With Elon Musk pivoting Tesla towards the far larger humanoid robot industry, we have been invited to consider what this world will look like when every human has at least one cobot companion, with the understanding that we would soon find ourselves with many cobots in various forms and sizes.
As Elon ponders the shift from autonomous transportation to the market of human labor and even human thought, the conversation includes topics such as universal basic income and the promise of the Abundance Society -- Universal High Income
Planetary Intelligence
We must embrace the embedding of intelligence into every technology and device we create in order to garner the exponential benefits that come from building a truly intelligent planet and empowering all residents to participate in the planetary scale collective intelligence this will bring.
We are looking to inspire discussion on cobot development and soliciting input on this topic including our assumptions with the hope that collectively we can manifest societies filled with unimaginable abundance, based upon the technology we develop together, today.
Assumptions
Costs and Pricing
Estimated Build Cost (USD)
- 2025: $50,000
- 2030: $44,000
Estimated Retail Price (USD)
- 2025: $50,000
- 2030: $21,000
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Allocation
We assume that 50% of cobots manufactured in 2025 will be sold to private enterprises, and that 50% will be allocated to public enterprises.
By the end of the decade, we anticipate that to move to 60% public applications.
This enables cobot manufacturers to recognize sales revenue and provides cash flow to fund further development, while also creating public autonomous infrastructure that becomes the Abundance Societies' wealth engine.
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Duty Cycles
We anticipate initially modest duty cycles in both private and public enterprise an organization adopt the technology, with private cobots reaching 80% and 90% by 2030 for embodied and cognitive services respectively.
At high levels of utilization, cobots create autonomous infrastructure that delivers manyfold returns in economic benefits that provide the wealth to Abundance Societies.
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Service Pricing
Private cognitive and embodied services are priced higher than public as we expect high risk/reward applications for cobots to fall within private enterprises, while the public applications look to replace low paying hard labor jobs.
Private enterprise may for example charge a premium for cobots involved in hazardous work and environments, while the public interest might be on low cost ditch digging.
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Inference Energy Efficiency
Inference energy efficiency may be the most important element in creating intelligence.
Inference energy efficiency refers to the performance of AI models in terms of the number of Tera Operations Per Second (TOPS) per watt of power consumed.
This metric is crucial for the deployment of AI applications, especially in edge devices and other environments where power is limited.
Role in Developing Smarter AI
Here’s a detailed look at its role and the projected impact of a significant increase in inference energy efficiency:
1. Edge Computing and IoT: Improved energy efficiency allows for more complex and capable AI models to be deployed on edge devices such as smartphones, drones, wearables, and IoT sensors. This enables real-time decision-making and advanced functionalities directly on the device without relying on cloud computing.
2. Sustainability: AI models, particularly large ones, can consume significant amounts of energy. Enhancing energy efficiency reduces the carbon footprint of AI operations, contributing to more sustainable AI development.
3. Cost Reduction: Lower power consumption translates to reduced operational costs, especially in data centers where energy expenses are substantial. This makes it more economically feasible to deploy advanced AI solutions at scale.
4. Scalability: With better energy efficiency, it becomes possible to scale AI applications across more devices and use cases, expanding the reach and impact of AI technologies.
5. Battery Life: For battery-powered devices, such as mobile phones and autonomous drones, improved energy efficiency means longer battery life, enhancing the user experience and operational longevity.
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Impact from 2025 to 2030
Assuming inference energy efficiency increases by 200 times, from 5 TOPS/watt to 1000 TOPS/watt, here are some projected impacts:
1. Exponential Growth in AI Applications: The drastic improvement will enable the deployment of AI in numerous new areas, including more sophisticated robotics, enhanced augmented reality (AR) and virtual reality (VR) experiences, and more advanced autonomous systems.
2. Pervasive AI in Everyday Devices: AI capabilities will become ubiquitous in everyday devices, from smart home systems that better understand and respond to user needs to personal health monitoring devices that provide real-time insights and recommendations.
3. Smarter Cities and Infrastructure: Increased efficiency will allow for the integration of AI into urban infrastructure, leading to smarter cities with optimized traffic management, energy distribution, and public safety systems.
4. Advances in Healthcare: Portable medical devices with powerful AI will offer more accurate diagnostics and personalized treatment plans, improving healthcare outcomes and accessibility.
5. Environmental Monitoring and Conservation: AI-powered sensors and drones with enhanced efficiency will enable more extensive and precise environmental monitoring, aiding in conservation efforts and disaster management.
6. Enhanced User Experiences: Consumer electronics, including gaming consoles, smart TVs, and personal assistants, will offer more immersive and responsive experiences due to the increased capability of on-device AI.
7. Reduction in Data Center Energy Consumption: Data centers will see a significant decrease in energy consumption, even as the demand for AI services grows. This will help mitigate the environmental impact of expanding AI services and support more sustainable growth.
8. Economic Growth and Job Creation: The AI industry's growth, fueled by improved energy efficiency, will likely lead to the creation of new jobs and economic opportunities in AI development, deployment, and maintenance.
9. Innovation in Transportation: Autonomous vehicles will benefit from more efficient AI, leading to safer and more reliable self-driving cars, improved traffic management systems, and innovative forms of transportation like autonomous delivery drones.
The increase in inference energy efficiency from 5 TOPS/watt to 1000 TOPS/watt will be a transformative development, driving significant advancements in AI deployment across various sectors.
This leap will enable more complex, powerful, and sustainable AI applications, leading to smarter technologies that permeate every aspect of daily life.
By 2030, we can expect AI to be deeply integrated into our societal infrastructure, enhancing efficiency, sustainability, and overall quality of life.
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2030 Production: 100 million cobots
Rather than speculating on what cobot manufacturing capabilities might be needed, we have set a 2030 target of 100 million cobots per year and then extrapolate backwards to determine each year's expectations.
Since the number of 2025 cobots is currently estimated in the thousands, we assumed 10,000 units as the global starting production target.
2026 and 2027 are expected to grow at 10X each, taking production to 1 million/year in 2027.
2028 and 2029 are forecast at 5X and 2030 slows down to only 4X.
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AI/Compute
If we can realize the inference efficiency and production targets laid out here, then we anticipate the cobot fleet will be able to offer mobile and decentralized AI/compute capabilities on demand with these collective performance levels.
Note this capability is found within the "heads" of these cobots and offers universal access to super human intelligence to any community or person.
For comparison purposes, Tesla is estimated to have more than 1000 exaops by the end of 2024 for its AI training needs.
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Energy Use
We assume each cobot will require 2 Mwh to build.
We estimate the annual energy budget for 100% duty cycle at 5 Mwh, and that the AI chips will meet the inference energy efficiency targets.
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Taxes
In order to provide universal high income to Abundance Societies' citizens, we have implemented a flat tax of 20%, 15%, and 10% for cognitive and embodied services, and sales respectively.
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Economics
If the previous estimates of production, costs and pricing are achieved, we could build a cobot economy larger than the current global economy before this decade is out.
Roughly over $10 trillion would provided to local communities as part of universal income and other social programs.
The remaining $80 trillion need not be in fungible form.
Instead we will see it be integrated into the Abundance Society's autonomous infrastructure enabling it to create a hyperabundance of products and services on demand.
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