Redifning the career path for a 'World in Transition'

 

Three problems on a table

Sixteen years ago, during an internal training programme, I put three problems in front of a large group of colleagues and asked each of them to pick one.

The first was long and tedious. It needed a lot of brute-force computation, but the method was clear from the start. The second was a logic puzzle. The answer was measurable and there was obviously a method to reach it, though you had to find that method yourself. The third had no clear method and no single right answer.

Most people picked the first. A few picked the second. Only a handful picked the third.

I wrote about that experiment on my blog in 2010, in a two-part post on scaling up. My argument then was about managing uncertainty inside organisations. I still hold that argument. But the years since, and especially the last three, have convinced me that the more important story is about that handful of people who reached for Problem 3, and about what made them different.

We are trained to choose Problem 1

The choice was not surprising. Most of us spend our lives buying certainty. We accept the uncertainties we cannot control, such as health, weather and fate, with some equanimity. Where we do have some control, we work hard to remove uncertainty.

We go to college partly so that a degree can vouch for us. We join a company instead of starting one because someone else has already decided the business, the product, the technology and the process, and our job is to execute. A government career reduces uncertainty further, because in many positions the system asks for compliance with process far more than it asks for results.

There is nothing wrong with this. It is how societies function. But it means that by the time most people reach a workplace, they have spent twenty years being rewarded for choosing Problem 1. Exams have answer keys. Appraisals have rating scales. The habit of reaching for the problem with a clear method is not a personal weakness. It is what our education and our institutions have trained into us.

The machine for the many

In 2010 my conclusion was practical. If most people are uncomfortable deciding under uncertainty, then an organisation that wants to scale must reduce the uncertainty they face.

The small founding team of any venture is usually made up of restless, high-energy people who are at ease with ambiguity. They cannot run the operation at scale, and they rarely want to. Scale depends on large numbers of people who do their best work when the task is clearly defined.

So you map organisational goals down to team deliverables and then to individual deliverables. You write standard operating procedures and business rules for as many cases as you can. You build in exception reporting and escalation paths for the cases nobody anticipated. Without these, people behave like software with no error handling. They hang.

Chris Argyris made a similar point in Harvard Business Review, observing that reengineering worked best when jobs were rigorously specified rather than left for individuals to define. I also warned about the opposite failure: teams that follow the process so faithfully that they forget the result it was meant to produce.

All of that still stands. Systems, processes and SOPs carry the many through what is known. Every institution that has scaled, from a bank branch network to a national digital platform, has done it this way.

What changed: the machines took Problem 1

In 2010 I assumed that Problem 1 work would always need people. That assumption no longer holds.

Long computation with a known method is exactly what software has always done well. What is new is that AI systems now handle a growing share of Problem 2 as well. Give a capable model a well-posed puzzle, a contract to review against a checklist, a dataset to reconcile or code to write against a clear specification, and it will often find the method faster than most of us.

A well-written SOP was always a kind of program for people. Increasingly it is becoming a program for machines. The very work that organisations spent decades making predictable is the work that is easiest to automate, because it is predictable.

This is the uncomfortable part. The people who chose Problem 1 for their careers did nothing wrong, but the ground under that choice is shifting. And the scarce resource in any organisation is now the person who can face Problem 3, a situation with no method and no single right answer, and still move forward.

AI is useful there too, as a research assistant, a sparring partner and a fast drafter. But it does not decide which problem is worth solving, or own the consequences of a judgement made with incomplete information. That remains a human job.

What Problem 3 asks of a person

What carries a few people into Problem 3 is something no SOP can specify. In my experience it has three parts.

The first is critical thinking: the ability to question how the problem has been framed. Problem 3 situations rarely arrive labelled. They show up as a stalled project, a market nobody serves, or a regulation that works on paper and fails on the ground. The first act of problem solving is often to ask whether we are solving the right problem at all.

The second is the discipline of problem solving itself. Because there is no algorithm, you have to build one: break the mess into parts, test assumptions cheaply, accept partial answers and improve them. This is less glamorous than it sounds. Most of it is patience.

The third is an inventive mind, one that is willing to try an approach that has not been tried and is comfortable being wrong in public along the way. Most organisations punish visible failure far more than invisible inaction. People learn quickly that choosing Problem 1 is the safer career move.

None of these is rare in the sense of being a gift. Each can be trained and strengthened. What is rare is an environment that rewards them.

The advantage of having been elsewhere

There is a fourth quality that I have come to value more than any of these: exposure to diverse domains, and the ability to distil a lesson from one field and apply it to another.

Problem 3 in one domain is often Problem 1 in another. Someone has already solved something structurally similar. The difficulty is that they did it in a different industry, using different vocabulary, and nobody in your room has ever been there.

Taiichi Ohno built the pull system at Toyota by borrowing the logic of the American supermarket, which restocks shelves only as customers empty them. According to one history, he was applying the idea in his machine shop by 1948, years before he actually visited a US supermarket. Closer home, Dr G. Venkataswamy built Aravind Eye Hospital’s high-volume model of cataract surgery after studying how McDonald’s achieved scale. A fast-food chain taught an eye hospital how to restore sight to millions.

India’s digital public infrastructure has followed the same pattern. The idea that commerce could be opened up the way UPI opened up payments, with a shared protocol and many interoperable players instead of a few closed platforms, came from carrying a lesson across domains. It did not come from inside the industry it eventually changed.

David Epstein makes the broader case in his book Range: in complex, unpredictable fields, generalists who have sampled widely often outperform early specialists. That suits a world where AI is becoming the ultimate specialist.

The real job of the few

This is where my two arguments, sixteen years apart, meet.

It is tempting to read all this as a story of heroes and followers, with brilliant minds on one side and process-bound masses on the other. That reading is wrong, and it is dangerous.

The people who pick Problem 3 do not matter because they avoid process. They matter because they create it. Their real contribution is to turn Problem 3 into Problem 2, by finding the shape of the problem, and then into Problem 1, by writing down the method so that thousands of others can follow it without needing to be heroes.

Every SOP that works was once somebody’s Problem 3. Somebody faced the first failed transaction, the first angry customer or the first case the rules never anticipated, and worked out what to do. The SOP is the record of that thinking, written so that nobody has to repeat it.

Seen this way, the few and the many are not in competition. They are two halves of one system. An organisation with only Problem 3 people never scales. An organisation with only Problem 1 people stops working the moment the world changes, and in 2026 the world changes every quarter.

The turbocharger

Skills explain how someone solves Problem 3. They do not explain why anyone would choose it in the first place, or stay with it once it turns difficult.

Problem 3 is lonely for a long time. The early evidence is thin, the critics are confident, and every rational signal says retreat to safer ground. Many capable people with all the right skills turn back at exactly this point. The ones who keep going usually have three things that no training programme can install: commitment to the work, conviction that the direction is right, and a quiet faith that the thing is worth doing even if it fails.

I use the word quiet deliberately. The faith I mean is not the loud certainty of the pitch deck. It is closer to the steady belief that keeps someone working on a hard problem after the meeting ends and the audience has gone home.

When I wrote in 2010, I ended with a line from Bruce Barton, that nothing great has been achieved except by those who dared to believe something inside them was superior to circumstances. I would put it differently now. Skills are the engine. Commitment, conviction and that quiet faith are the turbocharger. An engine without one still runs, but it will not climb the steepest roads.

What institutions should do differently

If Problem 3 thinking is now the scarce resource, most organisations are set up to waste it. Five changes would help.

1.           Hire for range, not only for depth. A candidate who has worked across sectors, or moved between government, markets and technology, is often screened out as unfocused. In a Problem 3 world, that breadth is the asset.

2.          Give people Problem 3 work early, and protect them when it fails. Comfort with uncertainty is built through practice. If the first honest failure ends a career, nobody will try a second time.

3.          Measure people on results, not just on compliance with process. An SOP is a tool for producing an outcome. People who improvise sensibly within agreed boundaries to reach that outcome should be rewarded, not written up.

4.          Promote for judgement under uncertainty. Many organisations take their best technical expert from a low-ambiguity role and place them where ambiguity is the whole job. They often struggle, and the whole team suffers with them.

5.          Let AI handle Problem 1, and redeploy the time. The productivity gained from automation should not simply become headcount savings. Some of it should buy capacity for people to work on problems that do not yet have a method.

The same applies to our education system. As long as our exams reward the fastest route to a known answer, we will keep producing graduates who instinctively pick Problem 1, just as machines are making that choice redundant.

Which problem would you pick?

When I first wrote about the experiment, a reader asked in the comments whether I would share the three problems. I never did. Perhaps that was the right instinct, because the specific problems never mattered. The choice did.

If I ran the same exercise today, I suspect the result would look much the same. Most people would still reach for Problem 1. The difference is that in 2010 that was a safe choice. In 2026 it means competing with a machine on the machine’s own ground.

Systems will carry the many through what is known, and they should. Institutions that scale have to be built that way. But the future of those institutions, and of the people in them, will be decided by the few who reach past the clear method for the problem that has none. They will bring with them sharp thinking, lessons borrowed from other fields, and enough quiet faith to keep going.

The good news is that the few do not have to stay few. The question is whether we will build organisations and schools that make room for them.

“You can’t connect the dots looking forward; you can only connect them looking backwards. So you have to trust that the dots will somehow connect in your future.”

Steve Jobs, Stanford commencement address, 2005

This essay builds on two posts from May 2010: Scaling up: The Art of the Impossible, Part I and Part II.

Tourism Just Showed India What an Open Network Is Really For

 

"A network is open only when the smallest seller can be found by the largest buyer, without asking anyone's permission."

Picture someone planning four days in Hampi. They book the train on one app, look for a homestay on a second, and compare hotels on a third. The monument tickets come from a government portal. A local guide is found through a friend's WhatsApp forward, and the auto-rickshaw is haggled for at the station. Each step is a separate search, a separate login and a separate payment, and none of these services knows the others exist.

Now picture the woman who runs a four-room homestay outside Hampi. Travellers can find her if she pays a large commission to one of a handful of platforms, or if she sits high enough in a search algorithm she has no say over. Otherwise she depends on word of mouth. The tour guide, the coracle operator on the Tungabhadra and the family that cooks a Karnataka thali for visitors are in the same position. India's tourism economy is full of people like them, and digitally most of them barely exist.

In late September, the Ministry of Tourism and ONDC launched the National Digital Tourism Stack. It would be easy to file this under the usual pattern of an MoU, a press release and a photo opportunity. That would be a mistake. For the first time, a sector ministry has chosen the open network as the operating architecture for its whole domain, rather than treating it as one more channel to put on a slide. If the effort works, it will matter far beyond travel.

The idea behind the rails

ONDC began in retail, with support from the Ministry of Commerce. Retail was never the whole point, though. The idea was larger and simpler: any product or service that can be described in a catalogue should be listed on the network once and then be discoverable through many buyer-side interfaces. Each of those interfaces would put together the selection that suits its own users.

This turns the platform economy around. On a closed platform, the seller goes to the platform, follows its rules, pays its fees and serves its customers. On an open network, the seller publishes once and many storefronts compete to bring that seller to their users. A farmer-producer organisation, a neighbourhood kirana, a taxi driver or a homestay owner can each be found by a grocery app, a fintech app, a travel app or a vernacular app built for one district. The network handles discovery, trust and settlement. Interfaces compete on curation and service, and nobody owns the customer outright.

This is how UPI works, and it is why UPI won. No single bank or app owns the payment, and every app can reach every account. ONDC is the attempt to do for commerce what UPI did for money.

What retail taught us, uncomfortable parts included

It would be dishonest to write this piece without saying what the first years showed. Early transaction volumes on ONDC retail were driven largely by incentives and discounts. When those incentives were reduced, volumes fell. Some read this as proof that the open network idea had failed. That reading is wrong, but the lesson behind it is real and important.

Rails do not create demand. A highway with no reason to travel on it stays empty. Network effects depend on enough sellers being present that buyers find what they want, and on buyers having a reason to come back. Subsidies can prime the pump, but a market cannot be bought into existence. The parts of the network that have grown most steadily are the ones where the open model solved a problem the incumbents were not solving. Mobility is the clearest case. Driver-first ride-hailing on open rails, such as Namma Yatri in Bengaluru, grew because drivers kept more of each fare and riders got fair pricing. Farmer-producer organisations, MSMEs, and financial products such as credit and insurance have since joined, one after another, and each is gaining traction at its own pace.

The pattern is clear. ONDC as orchestrator can make onboarding, discovery and transactions possible across very different goods and services. Ecosystem participants have to drive the growth. Government's job is catalytic: nudges, early interventions and convening power, used to start the pump and then stepped back from.

Why tourism is the right next domain

Tourism may be the best fit yet for the open network model, because tourism has always been a bundle.

A trip is not a single product. It is transport, a stay, food, experiences, guides, tickets, local mobility, shopping and payments, often across several states and languages and supplied by dozens of independent providers, most of them small. Closed platforms deal with this in one of two ways. Some take a narrow slice, such as flights or hotels, and leave the traveller to assemble everything else. Others try to own the whole bundle, and that pushes them towards a small number of large, standardised suppliers that are easy to list and easy to charge.

An open network handles it differently. If the homestay, the guide, the monument ticket, the local cab and the craft workshop are each catalogued once on shared rails, any buyer-side app can assemble them into one itinerary. A pilgrimage app, a backpacker app, a luxury concierge service and a state tourism department's own app can draw on the same supply and present it in different ways. The traveller plans, discovers and pays in one place. The supplier is listed once and becomes visible everywhere.

Several of the pieces are already in place. Mobility is live on the network. UPI handles payments. Ticketing, accommodation and experiences are the categories NDTS now aims to connect. Interoperability across categories is exactly what tourism has lacked, and it is exactly what an open network is built to provide.

What the Ministry of Tourism got right

The most important decision the Ministry made was the one it chose not to make. It did not build a government tourism app.

India has a long list of well-meant government apps that were launched with fanfare and later quietly abandoned. They failed because a ministry cannot match the product quality, marketing budgets and iteration speed of private companies competing for users. A ministry can do something no private company can: convene a whole sector around a neutral, shared infrastructure that none of the participants would trust if a competitor owned it.

That is the role the Ministry has taken. It is acting as market-maker rather than market participant, building on ONDC's rails with active industry involvement. The intent is to connect the scattered players in experiences, travel, ticketing, homestays, hotels, mobility and payments, so that the traveller gets one complete offer and the supplier gets a level field.

The Ministry has also seen what many policymakers still miss. The value of DPI does not come from building rails for each sector separately. It comes from reusing rails that already exist. Tourism did not need its own protocol, its own payment system or its own registry framework. It needed to connect to what was already built and add what was specific to tourism. That is what makes the effort quick and inexpensive, and if it works, easy to copy.

What could still go wrong

Launch is not adoption, and anyone who has worked on an open network knows how wide that gap can be. Five risks need attention from the start.

Catalogue quality. A homestay with blurred photos, an unclear cancellation policy and no live availability will not be booked, however open the network is. The hard part is onboarding millions of small suppliers with catalogues good enough to compete. That needs state tourism departments, industry associations and seller-side apps doing the unglamorous work in each district.

Accountability across a bundle. If the homestay cancels, the cab does not turn up and the guide is a stranger, who does the traveller complain to? On a closed platform, one company takes the blame. On an open network, grievance redressal has to work across several independent parties. If that fails, travellers will go back to the platform they know, even if it costs more.

Incumbent incentives. Large travel platforms have built their margins on owning the customer relationship. Some will join the network as buyer apps, and that should be welcomed. Others will keep their best supply exclusive. The network grows only if joining is clearly better than staying out.

Buyer-app economics. Someone has to make money putting together and serving trips built from network supply. If the unit economics do not work without subsidy, retail's history will repeat itself.

State participation. Tourism is administered largely by the states. A national stack that state tourism departments do not actively adopt, populate and promote will cover the map unevenly.

None of these is a reason for scepticism. They are a reason to measure the right things. The test of NDTS is not how many announcements are made or how many apps sign up. It is how many travellers come back to book a second time, and what share of tourism spending reaches small suppliers who were invisible before.

A template for every ministry

This is why the tourism initiative matters well beyond tourism.

Each ministry oversees a domain made up of thousands of fragmented suppliers and millions of underserved users. Each faces the same choice the Ministry of Tourism faced: build its own portal and hope people use it, or connect its domain to rails that already exist and act as steward and catalyst.

Look at the range. In finance, credit, insurance and investment products are already moving onto open rails. Spiritual and pilgrimage travel is one of India's largest and least organised economies, covering darshan bookings, prasad, pilgrim stays and local transport. Handloom and fashion connect weavers in Varanasi and Pochampally to buyers they cannot reach today. Skills and jobs could match training providers, apprenticeships and employers in the same open way that commerce now matches buyers and sellers. Healthcare, agriculture inputs, education and logistics all have the same structure: many small suppliers, many users, and a middle layer currently dominated by a few gatekeepers. 

While some sectors have their own platorms and their own network. How do we make them all part of a larger interoperable network of transaction.

The protocol, the payment layer, the trust framework and the dispute resolution design already exist. What each domain needs is a ministry willing to do what Tourism has done: see the larger picture, bring its industry to the table, and use its catalytic power to get the first few thousand suppliers and the first few apps onto the network.

The real stakes

India built Aadhaar so that every resident could prove who they are. It built UPI so that every resident could pay and be paid. Both succeeded because they were open, interoperable and owned by no single company. The next stage is commerce itself: the right of every seller to be found and every buyer to choose, without having to go through someone else's gatekeeper.

That is what "democratising digital commerce" means in practice. The homestay owner outside Hampi does not need a bigger subsidy or another government app. She needs to list once and be visible to every traveller using every app. The Ministry of Tourism has now laid the track for her.

Whether other ministries follow will decide whether the open network stays a set of promising pilots or becomes the way India trades. The rails are already built, and the remaining question is which other ministries will put their sectors on them.

"The Web as I envisaged it, we have not seen it yet. The future is still so much bigger than the past." Tim Berners-Lee

Views are Personal 

Sources:

• NewsOnAir: Government launches project to build national DPI for tourism
• ThePrint: Digital stack initiative for tourism sector
• Digital Terminal: MoT and ONDC build open digital infrastructure
• Franchise India: MoT and ONDC launch NDTS

For BRICS, the Digital Asset Question Isn't Which Currency Wins. It's Who Writes the Rules.

 

Every debate about digital assets eventually collapses into the same framing: will central bank digital currencies win, or will private stablecoins? I want to resist that question, because it points BRICS economies at the wrong problem. The interesting issue in front of us is not which form of digital money prevails. It is whether the different forms on offer, CBDCs, stablecoins, tokenised bank deposits, can be made to work with each other across borders. That problem is not primarily technical. It is legal and institutional, and as a bloc BRICS is not yet organised to solve it.

Start by clearing up the taxonomy, because it changes what the argument has to be about. A central bank digital currency is simply digital central-bank money. A stablecoin is a privately issued digital claim whose credibility rests on the quality of its reserves, its governance and the regulation around it. A tokenised deposit is a third thing again: ordinary commercial-bank money delivered through programmable infrastructure, which lets it become atomic, machine-readable, and usable in smart-contract settlement. These are not three competitors racing toward one winning technology. They are three layers of a monetary system that could, in principle, coexist. The real question is whether they can become interoperable forms of settlement rather than three separate, closed ecosystems each optimising for itself.

The same clarity is missing from how we talk about regulation. The instinct has been to regulate the technology, blockchain rails, tokens, wallets, when the more useful instinct is to regulate the economic function underneath it. If something performs a payment or settlement function, the questions that matter are old and familiar: who bears the risk if it fails, what backs the asset, can the holder redeem at par, who holds legal title to the reserves, how are client assets protected, how does AML and KYC actually work across the transaction. Technology moves globally and almost instantly. Financial regulation remains stubbornly jurisdictional. That mismatch, not block times or transaction throughput, is the structural problem cross-border digital assets actually face.

My years building India's Open Network for Digital Commerce taught me a lesson that generalises well beyond commerce: when you build an open network, the mistake is trying to build the biggest marketplace on it. The value sits in the protocol that lets many marketplaces, and many kinds of participant, interact on common terms. Applied to digital assets, the prize is not the best national CBDC or the most widely adopted stablecoin. It is the protocol layer that lets different forms of digital money and different tokenised assets interact across jurisdictions, without every pair of counterparties negotiating a bespoke arrangement from scratch.

Here is where most of the current conversation goes astray. Putting an asset on a blockchain is the easy part, and arguably the least consequential part. The hard part is building the legal, regulatory and settlement architecture around that asset so it holds up when the asset, the issuer, the investor, the custodian and the secondary market sit in five different jurisdictions. This is not hypothetical. Picture an asset originated in India, tokenised by an issuer based in Singapore, bought by an investor in the UAE, held by a custodian in Hong Kong, settled through a US-dollar stablecoin, and eventually traded in a secondary market somewhere else again. Every one of those legs raises a question that has nothing to do with cryptography: whose law governs ownership, transfer, custody, insolvency, taxation, investor protection and settlement finality. This is why I would argue legal interoperability matters more, in the end, than technical interoperability.

That argument rests on treating governance as infrastructure in its own right, not as a compliance afterthought bolted on to the technology. It is tempting to think of infrastructure as the visible layer, the APIs, the protocols, the databases, the blockchain itself. But the layer that actually determines outcomes is governance: who is allowed to participate, who sets the standards, who can change the protocol, how disputes get resolved, how neutrality is maintained, how incentives are structured. We have spent two decades getting reasonably good at making data interoperable. The next challenge, a considerably harder one, is making rights and value interoperable. A network that is technically open but institutionally captured, or institutionally absent, will not earn the trust that financial infrastructure requires. When the infrastructure in question carries property rights and financial value rather than product listings, governance stops being a nice-to-have and becomes the whole exercise.

None of this is an argument against tokenisation. It is an argument for sequencing it correctly. Tokenisation can genuinely improve accessibility, fractional ownership, distribution, transparency and settlement efficiency. Moving settlement from T+2 to T+0 can meaningfully reduce settlement risk. But multiply tokenisation platforms without a shared governance and legal layer underneath them, and the result is platform fragmentation and liquidity fragmentation instead of the promised efficiency gains. A tokenised illiquid asset is still an illiquid asset unless there is an ecosystem of buyers, sellers and interoperable venues around it. The danger ahead is not too little tokenisation. It is tokenisation proceeding faster than the interoperability layer that would let it function as an actual market rather than a collection of walled gardens.

The stakes rise further as the technology matures, because we are not simply digitising records anymore. The trajectory runs from digitising records, to tokenising individual assets, to tokenising entire transactions, to tokenising whole economic workflows: a tokenised security, a tokenised deposit, a digital identity credential, a compliance credential and a smart contract all interacting automatically to reach atomic settlement. At that point the system looks less like crypto and more like new financial market infrastructure. Every asset that can be tokenised can also be fractionalised, which is precisely why the governance question cannot be left until later. The more the workflow automates itself, the less room there is to fix a broken legal or governance layer after the fact.

This also reframes what serious capital actually prices. Investors weigh currency risk, counterparty risk, settlement risk, custody risk, liquidity risk, regulatory risk, jurisdictional risk and technology risk together, not settlement speed in isolation. Faster settlement does not automatically produce better markets if it arrives bundled with fragmented liquidity and unclear jurisdictional recourse. What institutional capital will actually reward is legal certainty, regulated custody, clear accounting and tax treatment, and enforceable interoperability, considerably more than it will reward a marginal improvement in settlement time. Nor does tokenisation replace traditional finance. Banks, exchanges, custodians, asset managers and market infrastructure providers all remain relevant; what changes is their role, as they move from being the entire transaction to being interoperable participants within it. The winner in this transition is unlikely to be the ecosystem that eliminates intermediaries. It is more likely to be the ecosystem that makes intermediaries interoperable.

This is where BRICS has a genuine opening that the current national CBDC conversations mostly miss. The instinct, understandably, is for each country to build its own system: its own CBDC, its own regulatory sandbox, its own custody rules. That instinct is not wrong on its own terms, monetary sovereignty is worth protecting, but it is insufficient on its own, because a collection of sovereign, non-interoperable systems produces exactly the fragmented outcome tokenisation was supposed to fix. The more ambitious version of BRICS digital-asset cooperation does not ask which member state builds the best CBDC. It asks whether BRICS economies can agree on shared standards for identity and KYC recognition, common frameworks for cross-border settlement finality, and workable governance for resolving disputes when something goes wrong across borders, all while every country keeps its monetary sovereignty fully intact. National CBDCs and tokenised deposits stay domestic. Regulated private stablecoins are free to coexist. Tokenised securities move across markets because the underlying legal and compliance architecture recognises them wherever they travel. No single country, and no single private platform, controls the network.

That is digital public infrastructure thinking applied to international finance rather than to domestic commerce, and it happens to play to a strength several BRICS economies already have relative to most Western financial centres. India, in particular, has already built and operationally proven open, interoperable digital infrastructure at national scale for identity, payments and commerce. That lived experience, more than any single country's crypto regulation, is the raw material for a BRICS approach to digital assets worth taking seriously.

The honest complication is that legal interoperability is far more politically difficult to negotiate than technical interoperability. Getting five or more sovereign regulators to agree on mutual recognition of custody, insolvency treatment and settlement finality is a slower, more contested process than agreeing on a messaging standard. But that is exactly why it is the more valuable thing to get right, and exactly why the work should start now, before each country's system hardens into an incompatible standard of its own. The window in which BRICS can shape this jointly, rather than reconcile five already-built systems after the fact, is not indefinite.

So the question worth putting to BRICS policymakers is not which country will have the best CBDC or the most credible stablecoin regime. It is whether the bloc is willing to do the harder, less visible work of building interoperable governance and legal recognition across its members, before technology forces a messier reconciliation later. We have become reasonably good, as an industry and as regulators, at making data move across borders. The next test, and the one that will actually determine whether tokenisation changes finance or simply relabels it, is whether we can make rights and value move across borders just as easily, without any one country or platform having to surrender control to get there.

In this context, the idea of the Finternet gives us a useful, consumer-centric framework for how this can evolve. The concept was proposed in a 2024 paper by BIS General Manager Agustín Carstens and Nandan Nilekani, following discussions at the G20 in Bengaluru, giving it a formal endorsement from the institution that sits at the centre of global central-bank cooperation. This has been since carried further through Networks for Humanity (NFH), a global nonprofit Nandan helped build that connects people, institutions and open networks around inclusive digital public infrastructure, with the Finternet as its core mission for the future of finance. The Finternet is not primarily about creating a new kind of money. It is about creating an open, internet-like infrastructure for the movement and exchange of value, where individuals and businesses, not financial intermediaries, sit at the centre, able to transfer any financial asset, in any amount, at any time, to anyone, anywhere. Carstens and Nilekani describe it as multiple financial ecosystems connecting with each other much as the internet connects networks, built on unified ledgers that make different forms of tokenised value interoperable by design rather than by bilateral negotiation. It is, in other words, the global equivalent of the DPI logic behind the argument above, aimed one layer further out, at the end user rather than the institution.

"It would be unfortunate if we ended up with a fragmented system and legal framework in which different digital currencies don't interoperate." - Agustín Carstens, General Manager, Bank for International Settlements, "Legitimacy, Privacy, Integrity, Choice: Towards a legal framework for central bank digital currencies," BIS Innovation Hub–FSI conference, Basel, 27 September 2023

 

Heaven, Hell, or Just a Nicer Rome?

What we're really building when the machines finally clock in

Picture a career counselor cornering a hunter-gatherer forty thousand years ago. "So," she says, clipboard in hand, "what's your passion?" The hunter-gatherer looks at her the way you'd look at your smartphone if it asked about your feelings. Passion wasn't on the menu. The menu was: find food before the food finds you, or before someone faster finds it first. For most of human history, "what do you want to be when you grow up" would have gotten you a short, literal answer. Alive. Ideally.

Once we settled into cities, towns, and villages, this didn't go away, it just got a filing system. You were a farmer, a carpenter, a stone cutter, a priest, or, if the dice rolled badly, someone else's property doing someone else's menial work. And that role wasn't a job in the modern sense, the kind you quit for a better offer. It was you. It got stitched into your family name, your neighborhood, your marriage prospects, and in a great many societies, into a caste system that made sure your grandchildren inherited the same slot whether they wanted it or not. Occupation wasn't what you did between nine and five. It was the answer to "who are you," full stop, and it stayed the answer for most of your waking life, for most of your life's length, across generations.

It's worth pausing on how brutal that filing system actually was, because we like to romanticize the pre-industrial "artisan economy" as a golden age of proud craftsmen. In truth, a huge share of the necessary work (mining, hauling, tanning, the unglamorous end of agriculture) was done by people with no say in the matter at all: slaves, bonded labor, the lowest rungs of whatever caste ladder a society had built. India built one of the more elaborate versions of this, a jati system precise enough to tell a family not just what they did but whom they could marry and which well they could drink from. The stone cutter who could at least pass his trade to his son was, relatively speaking, one of the lucky ones. Most of history's necessary, backbreaking work was done by people history didn't bother naming. That's the foundation this whole conversation about "purpose" sits on, and it's worth remembering before anyone gets too dreamy about the old days when everyone supposedly knew their place.

Then there was the other one percent: the people who never had to answer the food question at all. Aristocracies, priesthoods, the occasional lucky merchant dynasty. Freed from survival labor, they had something the stone cutter never got: time. Blank, unaccounted for, nobody-asking-you-to-justify-it time. Some of them turned it into the Parthenon, into Euclid's geometry, into the Medici funding Michelangelo, into the whole intellectual apparatus we now call civilization. Philosophy, art, and science are, in no small part, hobbies of people who didn't have to farm. Not a flattering origin story. But an honest one.

And some of that same leisure class turned their freedom into an extremely long, extremely well-funded party instead. Rome kept its idle patricians happy, and its restless plebs quiet, with the same trick: bread and circuses, until the circus more or less ate the empire. Every civilization that produced a class exempt from survival work also produced, right next door, a case study in what happens when that class runs out of ideas and settles for entertaining itself into oblivion. Leisure isn't automatically virtuous. It's a coin flip, and the house doesn't always win.

Now here's the strange part, the one that's easy to read past in your outline: that ratio barely moved for centuries. Steam engines showed up, then assembly lines, then computers, and menial jobs kept getting automated away in wave after wave. Every time, someone confidently announced that mass leisure was around the corner. John Maynard Keynes predicted, in 1930, that his grandchildren would be working fifteen-hour weeks by now, filling the rest of the week with what he called "the art of life": wine, philosophy, long walks, that sort of thing. Instead we got the smartphone, the reply-all chain, and a culture that treats being permanently reachable as a virtue. Technology kept freeing up time, and humanity kept finding new work to pour into it, the way water finds a container exactly its own size. The share of people working for survival, against the sliver pursuing art or science for its own sake, barely moved. We ended up with sharper tools. Not more Athenians.

Which is what makes this AI moment different, or at least worth taking seriously as different. Unlike the steam engine, artificial intelligence isn't proposing to automate one category of task at a time. It's proposing to push the cost of food, health, education, and a great deal of physical and cognitive labor toward something close to zero, all at once. If that happens even partially, the old math breaks for the first time in recorded history. Not a wave of automation moving people sideways into new jobs, but a real shrinking of the total survival work available to justify anyone's calendar. The stone cutter's job doesn't migrate to a new task. It simply stops being necessary. Multiply that across enough professions and the aristocratic condition, time without an economic excuse, stops being a rare inheritance and starts looking like everybody's problem.

Notice I called it a problem, not a gift. Two things tend to get quietly skipped when people wax lyrical about a coming age of AI leisure.

The first is who ends up owning the machines. Every leisure class in history got its free time by sitting on top of somebody else's labor or somebody else's land. If AI-driven abundance arrives and the compute, the models, and the resulting wealth pool mainly with a handful of platforms and their shareholders, we haven't actually dissolved the aristocracy-versus-everyone-else structure. We've just automated the peasants out of the frame and kept the aristocrats. A world where a small number of people own the intelligence running everything, and everyone else gets a subsistence stipend and an ocean of unstructured time, isn't obviously heaven. It might just be Rome with better broadband. Who captures the abundance matters at least as much as whether the abundance shows up.

The second thing that gets skipped is quieter and more personal: most people, handed the aristocrat's blank calendar without the aristocrat's centuries of cultural training in what to do with it, do not spontaneously turn into philosophers. We've actually run small trials of this. Lottery winners as a group do not reliably become patrons of the arts; plenty report the windfall made their lives worse, not better. Towns that lost their defining industry overnight, a mine, a mill, didn't pivot en masse into painting and civic theatre; many are still dealing with the wreckage decades later. Involuntary retirees describe a familiar arc: relief, then a slow, disorienting loss of the structure that used to give a day its shape. None of this is destiny. But it suggests that handing someone time is not the same act as handing them purpose, and confusing the two is how a society ends up with abundant leisure alongside record loneliness and doomscrolling as a full-time hobby. We didn't even need an emperor to fund our version of the circus. We built it ourselves, for free, in our own time, which might be the more embarrassing failure of the two.

There's an older, stranger precedent worth sitting with here, and it isn't the Roman one. Around the same few centuries, roughly the middle of the first millennium BCE, agricultural surplus freed up enough people across several unconnected civilizations, Greece, India, China, Persia, that historians sometimes call it the Axial Age: the era that produced the Buddha, Confucius, the Hebrew prophets, and Greek philosophy, all more or less at once, all as byproducts of societies that suddenly had people with time to think and no immediate famine to think about. Nobody fully agrees on why it happened in parallel across unconnected civilizations, only that it did. Surplus, in other words, has produced both Rome's circuses and civilization's deepest religious and philosophical traditions, sometimes from the very same societies. Which one you get seems to depend less on the surplus itself and more on whatever cultural scaffolding happens to be standing when the surplus arrives.

Which is really the only useful question left, and it isn't a technical one. If we're heading toward an Axial Age moment rather than another Roman one, a few things seem worth putting on the table, not as a program, more as a starting list for the argument. One is ownership: whether some of the gains from AI infrastructure get structured as a shared claim, closer to a sovereign fund with dividends than a purely private windfall, so that the freed-up time comes with the freed-up means to use it. Another is education, and specifically whether we keep training children almost exclusively for employability, or whether some real portion of that effort shifts toward cultivating craft, curiosity, and aesthetic judgment as ends in themselves, the way a guru-shishya tradition or an old-fashioned apprenticeship once did, deliberately, rather than as an accident of aristocratic upbringing. A third is cultural: whether we treat the coming surplus of time as a design problem worth arguing about in public, the way we currently argue about growth or inflation, instead of assuming it will sort itself out because the technology is exciting.

None of that is a policy paper, and it isn't meant to be one. It's closer to a starting list for a dinner-table argument that most people haven't started having yet, because the technology still gets discussed as a productivity story rather than the civilizational one it actually is. Every generation handed unearned time, the Athenian aristocrat, the Renaissance patron, the involuntary retiree, the lottery winner, has already run this experiment for us, in miniature, with famously mixed results. AI is about to run it again, at the scale of the entire species, all at once, with no dress rehearsal and no one else to blame if it goes wrong. Rome at least had an emperor to blame for the circuses. We will only have ourselves, and a scroll bar.

The machines, for the first time in history, are plausibly willing to foot the bill for the shift. What they cannot do is answer the question one economist saw coming a century early, and named more precisely than anyone has managed since.

"Thus for the first time since his creation man will be faced with his real, his permanent problem: how to use his freedom from pressing economic cares, how to occupy the leisure which science and compound interest will have won for him, to live wisely and agreeably and well."

— John Maynard Keynes, Economic Possibilities for our Grandchildren, 1930 

Empires of Intelligence: What the Colonial Past Can Teach Us About the AI Future

 

The tech superiority in shipping and gunpowder warfare, combined with the appetite for distant resources and captive markets, aided Spain and Portugal in colonizing the Americas from the 1490s. The British and Dutch East India Companies were chartered around 1600, extending this colonization.

Then came the Industrial Revolution in 18^th century: a handful of brilliant machines, the steam engine, the spinning jenny, the power loom, burst out of Britain in the second half of the eighteenth century, raced across Western Europe and the Atlantic, and remade human productivity forever.

What the Industrial Revolution did was not invent extraction — it industrialized it. Railways now hauled raw cotton out of colonized land and finished cloth back in, at a speed and scale no sailing ship could match. The telegraph let a handful of colonial offices in London coordinate an empire on which the sun never set. Steamships and the Maxim gun turned a slow, centuries-long process of colonization into the frantic, decades-long "Scramble for Africa." The machines did not create the logic of empire. They gave an existing logic ‘industrial teeth’.

That distinction matters, because it is also the more useful lens for thinking about artificial intelligence today. The worry is not that AI will cause a new colonization from nothing. It is that AI is industrializing a concentration of power that is already visible, already forming — and that, exactly as happened two centuries ago, the architecture being built right now will determine who spends the next century as a builder of intelligence and who spends it as a tenant.

The New Raw Material, the New Merchant Fleet

Colonial extraction needed three things: a resource worth taking, a fleet capable of moving it, and a captive market for what came back. Frontier artificial intelligence has its own version of all three. The resource is data - the accumulated digital exhaust of human behavior, conversation, and transaction. The fleet is compute: the small number of companies and countries that control advanced semiconductor fabrication, the data centers, and the energy to run them. And the captive market is everyone else, every individual, enterprise, and government that will consume intelligence as an API call rather than build it as sovereign infrastructure.

Look at where frontier foundation models actually get built today. A handful of firms in the United States and China account for nearly all of the models capable of general reasoning at scale. Building one requires not just world-class research talent but hundreds of millions to billions of dollars in compute, energy contracts that rival those of small nations, and access to a semiconductor supply chain concentrated in a handful of firms and geographies. Export controls on advanced chips are, in effect, the modern equivalent of a colonial power controlling who gets gunpowder. None of this is a moral accusation against the companies involved — they are responding rationally to the economics of the technology. But the structural resemblance to the old pattern of resource, fleet, and market is difficult to unsee once you look for it.

The Questions Policymakers Cannot Defer

This raises three questions that deserve to be asked plainly, even though the honest answers are uncomfortable.

How many countries, realistically, will ever have the capital, energy infrastructure, chip access, and research talent needed to build and maintain a frontier foundation model? The number today is small (probably fewer than half a dozen) and the barriers to entry are rising, not falling, as the frontier moves toward ever larger training runs.

What happens to the nations and enterprises that cannot cross that threshold? Do they simply rent intelligence indefinitely, the way a colonized economy once exported raw cotton and imported finished cloth at a price set elsewhere? Renting is not inherently ruinous; nations rent all kinds of capability today, from cloud infrastructure to vaccine manufacturing, without becoming colonies. But renting the layer that increasingly mediates commerce, education, healthcare, and governance is a different order of dependency, because it is not a discrete purchase; it is a permanent tax on every future transaction, and the terms of that tax are set entirely by the renter.

And is "intellectual colonization" too strong a phrase for this, or is it precisely the right one? Colonization was not merely economic dependency, it involved the erasure of local systems of knowledge and their replacement with the colonizer's categories, language, and worldview. A world in which every culture's laws, medicine, and commerce are mediated through a handful of models trained overwhelmingly on the historical and linguistic corpus of a few countries risks something structurally similar: not a flag planted in the ground, but a worldview quietly planted in the model weights that every other nation's citizens interact with daily.

These are not rhetorical questions asked for effect. They are the kind of question that, if left unanswered for another decade, answers itself by default,  in favor of whoever already holds the compute.

Two Ways to Diffuse the Power

If concentration is the risk, diffusion is the countermeasure, and there are two distinct architectural choices policymakers and technologists can push toward, both of which already have working precedents.

The first is pushing intelligence to the edge instead of pooling it at the center. Today's default architecture treats a handful of giant, universally trained models, updated continuously from everyone's interactions, as the intended destination for every query, every business process, every personal decision: an omniscient friend, philosopher, and guide for each individual and enterprise, with the full transcript of the relationship flowing back to a central server. An alternative already exists in embryonic form. Federated learning, a technique in which a model on a device learns from local data and sends back only aggregated, anonymized updates rather than the raw data itself  has been used for years in consumer products like predictive keyboards, precisely because it lets a system improve without every keystroke leaving the phone. Extend that logic further: a model that lives on a phone, a home router, or an enterprise gateway can handle the great majority of everyday reasoning locally, drawing on a general model only when it genuinely needs broader context, and sharing back to any central knowledge base only what the user or enterprise explicitly consents to share. The transaction stays local by default; participation in the global commons becomes an opt-in choice rather than an automatic surrender.

The second is resisting the pull toward one all-purpose model and instead building highly specialized models for individual domains like  health, education, law, financial services,  each trained deeply enough in its own field to outperform a generalist model at the tasks that actually matter to citizens in that domain, and each able to keep improving through use within that domain rather than through indiscriminate absorption into a universal corpus. The risk of leaving specialization there is that it simply recreates ten small walled gardens instead of one large one. The answer is open standards and protocols that let these specialized models interact with each other on a consent basis, a health model calling a financial-inclusion model to check affordability, an education model calling a language model to translate content into a local dialect,  without any of them needing to defer to, or route through, a single dominant global model to reason well.

This is not a hypothetical. India's own experience with open digital protocols is a working demonstration of the underlying principle, even though it was built for commerce rather than AI. Before the Open Network for Digital Commerce, digital commerce in India was consolidating toward the same pattern seen almost everywhere else: a small number of platforms that owned both the buyer relationship and the seller relationship, with every transaction and every unit of pricing power flowing through their walled infrastructure. ONDC instead created an open, interoperable protocol, built on the Beckn protocol, that let any compliant buyer app discover and transact with any compliant seller app, with no single platform sitting in the middle extracting rent from every exchange. The lesson generalizes directly to AI: an open protocol layer for model-to-model interaction could do for intelligence what an open commerce protocol did for retail - letting specialized, smaller players interoperate on equal footing instead of every interaction defaulting to whichever platform happens to be largest.

Both of these architectural choices carry a second, more practical benefit that should appeal to any finance ministry worried about the cost of the AI transition: they are cheaper. Routing the bulk of everyday reasoning through small, local, or narrowly specialized models rather than a giant universal model every single time reduces the number of tokens processed, the compute cycles consumed, and the electricity drawn from the grid. Diffusion is not only a safeguard against concentration of power; it is very plausibly the more economically sustainable path as AI usage scales into billions of daily interactions.

Why Digital Public Infrastructure Is the Precondition, Not an Afterthought

Neither of these architectural interventions works unless the underlying data exists in a usable, trustworthy, and interoperable form in the first place. A local model on a phone in a country where identity, land records, health records, and financial transactions are still paper-based, fragmented, or locked inside proprietary corporate databases has nothing meaningful to reason over. This is precisely the argument for treating digital public infrastructure — the interoperable, open "rails" for identity, payments, and data exchange that countries like India have built through systems such as Aadhaar and the Unified Payments Interface — not as a separate policy agenda from AI, but as its precondition.

The alternative to open, interoperable rails is not the absence of digitization; digitization is happening everywhere regardless. The alternative is digitization captured inside walled gardens controlled by a handful of private platforms, each sitting on a pool of data large enough to train a proprietary model, each with every incentive to prevent that data from ever becoming interoperable with a competitor's, and each able to charge rent on that data's use indefinitely. Digital public infrastructure, built on open standards with consent-based data sharing at its core, is what allows every country, not just the handful that can afford frontier compute,  to accumulate a well-structured, contextually rich pool of its own data, and to let smaller, local, or open-source models be trained meaningfully on that data instead of being permanently dependent on a foreign model's second-hand understanding of local context.

This is also, not incidentally, the strongest antidote to rent-seeking. A market with open, interoperable rails and many interoperating specialized models is a market with real competition, which pushes the cost of intelligence down for everyone. A market of walled gardens converging on two or three global models is a market that, however impressive the technology, will behave like a monopoly,  because eventually, it will be one.

The Choice Is Being Made Now

The colonizing nations of the eighteenth and nineteenth centuries did not sit down and vote on empire; the choice was made, cumulatively, by which ships got built, which trading companies got charters, and which technologies got industrialized first, long before most of the affected societies had any say in the matter. By the time the consequences were fully visible, the architecture was already locked in, and undoing it took centuries.

The architecture of artificial intelligence is being decided now, in this decade, in choices that look small: which protocols become standards, whether edge inference is subsidized or taxed, whether digital public infrastructure is built as an "open commons" or licensed out to whichever platform arrives first with capital. None of these choices individually looks like a decision about empire. Collectively, they are exactly that. The lesson of the last industrial revolution is not that the machines were the problem, it is that by the time everyone understood what the machines had made possible, the terms had already been set by whoever built them first.

Policymakers have a narrower window than they think to make sure that this time, the terms are set by more than a handful of hands.

 “Every empire, however, tells itself and the world that it is unlike all other empires, that its mission is not to plunder and control but to educate and liberate." -Columbia University professor Edward Said.

When AI Climbs the Quadrants of Success

 

What happens to careers, and to education, when machines start solving the problems we used to climb through

For decades, a professional career followed a script so familiar we rarely questioned it. Score well in school. Earn a professional degree that hands you a domain-its concepts, its standard procedures, its tricks, tips and tools. Land a job in an established institution or in government. Then climb, level by level, into steadily more complex problems, until either the ladder ends or you do.

Strip away the detail and every job at every level reduces to the same thing: solving problems, for yourself or for others. What changes as you rise is not the act but the kind of problem you are handed.

A map I drew fifteen years ago

In 2010 I sketched this progression as four quadrants of uncertainty, borrowing a frame from R. Gopalakrishnan and stretching it to fit a career. The two axes were simple: how well you can identify the problem, and how well you can find the solution. (The original post is here.)

In Quadrant 1, the problem is known and so is the solution. Success needs a good repository of knowledge and the discipline to apply it, you know the tune and you know the steps. In Quadrant 2, the solution is known but the problem is not; you earn your keep through analysis and root-cause deduction, figuring out what the crowd wants and then performing it. Quadrant 3 is where the known solutions run out: the problem is clear, but the answer must be invented, which demands curiosity, persistence, and the maturity to abandon an idea you have grown fond of. Quadrant 4 is the leader's terrain, problem undefined, solution unforged, only a vision of a place no one has been and the will to get others to build toward it.

Most careers walk this map slowly, from corner to corner. A few leap quadrants. A rare few begin in the hardest one. But the shape held for a generation, and organisations were built around it, entry rungs where the young learned their craft, senior rungs where judgment lived. The whole edifice assumed a pipeline: you paid your dues in Quadrant 1, and the paying itself was the education. The trainee who reconciled ledgers for three years was not just producing reconciliations; she was, without anyone naming it, absorbing the pattern-sense that would later let her spot the anomaly no procedure flagged. The value flowed in two directions, output for the firm, tacit judgment for the person, and we mostly noticed only the first.

It is worth being precise about what each quadrant rewards, because AI does not treat them equally. Quadrant 1 rewards fidelity: doing the known thing correctly and consistently. Quadrant 2 rewards diagnosis: seeing through symptoms to the actual fault. Quadrant 3 rewards invention under constraint: producing a solution that did not exist, to a problem you can at least name. Quadrant 4 rewards imagination under ambiguity: deciding what is even worth attempting when neither problem nor answer is given. Fidelity is the most codifiable of these, and codifiable is exactly what machines eat first.

Then AI walked onto the map

Every wave of technology made us better inside each quadrant. Better tools, better analysis, better reach. AI is different in kind, not degree: it does not just sharpen the work within a quadrant, it absorbs the quadrant.

Quadrant 1 is already substantially automatable. The junior coder writing from a known pattern, the first-line support agent following a script, the trainee doctor working through a differential, the junior lawyer drafting from a template; this is precisely the known-problem, known-solution work that current systems handle well. Quadrant 2, the diagnostic labour of finding the real problem before applying a known fix, is following fast; increasingly the machine does the root-cause pass and hands a human the shortlist.

Quadrants 3 and 4 will hold out longer. Genuine invention and genuine vision still sit beyond what these systems originate rather than remix. But "longer" is not "forever," and even here the honest word is augment: the innovator and the visionary will work with AI as a collaborator that widens what one mind can attempt.

Two stories, and the one that actually matters

You can tell this two ways. The grim version: AI is eroding the opportunities of a vast section of job-seekers, dissolving exactly the lower rungs where a generation once learned its trade. The expansive version: it multiplies what we can attempt, new problems become solvable, service quality rises, new forms of entertainment and enterprise appear, and human–AI collaboration opens frontiers we could not reach alone. Perhaps, eventually, even new geographies, new planets, a genuine abundance.

Both may turn out to be partly true, and we will argue about the balance for years. History gives ammunition to both camps: earlier automation destroyed categories of work while creating others we could not have named in advance, and the net was, over long horizons, more prosperity. But that reassurance carries an asterisk that is easy to skip past. The transitions were brutal for the people caught in them, they unfolded over generations rather than years, and the new work usually demanded capabilities the displaced did not have and could not quickly acquire. "It worked out eventually" is cold comfort to a cohort whose working life is the transition.

What is genuinely new this time is the direction of travel. Past machines climbed from the bottom, they took the physical, the repetitive, the manual, and human advantage retreated upward into cognition. AI climbs from a different face of the mountain. It is strongest precisely at the codified cognitive work we treated as the entry point to professional life. The rungs being removed are not only the lowest; they are the ones we used as the on-ramp to everything above.

But fixating on the optimist-versus-pessimist argument is a distraction, because it treats the outcome as weather, something that happens to us while we hold an umbrella. The decisive variable is not whether AI expands or erodes opportunity in the aggregate. It is whether we rebuild the one institution that decides which future the next generation is actually equipped for: education.

Here is the uncomfortable mechanism. For a century, education has been optimised to manufacture competent Quadrant-1 workers, people who can absorb a domain and execute known solutions reliably. The examination, the syllabus, the graded problem set, the very architecture of a degree, all reward the reliable reproduction of known answers. That was rational when the economy needed millions of such people and when Quadrant-1 competence was also the on-ramp to everything above it. AI breaks both halves of that logic at once. The economy needs far fewer Quadrant-1 executors, and the traditional path, learn the craft by grinding through its lowest tasks for a decade, no longer works when those tasks are the first to be automated. You cannot apprentice into judgment by doing the drudgery when the drudgery is gone.

This is the trap, stated plainly: the same system that is least useful for producing the capabilities AI cannot replicate is the system we are still running at full capacity. We are optimising harder for the quadrant that is vanishing, because that is the quadrant our institutions know how to measure, fund, and rank. The measurable is crowding out the valuable, and the gap widens with every model release.

What reinventing education would actually mean

If that diagnosis is right, tinkering will not save us. "Add a coding class, add an AI module" leaves the Quadrant-1 factory intact. The harder pointers, offered as arguments rather than certainties:

–    Stop teaching for recall; teach for judgment. When the machine holds the facts and executes the procedures, the human value is knowing which problem to solve, when the machine is wrong, and what to do when the situation falls outside the training data. Curriculum built around memorising and reproducing is training for the quadrant that is disappearing fastest.

–    Move Quadrant 3–4 skills forward by decades. We currently defer invention, ambiguity, and vision to the senior rungs, things you are "ready for" at forty. If AI collapses the lower rungs, an eighteen-year-old must start much closer to the top. That means teaching students to sit with ill-defined problems, run experiments, and tolerate the frustration of not knowing, early and deliberately, rather than as a reward for surviving the grind.

–    Rebuild the apprenticeship model without the drudgery. The old system had a hidden virtue: repetitive junior work quietly built pattern-recognition and tacit judgment. Remove the work and you remove the schooling. Someone has to design the replacement, simulated hard cases, AI as a sparring partner that poses problems rather than solves them, deliberate exposure to failure, so the young still develop instincts they can no longer absorb by osmosis.

–    Teach students to direct AI, not compete with it. The durable skill is orchestration: framing a problem well, interrogating an AI's output, catching its confident errors, and combining several tools toward an end. This is closer to managing a talented, unreliable team than to using a calculator, and almost no curriculum teaches it.

–    Make learning genuinely continuous, and mean it. "Lifelong learning" has been a slogan for thirty years while the actual structure, front-load education, then work, stayed fixed. If the ground shifts every few years, the institutional model of a one-time degree followed by a career is itself obsolete. Re-entry into education has to become normal, cheap, and expected, not a mid-life exception.

–    Credential the things machines can't yet certify. As AI can pass most knowledge tests, the signalling value of exams that measure recall collapses. What still needs a human verdict, originality, judgment under ambiguity, the quality of a question rather than an answer, is exactly what our assessment systems are worst at measuring. That gap is where the reinvention has to happen.

None of these is a finished blueprint, and reasonable people will contest several. The point is the direction: an education system that keeps producing Quadrant-1 graduates for Quadrant-1 jobs that no longer exist is not protecting the young, it is walking them off a cliff with a certificate in hand.

The real question

So the future of work is not, at heart, a question about AI. AI is the forcing function. The real question is whether our schools, universities and training systems can stop preparing people to begin at the bottom of a ladder whose bottom rungs have been sawn off, and start teaching the next generation to begin where the machines, for now, still cannot follow.

We will figure out the world of abundanc
e or scarcity as it comes. What we decide now is whether the coming generation is built to shape that world or to be displaced by it. That decision is being made, mostly by inertia, in classrooms today.

 

“Disruption becomes transformative when it does not merely change what we do, but changes the ground on which we decide what is possible.”