It’s 2035.
A $10,000 household robot just made your morning coffee, repaired a leaky pipe, and folded the laundry, all before you woke up.
It didn’t come from a sci-fi movie. It came from the same unstoppable math that took us from a $2,000 beige box in 1995, barely able to play a grainy video, to a laptop today that outperforms NASA’s Apollo-era mission control.
Why Now? The Perfect Storm of Convergence
For decades, we have had three rivers flowing, but they are starting to converge, and the large language models were just the beginning of that synergy.
The Three Rivers Converging
- Foundation Models: ChatGPT moment for robot reasoning (2023-2024)
- Battery Revolution: Energy density finally crossing industrial thresholds
- Commoditized Mechatronics: 20 years of smartphone sensors + automotive actuators hitting critical mass
Previous robot waves failed because they needed to be customized for everything, but this next wave will succeed because it's built on the leftover R&D, with the influx of money looking for yield.
The Compounding Advantage
Unlike previous automation waves (which replaced specific tasks), this wave creates generalist systems that improve through:
- Fleet learning across applications
- Shared foundation models
- Standardized hardware platforms
- Network effects in tooling ecosystems
This is where iteration stops being linear and starts to curve upward.
The Iteration Robotics Law Defined
If Moore’s Law was “transistors double every 2 years,” the Iteration Robotics Law could be:
Task capacity per dollar doubles every 3–5 years, driven by converging advances in AI, energy, and mechatronics.
We can model it in three ways:
- Conservative (2× / 5y): Supply chain friction, slow regulation, complex integration.
- Base (2× / 3.5y): Steady multi-vector progress, occasional leaps.
- Optimistic (2× / 3y): AI and hardware convergence triggers step-change breakthroughs.
What "Task Capacity per Dollar" Actually Means
It’s not just about faster assembly lines, and Ford would be jumping at the chance to play in this space. It’s the blend of:
- Precision: Millimeter accuracy under load
- Speed: Cycle time per operation
- Adaptability: New tasks learned per training hour
- Uptime: MTTF measured in years, not months
- Intelligence: Context-aware decision making
The 2035/2045 Implications
By 2035, the base case scenario suggests robotics capability that's 8x more cost-effective than today. By 2045, we're looking at 50x improvement, making physical automation as ubiquitous as smartphones are today.
The Leading Indicators Framework
So, how do we “spot the curve” in real-time? We need to break it down into the components that spark this fire, cause when it ignites, it will be something to see.
Technical Metrics That Matter
- Battery Performance (Wh/kg + cycle life): The energy foundation
- Actuator Economics ($/Nm for precision, $/kW for power): The muscle
- Edge AI Efficiency (TOPS/W without throttling): The brain
- Reliability (MTTF under real-world conditions): The durability
- Ecosystem Density (off-the-shelf components): The acceleration multiplier
Market Signals to Watch
- Regulatory Velocity: Pilot-to-deployment timeframes
- Fleet Learning: Logged autonomy hours per vendor
Unlike software metrics, robotics improvements require physical validation. These indicators predict where the exponential curve accelerates or hits friction. We need metrics to separate hype from reality.
Investment Implications
The Paradox of Timing
While U.S. equity markets may face multiples compression (as outlined previously), the robotics revolution could create fundamental value that transcends valuation cycles.
How do you position for a 20-year technological shift while navigating a 6-8 year valuation compression?
Three Investment Theses
- Pick and Shovel Plays: Companies selling standardized components to robot builders.
- Application Leaders: First movers in specific verticals (manufacturing, logistics, healthcare)
- Platform Winners: Companies building the "iOS for robotics"
The Valuation Reality Check
Even transformative technology can generate poor returns if bought at peak multiples. The robotics revolution is real, but so is valuation gravity.
But before we get carried away by exponential curves, we need to grapple with some uncomfortable realities.
The Hard Questions
The Labor Transition
If task capacity per dollar improves 50x over 20 years, what happens to human employment? This time, even service jobs aren’t safe, and policy needs to shift from reaction to preparation.
Who controls the robotics stack controls the next century's economic advantage.
The Geopolitical Stakes
The country that dominates robotics components and platforms will have the same advantage in physical systems that the U.S. currently enjoys in software platforms; therefore, the risks break down into the three main categories:
- Supply chain dependencies (semiconductors, rare earths, precision manufacturing)
- National security implications (defense applications, critical infrastructure)
- The U.S.-China technology competition in physical systems
The Infrastructure Question
Reality is, Physical robots are more than algorithms; they need physical infrastructure:
- Standardized interfaces and protocols
- Maintenance and support networks
- Safety and liability frameworks
- Energy and connectivity requirements
Boston Dynamics' Atlas can now do backflips, but it costs $200K+ and breaks frequently. What happens when that same capability costs $20K and runs for months without maintenance?
Preparing for the Robotics Decade
Depending on your focus, let’s break down a cheat sheet to keep you aligned.
For Investors
- Track the leading indicators
- Think in decades, not quarters
- Diversify across the stack (components, applications, platforms)
- Consider geographic exposure (U.S. innovation vs. Asian manufacturing vs. European regulation)
For Businesses
- Start small, think big: Pilot programs in controlled environments
- Build internal capabilities: Robotics integration will become a core competency
- Partner strategically: Few companies can build the full stack alone
For Policymakers
- Proactive workforce transition: Retraining programs before displacement peaks
- Infrastructure investment: 5G, edge computing, standardized interfaces
- Regulatory frameworks: Balance innovation with safety/security
For Individuals
- Develop complementary skills: Creativity, relationship building, and strategic thinking.
- Understand the technology: Basic robotics literacy becomes table stakes
- Invest in adaptation: Continuous learning as a competitive advantage
The Next Moore's Law
We're potentially witnessing the birth of the next great exponential law. Just as Moore's Law shaped five decades of economic growth, the Iteration Robotics Law could define the next chapter of human productivity.
You may have noticed the play on words, “IRL”, a nod to the acronym for “in real life” as we come full circle back from the digital journey.
The next Moore’s Law won’t just make computers faster. It will make everything in the physical world faster, cheaper, and more capable, from surgery to shipping to the coffee in your kitchen.
Fifty years from now, people will talk about the Iteration Robotics Law the way we talk about transistors today. You have two choices: help shape it, or watch it reshape you.
The curve is already bending upward. The only question left is, will you be ready when it takes off?