The Heath Ledger guide to AI
The Dark Knight is really a movie about the Joker dressed as a Batman movie
Most Batman movies are about Batman.
The Dark Knight makes a greater impression as a movie about the Joker.
Christian Bale is the film’s lead, but the public and critical attention was entirely captured by Heath Ledger’s Joker, who dominates the cultural conversation despite having less screen time.
The Dark Knight is really a movie about the Joker dressed as a Batman movie.
The Joker drives the plot, escalates every central turning point, and forces all the other characters to react. The film’s most profound questions, of chaos versus order, moral compromise, and the fragility of social contracts, are posed by the Joker.
Unlike traditional superhero films, this movie doesn’t build toward the hero’s triumph, but rather toward the moral cost of survival in a world shaped by the Joker.
Systems, not AI
Over the past several months, I’ve been writing Reshuffle — a book that appears to be about AI but is actually about systems and how they evolve when a new technology is introduced.
That’s the untold story that isn’t understood enough.
Our obsession with AI blinds us to understanding how systems change in response to new technology.
Accordingly, some of the technologies I use to explain these effects are also the most ‘unintellient’ technologies - the shipping container, the barcode, the paper map - all of which transformed and restructured our economic systems.
The economic impact of AI is determined less by the benchmarks the technology meets or the complexity of the tasks it performs and more by how it transforms the systems in which it operates.
Reshuffle is really a book about systems, dressed as a book about AI.
AI’s impact is often framed in narrow terms - what work it replaces, what jobs it threatens, and how it boosts productivity.
However, the real impact of AI comes not from how it performs a task,
but from how it restructures the entire system around that task.
Reshuffle launches on July 20
I’m thrilled to announce that Reshuffle launches this Sunday, July 20th.
If you haven’t pre-ordered your copy yet, now’s a great time; it’s still available at a 70% discount.
The Kindle edition launches on July 20.
The paperback, hardcover, and audiobook versions launch on July 30.
Better questions - and a better frame
Both the hype and skepticism surrounding AI often stem from the same flawed approach: judging the technology by how well it performs a specific task, rather than by how it transforms the broader economic system that it enters.
This narrow focus keeps us chasing performance benchmarks, which are typically easy to measure but often miss the point.
To understand how AI might truly transform the economy, we need to ask better questions.
How does the economic system change when AI is introduced?
Which constraints on the system are removed, and which new ones emerge?
Where do new risks show up, and who manages them?
To shift our view from AI’s impact on tasks to its impact on the system, we need to look at the less-understood impact of AI - the other half of the story.
Missing the better half of the AI story
“AI” is a catch-all term that often obscures more than it clarifies. It covers everything from a recommendation system tweaking your Netflix feed to a model generating legal contracts or driving a car.
The many technologies that fall under the AI umbrella, including machine learning, deep learning, generative models, neural networks, and others, have distinct definitions, strengths, and limitations.
Lumping them together under a single term might seem counterproductive, as it obscures the technical nuance associated with each term.
Yet, to shift our lens from the technology to understanding how the technology transforms the economic systems - the institutions, workflows, and value chains - into which it is deployed, lumping these technologies under the umbrella term ‘AI’ has an often-overlooked benefit: despite their technical differences, their underlying economics are remarkably similar.
The shared economic logic of these technologies rests on two key principles:
(1) Their ability to dramatically increase the capacity for performing work, and
(2) Their ability to simplify the coordination of such work towards greater, better, and more novel output.
Whether it’s an algorithm forecasting demand or a model summarizing legal contracts, the economic impact stems from the same two forces - amplification of the capacity for cognitive labor and decision-making, and reduction in coordination friction between actors and their activities.
A lot is written and said about the former. Very little is discussed about the latter. But AI’s transformative impact on systems plays out primarily through the latter.
Two factors contribute to how AI enhances both the capacity and coordination required for knowledge work.
First, AI reduces the cost of task execution. Tasks that once required expensive experts can now be performed more efficiently, at a lower price, and at scale.
Equally important, but far less discussed, is AI’s potential to lower coordination costs: the hidden friction that arises when people and resources need to be aligned to get something done.
In most knowledge work today, that alignment is a struggle. Information lives in a dozen places at once, across emails, chat threads, spreadsheets, and specialized tools. That fragmentation keeps work from getting done, as the people or teams involved must constantly align their activities to ensure effective collaboration.
This problem compounds as more people and more organizations get involved. When work spans departments or crosses company boundaries, the costs of coordination multiply, as we spend increasing amounts of time making sure everyone is navigating with the same map.
AI can help bridge this gap by making sense of the unstructured information each player holds, building a shared model across them, and delivering the right insights to the right people at the right time. In doing so, it reduces the need for slow, manual coordination and helps teams and even companies move faster, with greater alignment.
We often overestimate AI’s potential for automation, applying it to tasks where it performs poorly and fueling cycles of hype and disillusionment.
At the same time, we consistently underestimate its potential for coordination.
With this framing error, we chase narrow performance benchmarks and, in the process, overlook AI’s true economic potential in restructuring how people, teams, and companies coordinate to create value. We fixate on individual tasks and job substitution, rather than asking which coordination failures AI can resolve and what new forms of economic activity get unlocked once those coordination frictions are eliminated.
The real story is coordination
Stories of coordination aren’t always obvious. Sometimes, the most ‘unintelligent’ technologies can transform entire economic systems because they unlock coordination.
Consider the shipping container.
It’s tempting to see containerization as the triumph of superior hardware and efficient automation: sturdy crates as opposed to breakbulk cargo, faster unloading through cranes, larger ships moving between larger ports, and sturdier boxes that could be uniformly stacked. If the story of containerization were just about speed and automation, then the revolution would have ended right there at the port. But it didn’t.
The first breakthrough was the single, integrated contract that came with the container. A unified contract enabled coordination across different modes of transportation - road, rail, sea. Coordination, though, wasn’t just about contracts. Containers also needed to move physically across ships, trucks, and trains without ever being unpacked. That required standard sizes, and standardization didn’t come easily.
The adoption of standardized container sizes and unified shipping contracts changed the nature of shipping.
Shipping became predictable. Transit times, once unpredictable, became reliable and calculable.
This was the triumph of coordination. Now that businesses could rely on shipping schedules, they stopped stockpiling inventory. Just-in-time manufacturing, where parts arrive exactly when needed, became the dominant model. Supply chains stretched across continents, linking factories in one country to assembly lines in another and customers in a third. With that, investment poured in. Ports, railroads, and trucking fleets all had to modernize to plug into this new system of trade.
The real impact of the container, however, showed up elsewhere - giving us companies like Intel, and today, Nvidia. As freight became faster, cheaper, and more reliable, that reliability broke the logic of vertical integration. Firms could specialize and outsource. And as companies specialized, components improved. Improving components led to greater product innovation through recombination. Component-level innovation and competition were made possible because the container, alongside improvements in information technology, enabled such unbundling. Entire industrial structures were transformed.
The real winners emerge as coordination hubs
Before standardized containers, a port’s value was determined largely by its efficiency and speed. After containerization, a port's value increasingly came from its role in coordinating global trade flows. Singapore's leadership grasped this economic shift well ahead of its competitors. While neighboring ports focused on automating cargo movement, Singapore invested in ensuring reliability across the entire logistics network.
Singapore integrated its customs and clearance process to eliminate other bottlenecks that were slowing down the movement of goods. Unlike other ports, which positioned themselves as an endpoint in the shipping journey, Singapore built out an integrated transport network across road, rail, and air to create a unified transit hub. The country positioned itself as a neutral, well-governed trade hub, building legal and diplomatic frameworks to attract major shipping alliances and multinational firms.
More than anything, Singapore made reliability the product it sold to the world.
Shippers knew that cargo passing through the port would move on schedule, with minimal disruption. Singapore knew that in an age of containerized coordination, reliability, not speed, would determine power.
Coordination involves creating systems where all parts move together reliably.
Singapore placed a bet on the coordination that was about to unfold across global trade and positioned itself accordingly. Automation helped Singapore's port load and unload goods faster, which is useful, but not transformative. Coordination, on the other hand, made it indispensable in the new system of global trade.
This explains why Singapore, a tiny island with no natural resources, developed so rapidly after adopting containerization. It was Singapore’s positioning as a coordination hub, rather than merely a modernized port, that transformed it from a regional shipping stop into a global economic powerhouse.
Singapore modernized its docks, but its success stemmed from recognizing the need for a new coordination logic for global trade and restructuring itself accordingly. Firms chasing automation may unlock short-term gains, but those using AI to orchestrate complex systems will unlock entirely new forms of value, creating competitive advantage.
The box didn’t change the world on its own, and neither did port automation. It was necessary, but it remains a small footnote in the history of global trade. The real revolution was the coordination that the box forced upon the world.
That’s the real opportunity that AI offers today. Most companies, though, are still busy upgrading cranes at their ports and firing the dockworkers.
This is the story that Reshuffle talks about.
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I ordered the book. I'm sure it'll be a great read!
Have just pre-ordered Reshuffle. A nice coincidence that it is being published on my birthday! 🎁