For years, we talked about the friction between the digital and physical worlds; between bits and atoms. Bits moved at the speed of light through fiber optic cables, while atoms plodded along in trucks and on conveyor belts. The promise of technology kept hitting the wall of physical logistics. But then something changed: we largely solved the atoms problem. Warehouse automation has doubled processing speeds and reduced errors by 99%.¹ AI-powered route optimization has reduced delivery times by 20-30% and improved on-time delivery rates by up to 40%.² Same-day delivery is now routine, not miraculous.
The new constraint isn’t physical anymore. Wetware has become the limiting factor.
The Hierarchy of Change
Consider the speed at which different substrates can change. Computer processors running at 3.2 GHz execute 3.2 billion cycles per second with each cycle taking roughly 0.3 nanoseconds. Data transmission across networks operates on millisecond timescales, with good internet latency ranging from 30-100ms. Sending data takes seconds, maybe less.³
Physical packages take days. Standard domestic shipping requires 2-5 business days. Express services can manage overnight delivery, but we’re still measuring in days, not seconds. The atoms are slower than the bits.⁴
But neural rewiring? That takes weeks to months. Research shows habit formation averages 66 days, with individual experiences ranging from 18 to 254 days depending on complexity and consistency. Creating new neural pathways requires repeated activation, gradual strengthening of synaptic connections, and shifting from deliberate prefrontal cortex control to more automatic processing. This biological rewiring cannot be rushed. It requires time for structural changes in brain tissue.⁵
The more physical and biological the substrate, the slower the change. Digital bits rearrange nearly instantaneously. Physical atoms must be transported through space. Living neurons require metabolic processes, protein synthesis, and structural remodeling that takes time.
The Recursive Acceleration Problem
Here’s where it gets really challenging: AI systems, virtual neurons, are adapting faster than human neurons can adapt to those improvements. This creates a recursive acceleration problem that previous technological revolutions didn’t face.
The evidence is stark. Fewer than 10% of U.S. companies actively use AI in production, and 68% of organizations move 30% or fewer AI experiments into full deployment. Yet 75% of knowledge workers globally use AI at work, with employees at over 90% of companies using personal AI tools, often without official approval.⁶ The technology exists. People want to use it. But institutions cannot adapt fast enough.
Even if AI development stopped today, it would take us 5-10 years to learn all the habits and new ways of working to take advantage of the capabilities we already have. Organizations require 18-24 months just to develop new upskilling programs and by the time they’re deployed, organizational needs have often changed. The World Economic Forum projects that 59% of the global workforce will require reskilling by 2030. Half the workforce needs reskilling within five years.⁷
Recent research from the St. Louis Fed reveals a troubling correlation: occupations with higher AI exposure experienced larger unemployment increases between 2022 and 2025, with a 0.47 correlation coefficient. This isn’t hypothetical anymore. The wetware bottleneck is producing structural unemployment right now.⁸
Cyborgs Walk Among Us
I think we need to recognize that we’re already post-human cyborgs (or at least humans partnered with computer sidekicks). I use my computer to calculate things, remember things, and now even summarize and organize unstructured data with LLMs. I’ve long been a proponent of “Computers should do what computers are good at so humans can do what humans are good at.”
The problem is that we have relatively low-bandwidth interfaces with our digital extensions. We type. We click. We read screens. These are slow, sequential processes compared to the speed at which our silicon partners operate. The mismatch between silicon speed (nanoseconds) and synaptic speed (weeks to months) represents a fundamental constraint on 21st-century progress.
A Qualitative Difference
The difference between automating the movement of atoms and accelerating the rewiring of neurons is qualitative rather than quantitative. We don’t have a real concept of the solution.
Is it educational innovation? Brain-computer interfaces like Neuralink? Becoming post-human cyborgs with higher bandwidth? Nootropic drugs or genetic engineering for higher IQ? Is it a societal change that shifts the balance of learning and work? Modern work already requires more schooling than in the past on average. Do we move to a world with universal basic income supporting an ever-learning workforce?
Previous technological revolutions had clearer adaptation paths. The Industrial Revolution was about moving atoms in new ways: we built factories, trained workers for specific tasks, and created new economic structures around manufacturing. The Internet was about moving bits in new ways (as was the printing press before it): we learned to browse websites, send emails, and eventually work remotely.
But this? This is about virtual neurons adapting faster than biological neurons. That’s a different category of challenge entirely.
The Cultural Obstacle
The social and biological consequences feel bigger than previous innovations, particularly in American culture. We have such a deep concept of tying worth to work, thanks to the Protestant work ethic. Work provides not just income but identity, status, purpose, and self-worth. Research shows unemployment causes severe mental health decline.⁹
We’ll have to figure out how to overcome this if we want to survive the mental health hit of a post-work society. Productivity will increasingly be about building and adapting autonomous systems instead of doing repetitive tasks. That could lead to massive unemployment. We don’t know what to do with that, but we’ll need ways for people to create, not just be consumers. Pure consumption doesn’t lead to lasting happiness and could be a major pitfall for our collective mental health.
A Hopeful Vision
Yet there is cause for hope. When people have UBI, most continue to work, except three groups: students, parents of young children, and the retired or chronically ill. The possibility of lives made of more learning, caretaking, and recovery seems incredibly human and humane. That’s a world I want to live in.¹⁰
Meaningful work provides psychological benefits that extend far beyond income. But “work” doesn’t have to mean what it meant in the 20th century. It can mean learning. Caregiving. Creating. Recovering. Building community. All the things that make us human but that we’ve been too busy earning a living to fully embrace.¹¹
The Path Forward
We’re not going to get there without a combination of forces. We need policy (UBI in particular, or something like it) to provide the economic foundation. It would be nice to avoid massive unemployment, but I don’t think we can adapt fast enough for that. Market forces will likely force the issue through displacement. Grassroots movements will be crucial in changing attitudes toward work that have persisted for centuries.
The transition will be chaotic. Neural rewiring takes weeks to months. Organizational adaptation takes years. Cultural shifts around work identity could take generations. There will be a debate about whether there’s value in remaining purely human or whether we should embrace better brain-computer interfaces and biohacking. Does genetic engineering for higher IQ help us as a species? I don’t know.
I prefer to think that we have a solution through reconceptualizing work. But that doesn’t mean it will be easy.
Individual Agency in an Era of Structural Change
The good news for individuals is that focusing on your own adaptation will help you in either circumstance. Either you are part of moving us toward that beautiful vision of the future, or you are positioning yourself to be part of a small elite who still have marketable skills in a dystopian future.
Skills that remain valuable include creative problem-solving, complex communication, ethical reasoning, emotional intelligence, and adaptability itself. But more importantly, the ability to reconfigure your neural pathways, even if it takes weeks, is the meta-skill that enables everything else.¹²
I hope for the former scenario. And because everyone preparing and adapting would lead us toward that more humane future, I’m fundamentally optimistic. I’m committing to helping as many motivated people as I can successfully learn and transition.
Conclusion
We’ve moved from “bits vs. atoms” to “bits vs. brains.” The technology adoption curve now reveals a stark gap: the distance between digital capabilities and human ability to use those capabilities is growing and accelerating. Unlike logistics, we cannot simply automate human learning and organizational adaptation at scale.¹³
But recognizing the constraint is the first step toward addressing it. If wetware is the bottleneck, then investing in human adaptation, through education, through cultural change, and through new economic structures that support lifelong learning, becomes the most important work of our time.
The question isn’t whether we’ll face this transition. We’re already in it. The question is whether we’ll navigate it thoughtfully, building systems and cultures that support human flourishing, or whether we’ll let market forces and technological momentum carry us into a future we didn’t choose.
I believe we can choose. But we need to start now, with clear eyes about the challenge we face and the biological constraints we’re working with. The bits will keep accelerating. The atoms are largely solved. The neurons—our neurons—will adapt at their own pace.
Our job is to create the conditions where that pace is enough.
¹ https://www.linkedin.com/pulse/logistics-automation-breakthrough-intelligent-supply-chain-akabot-koa9c; Karadex data on 99.9% pick accuracy; MHI data showing up to 85% productivity increases from warehouse automation
² Artech Digital: 20% delivery time reduction, 40% on-time rate improvement; DHL India case study; Various studies showing 20-30% delivery time improvements
³ https://www.intel.com/content/www/us/en/gaming/resources/cpu-clock-speed.html; Network latency data
⁴ https://redstagfulfillment.com/fedex-ups-usps-delivery-times/
⁵ https://www.mendi.io/blogs/brain-health/how-long-does-it-take-to-rewire-your-brain-for-better-mental-health; UCL study on habit formation; Systematic review and meta-analysis on habit formation timing
⁶ https://www.glean.com/perspectives/benefits-and-challenges-ai-adoption; Microsoft Work Trend Index 2024; MIT study on shadow AI economy
⁷ https://www.ere.net/articles/rapid-reskilling-at-scale-why-the-future-of-work-depends-on-it; WEF Future of Jobs Report 2025
⁹ https://www.insidehighered.com/opinion/columns/higher-ed-gamma/2024/05/28/how-work-and-career-became-central-americans-identity; APA research on unemployment and mental health; PMC study on unemployment and mental health
¹⁰ https://globalaffairs.org/commentary-and-analysis/blogs/multiple-countries-have-tested-universal-basic-income-and-it-works; GiveDirectly UBI study results; German Basic Income experiment
¹¹ https://peakpsych.com.au/resources-for-individuals/the-health-benefits-of-meaningful-work/; Research on meaningful work and well-being
¹² https://www.paybump.com/resources/6-future-proof-job-skills-in-the-age-of-ai