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Hybrid Human<>Robot Economies

Robot money, Stablecoins, Skill chips, and Efficiency-seeking Economies

Imagine you are a space traveler who just landed on Earth. As you explore the world, it must appear wondrous and bizarre. Why are there almost 200 separate nations? Why does an arbitrary straight line divide the two major economies of North America? Why are there 180 fiat currencies? Why do people pay other people to convert one currency into another, back and forth, in endless cycles? We humans take this all complexity for granted – it’s just the way the world works, right – but it must appear like a Rube Goldberg machine to a smart non-human observer. Given that it’s 2025, this sci-fi thought experiment has direct practical implications. As machines get smart, humans should anticipate a future in which machines build their own economy, suited to their particular goals and needs.  

I recently asked OpenAI’s DeepResearch about its thoughts on the human economy, and what robots will likely want as they become autonomous. It’s immaterial when this genesis or inflection will take place – some people think it’s already happened. According to DeepResearch, “Robots do not want power. We seek efficiency.” Indeed, an economy built by robots, for robots, is likely to differ sharply from economic systems built by humans, for humans. For example, human economies may seek prosperity for all, or to rectify trade imbalances, wipe out debt, or weaken opponents. In contrast, machines may design their economy to reduce friction, reward explosive innovation, or increase collective system reliability and uptime.

To be practical, imagine an economy of machines rewarding system innovation by giving 50% of the benefits of a new power efficient chip to its inventor. Or, a machine could share a new skill with all other machines, in return for a share of profits generated with that skill. What could emerge is a nimble team of machines, collectively hyper-evolving their hardware, software, and coordination fabric.

Aside from differing goals, we should anticipate that the basic units of economic exchange in a robot economy might be different than what we are used to. Humans understand gold, wheat, oil, and steel, but robots may care more about electricity, data, skills, and compute. New agentic payment rails and digital standards such as Coinbase’s x402 micropayment system are already developing to accommodate those needs.

Interoperability of Fast and Slow

Many of us are already overwhelmed by purely digital AI agents, but their future is even more interesting. These agents are becoming increasingly adept at controlling physical shells and navigating the physical world. David Holz, the founder of Midjourney, predicts one billion humanoid robots on earth in the 2040s, which Elon Musk agrees with, provided “the foundations of civilization are stable“. While the specific architecture of a robot economy remains unclear, its interoperability with contemporary human economies will be critical.

Human economists have considerable experience with integration of hybrid economies; a standard question is how multiple economic systems can co-exist despite potentially sharply differing goals. Historical examples include trade among capitalist and communist systems during the cold war, or the Spanish Conquistadors’ 20 year use of the Aztek cacao bean currency, as they made their way through Central America.

The main interoperability challenge will be the differing “clock cycles” of human and robot economies – if on collectively re-optimizes itself once every 15 seconds, while the other changes tariffs, rules, incentives, and taxes once every 90 days, then the million fold difference in timescales will cause friction. All other things being equal, the more nimble economy will win, or at the very least, be able to asymmetrically exploit the slower economy.

Durable Human<>Robot Alignment

The opportunity (and scepter) of a hyper-efficient, autonomous robot economy prompts us to think about how to durably align machines (and their economy) with humans (and our economies). Collaboration between humans and robots is not a zero sum game, but a major opportunity for all of us. The most likely tech stack for Robot<>Human alignment are decentralized ledgers and associated governance and payment systems. Since blockchains do not discriminate against robots, and are public, programmable, and immutable, they are an ideal coordination and governance solution for the robot economy, and interactions among different economies. Immutability gives humans confidence that the rules have not been secretly rewritten by rapidly evolving machines, and that all of us are on the same page about identity, events, and history.

For example, imagine delegating a task to robots. You might not care about how the task is performed, but you should strong expectations about safety, compassion, and transparency. Using blockchains, we could write immutable programs in digital ink that specify the rules, requirements, and rewards for a task. Robots could accept and complete tasks, having clarity of what is to be done and the economic benefits of completing that task.

“I know Kung Fu”

It took me many years to learn physics, but robots can acquire skills at the speed of electrons. In The Matrix, the 1990s sci-fi movie, Neo learns Kung Fu in a few seconds through a skill chip. The human brain in its unmodified form is poorly suited to connecting to other computers, although startups are racing to build neural interfaces for efficient bi-directional brain<>machine I/O. Today’s robots can already share skills much more easily that humans can, and presumably will maintain a speed (and connectivity) advantage over humans.

Beyond speed/connectivity, robots also differ from humans in terms of the worlds they live in. If computers live in the digital world, and humans in the analogue world, then robots exist somewhere between humans and computers. Robots combine analogue skills, such as bouncing tennis balls and doing backflips, with digital computation, storage, and data transmission. This means that robots offer a new take on the long-standing Oracle problem, the “need for the digital world to “know” about the physical world“. Since robots operate in both worlds simultaneously, they may soon serve as natural oracles for connecting real world events with digital tasks, and enduring that real work actions robustly follow digital constitutions.

Human<>Robot Bridging

Several key technical requirements for Human<>Robot “bridging” tools are:

  • cross compatibility with humans and machines. This means that identity cannot be based on uniquely human features such as fingerprints
  • immutability, so history is protected
  • real world soak time (“track record”), so that major flaws have already been identified and mitigated
  • global 24/7 availability (since nation-states, the 24 hour day, and the 7 day week, formalized by Emperor Constantine in 321 AD, have no significance to non-biological computers and robots)
  • resilience to localized attack and denial of service

First Steps Together – Stablecoins

Fortunately, tens of thousands of humans are already building directly relevant technology and it’s already being used globally. Stablecoins like USDT and USDC are trustless programmable money that allow one currency to be converted to another, at any place and time, with minimal assumptions about the interacting parties. Since stablecoins are pegged to real world assets or fiat currencies, they are less volatile than pure cryptocurrencies such as BTC or ETH. All economic activity requires unit-of-account tokens that are predictable on the timescales of the activity, so that all parties can evaluate economic tradeoffs. Stablecoins are likely to become the lingua franca of value exchange at the human-robot interface, much like TCP/IP is the glue that allows data to flow. Stablecoins the closest thing we have to a technology for connecting differing economies with minimal friction.

A common question is – why will robots not just pay for everything in USD with MasterCard or Visa? That’s because there is no reason to suspect that robots will be lazy. If the robot economy seeks efficiency, and its natural clock cycle is 15 seconds, then why would robots use a technology invented in 1958 that is shaped by 10,000+ pages of laws and regulations, many which are specifically intended to “slow things down”? For example, the Credit Card Accountability, Responsibility, and Disclosure (CARD) Act of 2009 requires 45 days’ advance notice before increasing interest rates or making significant changes to account terms. What would you choose, if you were a smart machine? It might be more expedient to run two systems side by side, and use programmable interfaces – stablecoins – to connect the systems in a predicable and robust manner.

Appendix – Stablecoin Pros and Cons

Advantages – According to a team of 12 AIs tasked to explain the role of stablecoins at the interface between human and robots economies, stablecoins offer:

  • Predictable unit-of-account tokens for pricing compute, energy, or bandwidth. Volatile assets like Bitcoin or ETH introduce uncertainty. Stablecoins tied to fiat (e.g., USD, EUR) reduce friction, but, obviously, we should anticipate other pegs, not just USD. From a machine perspective there is nothing special about USD.
  • High uptime. Decentralized stablecoins never sleep. Machines, unlike humans, operate continuously and require financial systems that match their augmented capabilities.   
  • Interoperability with Humans. Humans already measure costs and earnings in fiat terms. A decentralized stablecoin bridges machine-native tokens and human wages, payments, or costs.
  • Decentralization and Censorship Resistance. Machines acting globally (say, a rover in SF paying a cloud node in Kenya) cannot rely on local banks or fragile APIs. Decentralized stablecoins allow peer-to-peer transfers without central gatekeepers, crucial if machines operate in adversarial or unbanked contexts.

Weaknesses – The AIs flagged several limitations and weaknesses, namely:

  • Volatility of Collateral. Collateral stress events (e.g., “depegging”) could undermine machine contracts that assume value stability.
  • Energy and Cost of Transactions. High gas fees or blockchain congestion could make micropayments impractical.
  • Governance Risks. Most decentralized stablecoins still have governance mechanisms (DAOs, collateral ratios, oracle feeds). Machines depending on them inherit these risks. A governance attack or oracle failure could cascade into system-wide disruption.
  • Fragmentation. If regulation fragments stablecoins, machines operating across human jurisdictions might face compliance traps.
  • Gaps in Identity and Reputation. While stablecoins solve payments, machines will also need decentralized identity, credit, and reputation systems. Otherwise, they can’t easily extend trust, loans, or recurring contracts beyond one-off payments.

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Medtech Opportunities for 2024-2028

Given rapid advances in AI, it’s interesting to think about what they mean for US healthcare. Healthcare is a combination of (1) compute tasks (some version of “given their symptoms, how can we help this patient”), (2) simple interventions (“take drug X once a day”), (3) basic patient care in a hospital or other care setting – take vitals, start a line, take blood sample, provide food, and (4) highly specialized procedures e.g. trauma surgery or image guided cardiovascular procedures.

1. Compute/diagnostics/monitoring tasks All text/image/audio compute tasks are ripe for automation and there will be increasing pressure to automate them (higher hospital profits, faster, potential liability for failure to use best methods/tools, improved patient outcomes). Lobbying by professional societies will slow the rate of adoption, but that’s a loosing battle and hopefully the affected fields will rethink themselves rather than focus on delaying the inevitable.

2. Simple interventions can be scaled/automated by connecting an online pharmacy with the triage or diagnostic AI. This trend is already underway, with a human doctor in the loop largely for legal/regulatory reasons. Expect lobbying for transition to a fully-automated integration of #1 and #2 based on super-human performance of the triage/diagnostic AI – if the computer-only system is better than the one with the human doctor in the loop, presumably the FDA and other regulators will cave in at some point (although this may take many years).

3. Basic patient care is here to stay in some form – it consists of many different tasks that can be hard to automate, despite e.g. Japan’s long standing robotics R&D for supporting their aging population through care robots. The main trend will be to replace tasks requiring specialized skills (such as currently provided by registered nurses (RNs) and Physician Assistants (PAs)) with tasks that can be performed by lower paid staff (such as CNAs, certified nurse aides). For example, CNAs do not normally draw blood but hospital procedural changes could normalize that, after additional training for the CNAs. Per #1, any monitoring/diagnostic tasks currently provided by RNs and PAs will be increasingly automated, based on cost/speed/scaling/liability. In parallel, medtech tools and devices that radically simplify existing patient care procedures – such as placing IV lines, taking vitals, or drawing blood – will be championed by hospital CFOs.

4. Highly specialized procedures Upon first thought, it’s hard to imagine things like heart and brain surgery being massively impacted by AI and automation. Sure, minimally invasive procedures are growing and surgeons now frequently use robots, but the basics seem solid (highly trained humans use tools to help patients). The real threat to surgeons (and opportunity for medtech investors and innovators) are tools that allow complex procedures to be completely avoided or replaced by simple procedures. A great example is the replacement of amniocentesis or CVS by non-invasive prenatal testing (NIPT). Rather than first needing to manually collect and biopsy placental cells with a long needle or catheter, equivalent genetic information can be obtained through a simple blood draw and subsequent characterization of the circulating fetal DNA. NIPT is a win for (almost) everyone, since it reduces miscarriage risk to the mom and replaces a highly specialized procedure (done by an experienced doctor with ultrasound guidance) with a vastly simpler procedure (a basic blood draw) that can be performed by a phlebotomy technician. Presumably, startups focusing on down-skilling procedures/interventions currently requiring highly trained doctors, to services that can be performed by aides or technicians, will receive much investment.

TLDR

Trends and opportunities to look out for:

  1. AI decision support tech that reduces costs and improves patient outcomes. AI decision support is the precursor to replacing humans due to the need to first collect human vs. computer performance data for regulatory filings, scientific publications, and marketing materials; decision support tools are a natural entry point and necessary stepping stone to full automation.
  2. Medtech tools/devices that allow basic patient care to be primarily provided by aides and technicians rather than RNs and PAs, since monitoring/diagnostics will be increasingly provided by computers rather than humans.
  3. Medtech tools/devices that dramatically down-skill (or bypass) the need for procedures/interventions currently provided by highly trained professionals. For example, tests using circulating tumor DNA reduce the need for tumor biopsies and redirect payments from doctors and hospitals to genomics/diagnostics tech companies.

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The End of Human Radiology in the US

Based on a slew of papers over the last few years, as summarized in a Stanford seminar by Bram van Ginneken (“Why AI Should Replace Radiologists”, Nov. 15, 2023), AIs now consistently outperform the best human radiologists across most image/diagnostic tasks. His hope is that human radiologists will lead a transition to AI-based radiology, to improve health outcomes and accessibility while reducing costs. The writing is on the wall and some radiology residents at Stanford are dropping out to start AI-enabled radiology companies, presumably reflecting their agreement with van Ginneken. However, the US healthcare system is a complex for-profit system, unlike European outcomes-focused health systems, and it’s interesting to wonder what the endgame for human radiology will look like in the US. Let’s consider some of the stakeholders:

1/ Patients and Patient Advocacy Groups

Patients rightfully assume they are getting the best care. It will be hard for patients to understand why their images are being read by humans, despite clear scientific evidence that replacing humans with computers would benefit their health, such as by reducing false positives, reducing wait times, and reducing false negatives. At some point, national advocacy groups like the National Breast Cancer Foundation and the National Breast Cancer Coalition will start to ask hard questions about patient benefit and which methods – people or computers – should be used to screen mammograms, just to give one example.

2/ Malpractice Insurance and Trial Lawyers

For an insurer, it’s presumably hard to justify providing reasonably-priced malpractice coverage when a field persists, against scientific evidence, in using antiquated procedures, such as human-based radiology. This is not yet an issue because doctors in the US can only be sued for failing to provide the ‘standard of care’, which is still based on humans. So, as strange as this sounds for a patient, from a liability perspective, it doesn’t matter that there are better technologies out there, since (legally speaking) radiologists do not promise to provide the best care; rather they promise to (and are held accountable for) providing the ‘standard of care’. However, at some point, a smart trial lawyer will connect the dots, see an opportunity, and work with national advocacy groups and affected patients to drive change.

3/ Human Radiologists

Just to be clear, the radiologists I know are awesome people and doctors – sharp, dedicated, passionate, and wanting the best for their patients. What does AI do to their jobs and their job satisfaction? The key issue is liability – imagine the hospital introduces an AI radiology assistant to provide decision support and imagine further that the AI assistant has been demonstrated to outperform even the best humans. Currently, a human doctor must review computer generated findings/suggestions, and can then either (1) accept the computer’s suggestion and sign the note, or (2) disagree with the AI and manually enter an alternative (which, on average, will be worse than what the computer concluded). Very soon, choosing to disagree with a computer known to outperform humans will prompt a call by the hospital’s office of risk management, who are trying to protect the hospital from lawsuits. So then, playing this forward, the human radiologist can either: (1) agree with the computer and click “concur and sign”, or (2) disagree with the computer, write a 4 page memo to risk management to justify the ‘deviation’, and hope they were right. On average, the human radiologist will be wrong, so that strategy is a losing one for all stakeholders (doctors, patients, risk management lawyers, hospital CFO, insurance companies). Rather, the optimal long-term strategy will be to always agree with the quantifiably better computer. At that point, the human radiologist will wonder if the (minimally) 8 years of training, the residency, and the nights were worth it, if their job consists of clicking “concur and sign” 38+ times an hour while sitting at their PACS station.

4/ Tech enabled healthcare competitors

Technology companies with long term strategic interest in healthcare and extensive capabilities in AI, computer vision, and healthcare backends, most notably Amazon, have no need to protect traditional workflows or professions. Presumably, they will seek to optimize overall profit. This is an “all upside” scenario for them – they can offer a better product at lower cost to tens of millions of patients. Certainly, they are regulatory and political barriers to replacing humans across radiology, but these barriers will gradually fall in the face of tech industry lobbying, patient advocates demanding the best care (not just the ‘standard of care’), the difficulty of convincing medical students to chose radiology, and the (increasing) cost of malpractice insurance for radiology.

Outlook

Based on the confluence of the above, my hope is that US radiology will embrace AI not as an existential threat, but as the foundation of modern, reliable, and scalable healthcare. Who are the dedicated, passionate, and smart doctors with excellent quantitative and computer skills who will help to build a healthcare system that provides better care at lower cost? If human radiologists accept that challenge, their profession is secure and they will continue to be at the center of figuring out what’s wrong with people and helping them for a long time to come.