A concise summary and critique of the SolveEverything blueprint by Dr. Alexander D. Wissner-Gross and Dr. Pe ter H. Diamandis, a vision of how AI and industrial-scale systems could systematically “solve” entire domains of human challenge by around 2035.
The essay frames a future where artificial intelligence and automated systems transition human civilization from scarcity to abundance:
It describes an Industrial Intelligence Stack that turns complex domains into “compute-bound” problems, meaning they’re solved not through individual genius but through systematic application of computation and data.
This stack includes shared benchmarks, rigorous testing, compute escrow (reserved compute credits that pay out only when a system meets defined outcomes), and public targeting authorities that set transparent goals.
In this future, breakthroughs in medicine, energy, manufacturing, and more become routine: organs are printed on demand, hunger is logistical rather than material, fusion energy becomes practical, and climate hazards are managed with planetary digital twins.
Social contracts and economic structures shift to universal access to “capacity” (compute and AI utilities) instead of income. Human roles evolve toward steering meaning and purpose, with scarcity replaced by a background “quiet hum” of solved infrastructure.
Critique
The piece is a grand techno-utopian manifesto rooted in belief in progress through industrialized automation.
It’s intellectually daring and framed with historical analogies (e.g., comparing past revolutions in science, industry, and information to the coming “intelligence revolution”).
But it has several weaknesses:
1. Overly deterministic timeline: The blueprint assumes that exponential progress will continue smoothly and that full domain-scale solutions by 2035 are plausible. Innovation historically has nonlinear bursts and setbacks, and systemic risks (technical, ethical, economic) could slow or reroute progress.
2. Simplistic view of societal change: The model treats social institutions as easily redrawn by engineering, yet political, cultural, and economic frictions often resist even modest reforms. The narrative assumes pre-existing consensus on values and governance, which is rarely the case at global scale.
3. Underplays risk and governance complexity: While the essay mentions mechanisms like automated safety brakes and test harnesses, it largely glosses over the ethical risks of powerful AI deployment, uneven geopolitical adoption, misuse by bad actors, and economic displacement, all of which could destabilize rather than smooth the path to “abundance.”
4. Human agency and meaning: The vision shifts human roles to “conductors of intelligence” and “creators of meaning,” which sounds poetic but may underestimate the struggle real people face in adapting to rapid economic and social transformation.
5. Absence of alternative futures: The focus on a single, optimistic trajectory may encourage tunnel vision; the real future may include hybrid scenarios with partial successes and failures, not a clean march to universal solving.
Overall Takeaway
SolveEverything.org is a bold speculative blueprint rather than a grounded roadmap. It’s valuable as a high-level provocation about where technology might take us, and as a conversation starter about how society could align incentives for shared progress. But it leans heavily toward optimism and industrial teleology, requiring careful examination of social, ethical, and political factors that don’t neatly reduce to compute curves or automated pipelines.
Dr Bob, Brain Mechanic 