. Critically, this should be extremely popular within the legislative branch - with the exception of Virginia and Maryland, every state could be a winner of a meaningful part of the ever-expanding federal bureaucracy.

Cyclists, Drivers, Runners and Pedestrians

One challenge for urban designers is that cyclists exist in an awkward superposition state between pedestrians and drivers - bikes are larger than people but smaller than cars; cycling is faster than walking and slower than driving.

Across the road network, therefore, we've generally accepted that dedicated bike lanes are an appropriate compromise - they create dedicated space for bikers ranging from 10 mph to 25 mph at the cost of ~6 horizontal feet.

The rise of electric scooters and bikes, however, necessitate a similar segmentation on mixed-use trails - because electric assistance increases the median speed of a bike has increased from ~10-15 mph to 15-20mph, the gap between both walkers and runners and cyclists has become much larger. Especially on highly-trafficked trails, situations in which two cyclists at different speeds are trying to pass pedestrians is becoming increasingly common.

The solution is to split off a packed gravel running and walking trail from an asphalt-based cycling trail. It should be well-graded to support strollers and ideally isolated from the biking trail as much as possible - e.g. by putting a pedestrian trail on one side of a river and a bike trail on the other side.

Leaving Palantir

Most well-known technology companies achieved their initial success by creating a singular breakout product which dominated an emerging category. Each story is different, but the pattern is similar.

But even after twelve years, the uniqueness of Palantir’s story continues to fascinate me – the lack of explicit hierarchy inherent in the company’s design creates a superposition of possible company structures, each only accessible from a different vantage point. And the symbiosis between us and our customers makes untangling what we do from what they do especially challenging.

Studying Palantir has been something of a hobby of mine over this past decade, and over the past few months, as I’ve started to feel my time here coming to an end, I’ve tried my best to distill my reflections down into something transmissible.

In my time here, I’ve been right about many small things, but I was wrong about one big thing – programs, not products or platforms, are the defining concept at the heart of Palantir.

Shrink-wrapped SaaS products may be good businesses, but they are a bad way to shape the world predicated as they are on an end of history worldview which implies that zero-sum optimization and financial engineering, not positive-sum growth and technology development, will be the defining activity of our generation.

Cloud-based computing platforms are also good businesses, but their optimism is indeterminate in nature, with a teleology defined by their users; a platform is a tool, but it has no intrinsic purpose. Great platforms are always a consequence of successful products, and their value is always defined by the next generation of products built on top of them; they are supporting actors solidifying known patterns into a strong foundation, not prime movers pushing on the ceiling into the unknown.

Programs – living cybernetic systems consisting of data, logic, and action – are the things that Palantir has learned how to build, at extreme cost. Programs are n-of-one entities; instances which are members of a class, but with unique identities, designed to be different and therefore to differentiate. We have collectively built these programs as an output of a tightly integrated business model – our best BD work combines elements of strategy and technical consulting, and our best PD work intentionally blurs the line between traditional software development and a classic services approach.

Producing programs is what Palantir was designed (has evolved?) to do.

But perhaps our work here has an even higher purpose, bordering on the spiritual – to challenge those of us who pursue it, and prepare us for our next adventures. Why did PayPal and General Magic, not Google and Facebook, spawn Silicon Valley mafias? Where are the Snowflake and Databricks founder mafias? Why did my brothers and I work for ABL, Tesla and Palantir, not Boeing, Ford, and Microsoft?

Culture, not technology, may end up being the main thing these institutions end up contributing to the world; people may turn out to be their most enduring legacy.

Palantir has a very special culture – of agency in the face of bureaucracy, of engagement with the world, of curiosity about how things work coupled with an optimistic belief that the future can be better, in concrete and achievable ways, than the present. A community of pragmatists, programmers, and philosophers. It’s a quintessentially American culture that transcends American geography, a melting pot of ideas and people whose whole is greater than the sum of its parts.

We are, and have been for some time, in the early stages of going supernova – spreading this unique culture across the broader industry and society, and I’m excited to join the diaspora.

Since I joined Palantir twelve years ago, there’s never been a bad time to join, and there’s never been a good time to leave; my work here isn’t complete, partially because it isn’t the kind of thing that is completable. The beauty of building a mission-driven company is that even as each campaign ends, the movement itself regroups to focus on the work ahead.

And yet the time has come to leave - to peddle my wares on the open market of late-stage capitalism and sink my teeth into a new challenge. I'm stepping onto the well-trodden path from software to hardware, joining an exquisitely capital-intensive space company and hoping to help them build an integrated hardware + software product strategy, something I've always been fascinated by theoretically; but as they say - in theory, there's no difference between theory in practice, but in practice, there is!

Perhaps I can even bring an ontology into my new board room, and into our new operations centers - the most forward-deployed engineer.

VLR!

Eight Sleep Thoughts

I've recently acquired an Eight Sleep, and have been enjoying it.

But broadly speaking, it also creates a culture of intensity around sleeping and bedtime which seems ill-advised.

A few things in particular have been bothering me: an imposition of various values on the user (namely not spending time in bed without sleeping), the lack of couple-oriented features and analytics, and the requirement of having a phone near bed to control the device at all times.

More generally - by definition, the people working on products are more focused on them than users are. Users use a wide range of products in conjunction with one another. 

I love reading before bed. And sometimes, I like lounging in bed in the morning. I'm not a robot pro-athlete / weirdo tech bro trying to optimize my every minute; I'm a hard-working white-collar worker who sometimes likes to relax in bed.

I want to get great sleep. I want to sleep well, and I want to develop good routines. But imposing sleep-centric values on me is generally frustrating, and candidly makes me less likely to feel good about the product and less likely to refer it to other friends.

Strava Adventures

Currently, Strava's feed is composed of activities. Increasingly, however, athletes participate in multi-activity adventures - think bikepacking trips, hiking journeys and ski touring adventures.

Adventures pollute the activity feed by over-posting, and reduce the amount of social behavior by partitioning comments and discussion across semantically related activities.

Strava should introduce a new concept (an adventure) which groups together contiguous activities for the purpose of increasing the prominence and engagement on the feed while also helping users remember particularly meaningful adventures by selecting key photos to highlight the experience.

Excel-based Azure Functions

The world runs on excel, and increasingly, Excel documents are store in O365 / Sharepoint / OneDrive - in the cloud.

Microsoft should support turning any Excel document into the source code for an Azure Function so that you could have lambda functions for every single macro in Excel, easily available for developers to hit.

This would continue to drive people into Excel while also supporting the transition of businesses towards interconnected API-based companies.

The pathology of Google's carbon-based flight metric

“A strange game. The only winning move is not to play.”

Google recently added a number of fairly odd features to their flight booking system that claims to help travelers learn how much carbon a given flight will release into the atmosphere (link).

But the numbers are misleading.

It's not exactly clear how they are computed, but even if they were correct on average, the irony of the feature is that a set of uncoordinated but climate-conscious consumers could end up increasing the aggregate amount of carbon for a given set of flights if they used these numbers as part of their decision-making process.

In fact - it's almost certainly true that price is a better predictor of the actual carbon emissions of a given route than the made-up Google metric. The cost of a flight is composed of a bunch of fixed costs which the airlines then try to make up on the margin by selling every seat on the plane. A full plane, broadly speaking, is the most carbon-friendly plane: and it's also the cheapest plane for everyone onboard.

Carbon emissions are obviously always lower for a direct flight assuming a full plane, but more direct flights between the set of airports would result in planes which are, on average, less full. There's some bin packing math to do here, but the basic premise is that while the naive calculation for me may imply that the direct flight from London to Austin only costs 731 kg of carbon dioxide, occupying one seat on both of the London to NYC and NYC to Austin flight may end up with fuller flights, thus reducing the per-capita cost of travel: both in carbon and dollar terms.

If consumers were to use this tool, therefore, the result could actually be to push airlines away from more efficient hub-spoke routes towards direct flights with lower utilization.

Of course, it's hard for me to actually believes that anyone actually think this matters: once you've chosen to fly to London, you've basically made the call that you're going to use a tremendous amount of carbon dioxide no matter what route you pick. And implying that the choice of flying direct is 20% better for the environment is just lazy - if you can book the flight, the planes are already taking off with or without you.

Climate change is not a problem that will be fixed by individual action: if we want to fix this problem, we need to pursue unpopular policies to increase the cost of carbon. As a consumer, trying to do multi-variate optimization for every choice is an exhausting way to make decisions; it's why having the almighty dollar, rather than a cryptocoin for every commodity, is the way we evolved out of a primitive economy into one based on the free exchange of goods and services.

Which leads me to the question: why did anyone at Google green-light this project?

The most cynical answer: it lets rich people (like Google employees) who can afford direct flights feel good about their ability to pay more money for convenience by giving them a hedonic boost when they book "carbon-friendly" direct flights (which again, they were going to do anyway, because they are actually paying for convenience). 

My Favorite books from 2020

2020 was a busy year - and so I'm writing my top five books list in February, 2022 - fourteen months late....I'm also ~20 reviews behind on Goodreads. Not a good sign...

Anyway, doing this is how I stay on top of my reading; producing, not simply consuming, and drawing the connections between books that make reading fun.

1. All the King's Men

Turns out there's a reason why this is considered one of the best American novels. It was a well-written story with characters who were frustratingly relatable in frightening and intriguing ways. Hard to say more than what's already been said, but for me, the beautifully oblique way that the unreliable narrator slowly revealed information over time so that I was constantly trying to figure out what was going on and how reality was being filtered through the narration, as well as the tragic interconnectedness between all of the characters was simply sublime.

2. Achieving Our Country

This book is a solid meditation on two formations of liberalism - the progressive liberalism of Dewey and Whitman set up against the anarchist, Marxist liberalism of the anti-Vietnam 60s. It's the final act of a philosopher who embodies one of my favorite traditions, American Pragmatism, and is of particular relevance in its prescient analysis of the dangers of hyper-woke politics.

3. The Eighth Day Of Creation

A very engaging and beautiful story of the discovery of DNA and the invention of microbiology as a sub-field. The greats like von Neumann and Watson and Crick and Monad and Schrodinger show up, but as humans, people, working on science, not as members of the pantheon. More than anything, this is an ode to the power of theory - and to Francis Crick, the ultimate theoretician. Pairing nicely with The Innovator's Solution, it's a book I go back to regularly, wondering why we haven't cracked the code past the Central Dogma...multicellular life is just fundamentally...different.

4. The Innovator's Solution

Ah, Christensen - the first business theorist I really jived with. Read his Innovator's Dilemma years ago, and this is a great update, extending his theories of modularity and integration (very Kuhnian in his own way). As a consummate defender of big businesses, working from the inside, he did a good job describing how to invert the paradox he identified earlier.

My favorite books from 2021

It was a year of Caro, and a year of change. I left California, and moved to Colorado with my future wife. I found myself more unfocused and disconnected than I expected, still working in a hybrid capacity which didn't come naturally to me, and with less of a social community than I wanted. I was also growing up, beginning the slow transition from a culture taker to a culture maker, realizing that I was becoming more of a leader than a follower. Perhaps this is why the biographies of Caro spoke to me in this moment. The raw unfettered potentiality of youth was fading.

1. Where Is My Flying Car?

Perhaps the urtext of what has come to be called the e/acc movement, this was a radicalizing book which inspired me to become more frustrated with the status quo. Something about reading it in the wake of leaving California and the COVID pandemic made me specifically angry, and this book grounded the anger in specific complaints about GDP, watts per capita, safetyism and other critiques. Presaging works like Abundance and Breakneck in 2025, this was a techno-utopian manifesto before that term became popularized by a16z and the American Dynamism movement.

2. The Structure of Scientific Revolutions

I'll admit that Kuhn's work didn't click the first time I read it. But by 2021, I had settled into my natural state as a theorist, and was working through a longstanding paradigm shift within Palantir, consolidating the ontology systems across Gotham and Foundry and ushering in a new era of the Ontology. This book was very helpful, especially in the concepts of incommensurability and the way in which many paradigms were rejected. The concept of anomalies, and of the formalization which ossifies and then gives way to more plastic eras was formative for me.

3. Master of the Senate

Some great men seem relatable; Johnson is not, for me. Kicking off with a beautiful ode to the institution that is the US Senate, in a world where traveling to and from it was not a simple quotidian flight, this was a book which stuck with me in its unabashed, not quite admiration, but perhaps awe, at its subject. Larger than life, Johnson was at the peak of his powers, a legislator with no peers. Also relevant for thinking through how the Senate actually worked!

4. The Power Broker

The worst best book - an incredible work of fact that reads like a work of fiction, perhaps one of the most destructive books in the history of the 20th century, a harsh counterposition to Storr's technological positivism. There isn't a book like it, and it was a beautiful read - a book I wished had gone just slightly differently, and a book which tells the story of the delicate intertwining of technology and humanity - the car and the man who bent the world into compliance with it.

When users are speculators: Can web3 apps generate sustainable usage?

Two of my previous posts were about the relationship between labor and capital in innovation-rich parts of the economy. Roberto recently asked: "I'm curious if you have any thoughts how the incentive structures change with web3 / crypto?"

I'm not a crypto bull, though I'm interested in the space, and I honestly think that crypto-economics has a related, but distinct modality of disruption.

In Observer or Participant I argued that investors, by betting on success, can reduce its likelihood. With crypto, I suspect that the same perversion can occur, at least in network-driven applications.

Consider a model in which users of a decentralized application, who have been granted tokens which increase in value as the application becomes more valuable. For the marginal user, is it better to a) participate in an existing application, or b) become a pioneer in a new clone of that application.

Without the user-owned tokens, the answer is clear - the value of the network will roughly track the number of users, with quadratic value a la Metcalfe's Law as an upper bound. The marginal user will receive more value from Application A than from Application B.

But in a crypto-economic world, the user's value equation is different: users receive value both from their usage of the application and from their token. I'm sure there's some math that we can do, but assuming a non-inflationary token (e.g. the first N users get the first N tokens), it's not clear that the same incentives hold. In some sense, you've turned your users into employees, and just like the investors in the previous post, these employees may find it more valuable to cash in and leave rather than staying - selling Token A at the peak and then joining Application B seems like it would be value-maximizing in many cases. And certainly for the marginal user, it seems rational to take a bet on Application B.

In other words - while it may be frustrating to users that Facebook's founder and employees have been able to capture so much of the value that Facebook's users have created, the new incentives in a token-based world may actually make it impossible to create networks with as much value as their web2 competitors; rather than winner-take-all, web3 network effects may actually drive a tremendous amount of internecine warfare with splintering communities forking and re-tokenizing each other's networks. Empirical results from the currencies (BTC / ETH etc) seem to imply at least some amount of stability, but we're in the early days, and the impact may not be what we expect - jealousy is a powerful motivator.

Throughput trumps prioritization

One thing I've been reflecting on a lot as I read about various "theories of vaccination" is the key philosophical distinction between people and their proclivity to think temporally - to make decisions in the present which reflect a belief that the future is both predictable and mutable.

This comes up in a lot of theories of innovation: growing the pie vs slicing the pie, centralized management structures focused on "efficiency" versus artist colonies focused on "creativity," Thiel's "determinate optimism" vs "indeterminate pessimism," focuses on journey versus destination, and whether to emphasize economic growth over economic redistribution.

In this case, we see a fundamental tension between throughput and priority, between efficiency and effectiveness, between theory and data, and between the present and the future.

Like with most dialectics, I suspect there is merit on both sides, but by focusing so heavily on how to most efficiently distribute the vaccine we have now, knowing what we know now, to do the most good, we're missing the point - our goal should be to develop a decision-making framework that engages with an uncertain, but predictable, future in order to do the most good over the next 12 months.

More importantly - good prioritization with low throughput is almost always far worse than high throughput with bad prioritization.

• High-throughput systems can avoid decisions - rather than deciding between two high-priority goals, a high throughput systems can do both.
• High-throughput systems can pivot faster as priorities change - forward momentum is almost always easier to redirect than the inertia of a system at rest.
• High-throughput systems produce more information - because the system is doing more, it has the capacity to learn more quickly. It spends less time in an ivory tower, more time tinkering.
• High-throughput systems are more fun for the participants - every member of a high-throughput system matters and can individually impact the goal of doing more and moving faster. Rather than simply waiting for orders from above, when the system is focused on throughput, every participant can help on the margin.

In other words - when you don't know what to do, doing anything is better than doing nothing, and building a culture of "doing" is far more important than building a culture of "waiting." Maybe people get a few extra shots, maybe we accidentally miss a few second doses, maybe we accidentally give the vaccine to someone who didn't "need it." But vaccinating the population as quickly as possible is the goal, and we should be spending as many of our human, scientific, and financial resources to get that done faster, even if that introduces some randomness and inefficiency along the way.

Observer or Participant: Can the invisible hand sow the seeds of its own destruction?

As discussed in the previous post, companies in rapidly developing markets need to run effective idea factories - organizations of extremely talented and motivated humans arranged to bring new ideas to market. While markets often reach a "peacetime" state of equilibrium in which a few key winners dominate, the transitional "wartime" state is often marked by intense competition (e.g. the Browser Wars or Cloud Wars).

This competition happens between competing idea factories as capital gets injected into a company and intellectual property comes out the other side until one of the factories achieves some kind of "moat," at which point the war concludes and the winner has a defensible business which can produce attractive profits for a long time.

Different organizations have different inherent talent - as described by Matthew Ball, Disney and HBO regularly outperform Netflix in the efficiency of their content production. And that talent is distributed between employees and the organization. But regardless of the distribution, no matter how talented the organization may be, unlike a traditional company, IP-driven companies at war still rent huge percentages of their idea factory from their employees and depend on that talent retention to continue fighting.

So what happens as the market gets involved? Investors look into the future and pick a winner - the company which they believe will emerge as one of the dominant "peacetime" players. They do this, often, by looking at momentum and velocity: which organization is moving more quickly, taking more ground, developing more features - which idea factory is more efficient?

Then they discount that victory into the present (with a historically and unsustainably low interest rate) so that capital flows into the winner. This creates a uniquely low cost of capital for the presumed victor, all but ensuring their victory. Perception becomes reality as optimistic prognostications become the assumptions that drive present value calculations. In this model, victory itself is a self-fulfilling prophecy.

But underneath all of this lies a key question about incentives: in a war that depends on human capital, where that same human capital is compensated with stock in the enterprise, what happens when investors pay the factory workers so much for the factory's potential that the market shifts from playing the passive role of an observer into an active role, as a participant.

What happens when individual employees - not founders, or even executives, begin taking home 5-10x more than they expected? Do they continue to fight with the hunger that is necessary to win? Do they keep burning with the passion that is required for true innovation? Or do they, individually and collectively, take their foot off the gas? Does the hyper-capitalization of the idea factory perversely result in a factory that becomes less capable of victory even as it becomes more expensive?

And if so, what does that mean for capitalism - if capital can destroy value, how can we insulate small teams from capital and enable them to commit to long-term change, long-term success? What types of compensation mechanisms can we develop that are more stable, that result in more self-binding, more Ulysses Pacts where liquidity is available, but without the corrosive effect of hyper-liquidity? How could we design a compensation system in which the workers had stakes in their outcomes even in excess of their investors, where by deferring liquidity today, collective organizations could agree to provide liquidity only after victory was assured?

Today, we distribute the spoils of a future war to a present-day army, and too few people have thought through the unintended consequences as newly rich soldiers quietly leave the field in the midst of battle.

Human Capital Development: Renting an Idea Factory

The past thirty years have seen a massive shift in valuation, from an emphasis on value investing based on tangible assets towards momentum investing based on intangible assets. This, in turn, has led to broad based consternation within the investment community as conventional financial and economic models are outperformed by superficially less sophisticated approaches. Empirically, though, something is going on - it doesn't seem like our current models are working and in this scenario, new theories are required.

This essay is a reflection on the oddities of human capital, the production of ideas, and the complex relationships between employees, markets and firms in the modern economies. As the economy shifts from a focus on physical goods towards a focus on more abstract intangible goods, more and more companies are de facto becoming idea factories.

However, compared to actual factories, in which labor is rented from employees to operate high-cost machinery owned by a firm, idea factories rely on high-cost labor carefully arranged in order to produce finished products (e.g. software and content). The output of this idea factory is "owned" by the firm, but the firm itself is also owned by humans, many of whom may be key employees in the firm. This fact - that individual humans play many different roles in the context of a modern idea factory (founder / factory manager / idea producer / owner / investor) - is wildly under-discussed.

Too often, valuation models focus too much on a given firm's stock of intellectual property with too little emphasis on the half-life (depreciation) of that IP - here, we attempt to reflect on the flow of intellectual property; the capacity of a firm to create novel IP, which is a function of the organization's ability to recruit, retain, and grow talent (atomic human capital) as well as the organization's ability to organize that talent productively (emergent human capital) - in other words, it's stock of human capital.

Unlike other forms of capital, atomic human capital is owned by the individual and rented by the corporation. Its productivity is wildly variable, hard to measure, and subject to power-law dynamics: the productivity of a single human within a complex idea factory varies tremendously. More importantly, though, individual humans have non-linear atomic growth. This growth, over time, increases the capacity of a given human to produce value. Perversely, though, this growth also increases the bargaining power of that human to lobby for higher wages. As such, the return on investment in human capital development is much different and complex than the return on investment in traditional capital - labor, not capital, should be able to extract some large percentage of that created value, and firms end up competing with one another for access - in some sense, the value of human capital investments accrues to the individual, not necessarily to the firm.

But aside from Substacks / Newsletters, there aren't that many great projects where the value is around the sum of the parts - most great idea factories require the management of brilliant individuals together, not to make them replaceable, as in a traditional factory, but to make them irreplaceable as in a great championship winning team.

On the acknowledgement of messages

 Recently, I've been hit with a virus - I'm in a twenty-person text thread which was initiated, for purposes unknown, by a some kind of spam genius organization. Frankly, I love it - I'm proud of the spammers: they've created a perpetual spam machine that has no clear end in sight. Every day or so, sometimes multiple times a day, an exchange like this occurs:

A frustrated member of our self-created prison lashes out, demanding that we all stop texting, and then a helpful good Samaritan replies, explaining that texts sent to the group do, in fact, get sent to the entire group. Of course, the ultimate irony is that by chastening the first texter, goodie two-shoes is the prime mover of the next exchange, ad nauseam, ad infinitum.

Hats off to the spammers, this is top notch spam!

Spotify Podcasts

I was recently listening to the Invest Like the Best interview with Daniel Elk, the CEO of Spotify, and was stuck by the dissonance between the theoretical articulation of the product vision and direction and my lived experience with the product, specifically as it relates to podcasts.

While Daniel coherently walked through the Clayton Christensen "job to be done" framework (also popular with the instagram founders in an earlier episode), I was shocked about how poorly the Spotify product team seems to have adopted this lesson in their product development strategy.

Specifically, while there's an overlap between the times I'm hiring spotify to find and curate music and when I'm hiring spotify to find and curate podcasts, after that initial decision, it feels like the team retrofitted the existing spotify app and user experience without critically thinking through how users actually want to interact with a podcast-specific experience.

In particular, I was shocked to see that they tried to smash podcasts directly into the existing Spotify app rather than having a podcast-oriented application geared specifically towards podcast users. As an episodic medium with repeat consumption and curation, podcasts are just very different than traditional music. Additionally, once I've decided to listen to a podcast, the likelihood of me wanting to listen to music is extremely low - podcast-specific search, prioritization and discovery is much worse when mixed up with musical mediums (https://community.spotify.com/t5/Live-Ideas/Podcasts-Split-out-podcasts-into-separate-app/idi-p/4938692).

This is the approach Facebook has taken with Messenger, and Gmail took with Inbox; as a user, I get a tailored experience without being distracted but content unrelated to my general user-intent.

As a final aside, while I understand the complexity of the b2b partnership side, as a consumer, if spotify is trying to move into podcasts, the lack of audible / deep integration with books-on-tape feels like another way to control the user experience.