Scarcity Amplification in the AI Era

One of the most profound questions of the coming decades will be how we manage scarcity in an era defined by AI-driven abundance of certain things and profound scarcity of others. As automation accelerates, jobs will continue to shift—and in many cases vanish. The more routine a task, the quicker it can be taken over by machines; even tasks that are less repetitive can yield to automation with the right context and data inputs. As a result, the things that cannot be fully automated—fundamental resources like fertile land, potable water, and reliable energy—will only become more valuable. Left unchecked, this dynamic risks transforming basic sustenance from a right into a privilege.

For a while now, we've heard that AI will create wealth. That might be true. But wealth doesn't simply materialize out of thin air; it accrues to those who control the factors of production. In the past, that might have meant who owned the factory or the farmland. In the future, it could mean who controls the most sophisticated AI models and the sensors and actuators they direct. Imagine a scenario where large swaths of the population find themselves in a world run by algorithms, with critical resources allocated not by democratic institutions, but by predictive models that optimize for something beyond our direct control.

Digital-Physical Integration

A signpost of this shift is already visible in the companies shaping the next wave of automation. Boston Dynamics builds "hands"—robotic arms that can manipulate the physical world. Palantir offers "eyes"—analytics platforms that can see patterns in data and environments. OpenAI provides "brains"—the generative and predictive intelligence to direct these systems. And they are not alone; every big player in AI is racing to link the digital and physical realms. If we connect all these capabilities—hands, eyes, and brains—we get fully integrated systems that manage production, allocation, and consumption with minimal human oversight.

In such an environment, the lines between the digital and physical worlds blur. Initially, we might assume humans stay in the loop, providing ethical guardrails and strategic guidance. But as these integrated systems mature, human intervention may start to seem like a bottleneck. Eventually, the role of human oversight may dwindle to a formality, while the real decisions—about who gets what, when, and how—are made by algorithms.

Tech Consolidation and Resource Hoarding

The natural endpoint of this progression is centralization. Companies that dominate AI capabilities will also command the ecosystem of resource distribution. Their predictive models, trained on oceans of data, will grow ever more adept at orchestrating logistics, predicting demand, and responding to fluctuations in weather, markets, and geopolitics. At first, this centralization might look like efficient management—fewer shortages, less waste. But efficiency often comes with a political price. When a handful of entities can predict and shape economic and social outcomes, they can tilt the playing field in their favor, reinforcing their dominance.

This is a situation tailor-made for inequality. Historically, those who control the resources control the terms. The difference now is that the resources in question will be both physical and digital. Data becomes an asset as essential as land. The owners of top AI models hold a key to reshaping markets, while everyone else risks being reduced to mere consumers, their access contingent on goodwill or their ability to pay.

AI as Gatekeeper

In a world where AI optimizes resource usage, the allocation of basic necessities might become a function of algorithmic logic. Without transparency, such allocations could appear arbitrary or even malicious. We must decide how to build systems that can explain their choices and remain accountable to humans. If we fail, AI-driven gatekeeping could elevate a new technocratic elite who need not justify their power in traditional political terms. The old checks and balances might not translate into a world managed by machines.

The Shift to Local Resilience

If massive centralization is one extreme, the other might be a return to local resilience. If you can't rely on algorithms in the cloud to feed you or to secure your energy supply, you might turn to what you can control yourself. Modern homesteaders might cultivate small patches of land, tap into local solar grids, and develop their own micro-economies. Ironically, this echoes a kind of neo-feudalism, where communities revolve around a local stronghold—be it a regenerative farm or a solar cooperative. This "stronghold" might provide basics like food and energy, but it won't easily supply advanced tools, specialized healthcare, or cutting-edge education without access to global networks.

Such local strategies might offer a cushion, but they won't solve the underlying dilemma: the rich continue to amass wealth through AI-driven efficiencies, while those without access risk sliding into negative value balance—consuming more than they produce according to the machine's tally. The question becomes: can local resilience ever scale enough to offer genuine autonomy, or will it remain a fallback option for those excluded from the centers of AI-driven production?

Collapse or Transition?

One way to frame this moment is as a fork in the road. If we allow resource management to fall under the exclusive purview of a few advanced AI systems owned by a handful of players, we could see the collapse of traditional economic roles. On the other hand, this could be a transitional phase that forces us to redefine what it means to work, to contribute, and to have a meaningful life. The potential outcomes run the gamut from near-ubiquitous abundance to dystopian stratification.

OpenAI's recent decision to release a new feature every day illustrates the rapid velocity of AI innovation. Such a pace disrupts entire markets, leaving smaller AI startups—many focused on user experience and data point collection—scrambling to compete. When a single entity can shape the direction of the industry at a breakneck speed, it compresses innovation cycles and forces everyone else to adapt or die.

Opportunities and Strategies

What should individuals and businesses do in this environment?

The Race for Better Interfaces

Recent insights on companies like Palantir highlight the race to create the lightest, fastest interfaces for plugging data into AI. The next logical step: feedback loops that let algorithms learn and respond instantly. This compression of the input-output cycle accelerates the entire ecosystem. It means even local strongholds seeking resilience need to understand how to integrate such tools—or at least be aware of their constraints.

Programmers will leverage AI to create value at astonishing speeds, finding product-market fit in days or hours rather than months. Consumers benefit from rapid improvements, but the competitive environment makes it harder to carve out stable niches. Winners reap enormous rewards; laggards struggle to survive.

In the final analysis, the question is whether we treat AI as a tool to enrich everyone—or as a mechanism to enforce scarcity and control. We're building the future right now, and we have a say in its design. If we want to avoid a world where AI-gatekeepers ration what we need to survive, we must ensure that both the code and the resources it manages remain accessible. Otherwise, we risk a future where abundance is theoretically limitless, but access to it is artificially constricted.

Acknowledgments: I'd like to extend my gratitude to those who influenced and inspired the ideas in this article. First, to the people I know personally—my wife, Svetlozara Taneva; my co-founder at Replero, Ivelin Iliev; as well as Mladen Venev, Boyan Peychev, Nikolay Danev, Ivan Vankov (gatakka), Vasil Zagorov, Joe Lemay (The Longevity Dude), Stefan Milev, Sean Jadoon, Mario Peshev, and Vassil Popovski—your insights have been invaluable. And to those I don't know personally but whose thoughts and work have shaped my thinking—Paul Graham, Elon Musk, Sam Altman, Alex Karp, Bill Gates, Warren Buffett and Pejman (PJ) Milani. If I've missed anyone, please know it's not intentional; I may add you later as you come to mind.

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