Intro
This is Part 4 of a multi-part series on what happens to human purpose when machines can do the work. Part 1 built the framework, roles reduce to judgment and downside risk. Part 2 dealt with the human cost : a glut of execution, a taste gap, and a crisis of purpose. Part 3 proposed mechanisms : energy as the new denominator, producer-side accountability, and the Blind Arena.
All of it ended on the same predicate: energy abundance. But energy isn’t evenly distributed, and neither is the ability to turn it into intelligence. Which means the question of who builds the models, who runs them, and who depends on someone else’s : is not a technical question. It’s a geopolitical one.
This post is about what that looks like for India. But the logic applies to any country that consumes more intelligence than it produces.
India
I’ve titled this India but, it applies to any country.
If we reduce this down to its fundamentals, Energy and its use become what we optimize.
What % of energy goes towards tokens and what goes towards anything else.
This includes but is not limited to:
- Travel
- Food production
- Commerce
- Logistics
We also need energy for general economic output.
It’s how we acquire resources like gold, metals, minerals, oil,, it’s how we build stuff, it’s how we power our laptops and phones.
The optimization that needs to be done is a function of all of this, historically, this is not something that can be done centrally, maybe possible now as a function of goal setting.
Countries do set their prioritize and optimize to some extent on it.
The paradigm shift that’s happening is one where we move from human driven economic output to token driven economic output.
Anything we consider as normal today comes at a cost of that being used towards tokens.
The obvious answer is to increase energy production significantly, but Jevon’s paradox will still apply unless we have infinite energy.
- Energy optimized optimal token generation becomes the wheel that drives economic production
- Pseudo formal proofs becomes the cost of reducing hallucination risk
- Human risk taking and judgement remain the means of reducing downside risk and partaking in as much upside returns as possible
Each country and its citizenry will soon have to deal with how they want balance this.
Additionally, for a country like India, this presents a unique dilemma.
If you do not have the energy sovereignty to train and run your own models, you become a Net Importer of Intelligence.
This is not like importing oil or steel or people.
When you import intelligence, you import the values, the biases, and the judgment of the entity that created it.
If the “Intelligent Purpose” of a foreign model is aligned with a foreign culture or a foreign corporation’s bottom line, using that model for your nation’s healthcare, law, or education may be a subtle form of colonization.
The colonization analogy is imperfect but directionally right. Historical colonization extracted physical resources. Intelligence importation is different it doesn’t take anything out, it puts something in.
It shapes how decisions get framed, what options get surfaced, what gets optimized for. A model trained primarily on American medical literature will not weigh the same factors an Indian physician would. A model aligned to Silicon Valley’s norms around individual autonomy will frame policy questions differently than one grounded in a more collective social contract.
To extend this, If the future economy is built on a Verification Layer through a protocol, and is one where humans provide the judgment, the taste, and the downside-risk absorption, then India’s demographic dividend takes on a new meaning.
To flip this, if a country becomes a Net Exporter of Intelligence, they are taking on the challenge of having an energy surplus on the rest of the world, they would have to start hoarding it in advance in order to own that advantage.
I don’t know how this game theory will play out, but, I think we’re already seeing some of it happen right now.
OPEC for Intelligence Three or four countries: the US, China, and maybe one or two others with sufficient energy surplus and model capability become net exporters of intelligence. Everyone else subscribes. The dynamics mirror oil dependency: you get reliable supply in exchange for strategic vulnerability. It also mirrors the defence dependency, several countries submit to military hegemony in exchange of defence and protection in times of need.
The energy bind: Training a frontier model today costs hundreds of millions of dollars, most of it in energy and compute. India’s per-capita energy consumption is roughly a third of the global average. The country is simultaneously trying to electrify 1.4 billion lives, industrialize further, and build sovereign AI capability. These goals compete for the same kilowatt-hours. Without a massive and deliberate expansion of energy production the math doesn’t work. You end up choosing between economic development and intelligence sovereignty.
The demographic flip: India’s demographic dividend has traditionally meant cheap, abundant, skilled labor. In the automated economy, this framing inverts. A large young population isn’t valuable because they can write code cheaper than Americans. It’s valuable because they represent the largest potential pool of human judgment.
People who can participate in verification, taste-making, and downside-risk absorption at scale. But only if they develop the judgment in the first place. Which brings us back to the Blind Arena: India doesn’t just need to build models, it needs to build the feedback systems that let its population develop taste in an era where the labor that used to teach taste is disappearing.
None of these are predictions. But all of these are plausible, and they share one thing in common: the countries that invested in energy sovereignty before they needed it are the ones with options. Everyone else is choosing between bad alternatives.
Scaling It Down
Everything above has been about countries. But the reader of this post is not a country. You’re a person, trying to figure out what this means for you.
The same logic scales down. If you depend entirely on an LLM for the work you do, you become a net importer of intelligence at the individual level. The model frames your problems, surfaces your options, and drafts your thinking. You review and approve. Over time, the judgment you’re supposed to be providing starts to thin, not because you lost it, but because you stopped building it.
This is the individual taste gap from Part 2, but viewed through the sovereignty lens. When we talked about it before, the concern was “how do you develop judgment without doing the labor?” Here the concern is slightly different: “what happens to your judgment when you outsource the thinking to a system whose defaults aren’t yours?”
Every knowledge worker is already making this choice daily. Which parts do I let the model do? Which parts do I insist on doing myself? Most of us don’t frame it as a sovereignty decision, but that’s what it is. Every task you hand over entirely is a muscle that stops developing. Every task you keep is an investment in your own judgment, at the cost of efficiency. Finding this balance for yourself is your own energy optimization framework.
This scales down to the unit of a person, up to a company, up to a country, and eventually, the world.
The Blind Arena was a mechanism for society. But there’s a personal version of it too: deliberately competing with the model, doing the work yourself even when the model could do it faster, and measuring yourself against its output. Not because you’ll always win. But because the losing is where the judgment comes from.
This is Frankl’s third pillar again. The attitude we take toward the struggle. The choice to do the hard thing not because it’s efficient, but because it’s how you stay capable of choosing at all.
Coda
We are here to decide what is worth the energy.
We are here to sign our names to the risk.
And ultimately, we are here to provide the one thing the machine cannot simulate: the capacity to care about the outcome.
In an energy-abundant world:
- execution becomes abundant and trivial
- intelligence becomes cheap
- verification becomes part-cryptographic
- judgment becomes scarce
- experience to develop good judgement becomes expensive
The predicate is to make energy as abundant as possible.
The economy will not reward those who can think. It will reward those who can decide under uncertainty and own the consequences. To learn how to make good decisions we need to do things, we need to develop systems and protocols that allow that to happen without sacrificing economic output.
These are my overall thoughts on the near future or the near present.
Over the course of this series, I’ve made a lot of claims and left a lot of threads loose. The remaining posts will try to pull on them.
The most obvious one: I keep saying “energy abundance” like it’s a switch that gets flipped. It isn’t. There’s a real question of how we get there, how long it takes, and whether the energy return on the energy we invest in building new capacity is actually favorable. Vaclav Smil has spent a career showing that energy transitions are slower and messier than technologists want to believe. That deserves honest engagement, not hand-waving.
Then there’s the verification problem. The Blind Arena and producer-side accountability are sketches, not blueprints. If we’re serious about building systems where machines stake something to back their output, the mechanism design matters. How do you structure incentives so that participants, both human and machine, don’t just optimize for passing the verification layer rather than producing quality? Goodhart’s Law is sitting right there waiting to break any system we design. ZKPs are one possible implementation, but they have real limits, and there are alternatives worth exploring.
There’s also something I haven’t addressed yet that I think matters: what happens when the artifacts that LLMs produce start becoming products? Right now, most LLM output is intermediate, a draft, a prototype, a starting point that a human refines.
But as the quality improves and the cost drops, and enough drafts/prototypes get shipped to production with iteration, the distance between “artifact” and “shipped product” shrinks toward zero.
When every conversation with a model can produce a functional application, the bottleneck isn’t building anymore. What stops the pre-built stuff from being packaged as products themselves? What happens to open source?
And underneath all of it, moral hazard. I’ve argued that LLMs face no downside risk, and that this is a structural problem. But introducing artificial downside risk into a system creates its own distortions. What happens when the cost of proving you’re right exceeds the value of being right? What happens when the staking mechanism becomes a barrier to entry rather than a filter for quality? These are not hypothetical, they’re the same problems that every accountability system in history has faced, from credit ratings to peer review.
I don’t have clean answers to any of these. But I think the questions are the right ones, and they’re worth working through in detail.
What do you think?
Thank you for reading
Sainath