The four-axis ranking
We rank humanity’s most important problems on four quantifiable dimensions — quantity of humans affected, severity per capita, current solution quality, and addressable market size — and package each as a proposal in the spirit of Musk’s Hyperloop Alpha. This document is the proposal for scientific productivity. Every number below is sourced and tagged with confidence. Every ranking is a conjecture, open to refutation.
Quantity · humans affected
8.1B
medSeverity · WTP / wealth
10%
lowCurrent solutions
2.5 / 10
medMarket size · TAM
$2.5T
highWhat we are trying to solve
Bloom, Jones, Van Reenen and Webb showed research productivity across semiconductors, agriculture, and biomedicine has been falling for decades, we spend far more scientist-hours for each new idea. The meta-problem: every other quest on this list depends on scientific throughput. Focused Research Organizations, AI-assisted research, metascience reform, and funding structure changes all compound across every domain.
The gap between the world and the world that is physically possible
Today: Per-researcher productivity declining for 50+ years (BTGD). Most grant time spent on paperwork. Replication crisis unresolved. New PhDs needed to maintain Moore’s-law-style progress doubling every ~7 years.
Current solution quality is rated 2.5 / 10 (med confidence) — meaning there is substantial unclaimed ground between what exists and what is possible. estimated — per-researcher productivity declining ("Are Ideas Getting Harder to Find?", Bloom-Jones-Reenen 2020); replication crisis and grant-admin overhead unresolved.
Who is already working on this
9 entities are currently working on this problem across public markets, private companies, and research orgs. Each is evidence the market is real; none has obviously solved it.
Recursion Pharmaceuticals
public · USAAI-driven drug discovery. Industrializes biology through automated phenotypic screening at scale.
$2.4B
Ginkgo Bioworks
public · USACell programming platform. Biosecurity Division runs metagenomic surveillance and pathogen detection at scale.
$350M
OpenAI
private · USAOriginally nonprofit research lab, now capped-profit. Safety and superalignment teams alongside capabilities work.
$157.0B
Replit
private · USABrowser-based coding platform. 100M+ learners worldwide; AI agent for app-building compresses learn-to-ship cycle.
$3.0B
Insitro
private · USAML-driven drug discovery from Daphne Koller. Combines stem-cell biology with deep learning.
$2.5B
Hadrian
private · USAFully automated precision-parts factories for aerospace, defense, and infrastructure. Removing labor cost from the BOM.
undisclosed
Isomorphic Labs
private · UKAlphabet-spinoff applying AlphaFold-class ML to drug design. Deals with Novartis and Eli Lilly.
undisclosed
If we solve this, here is the world we get
After · 20 years
Scientific output per dollar 10× higher via automated experiments, AI literature review, FROs, lottery + fast-grants reform. Trustworthy literature. New disciplines created on AI timescales.
Requests for startups · 3 concrete companies to build
The autonomous lab
A PhD spends most of their hours pipetting, not thinking. Build the closed-loop robotic wet-lab that runs experiments 24/7 and proposes the next one.
- why now
- Lab robotics + foundation models crossed the line where the design-build-test-learn loop can close without a human in the inner loop.
- shape
- A self-driving lab as a service: researchers submit hypotheses, the system designs, executes, and iterates experiments autonomously, returning results and next-best experiments.
- success
- Experimental throughput per scientist rises an order of magnitude in adopting fields.
The replication layer
Most published findings are never independently checked, so the literature quietly rots. Build the company that makes high-value replication fast, funded, and a default step.
- why now
- Autonomous labs + AI literature analysis make systematic replication economically feasible for the first time.
- shape
- A replication-as-a-service org funded by journals, funders, and firms that need a result to be true before they bet on it; outputs a trust score the field actually uses.
- success
- High-stakes results carry a replication status, and the replication rate of new work rises measurably.
Fast grants as a standing product
It takes 6–12 months to fund a science idea. Fast Grants proved it can take 48 hours without quality loss. Build the always-on micro-grant rail for frontier research.
- why now
- The Fast Grants experiment produced the playbook; no one operationalized it as durable infrastructure.
- shape
- A funding platform with rolling micro-grants, expert triage, and radically compressed decision latency, capitalized by philanthropies and firms wanting optionality on frontier work.
- success
- Time from research idea to funded work drops from quarters to days as a standing option.
full rubric + framing on the Requests for Startups page.
What the market can pay
The world is already paying $2.5T per year against this problem (global R&D spend (UNESCO Institute for Statistics) — productivity uplift is leveraged across all of this; high confidence).
A successful solution does not need to capture more — it needs to redirect a meaningful slice of existing spend, plus the latent willingness-to-pay implied by the severity score above. The cost ceiling for a real solution is bounded by this number; everything cheaper is dominated, everything more expensive is a non-starter.
What could go wrong, and how we know we are not wrong
Section in progress
Failure modes, ethical considerations, and the conditions under which this whitepaper would be falsified are being authored as the weekly cadence ships. The Deutschian commitment: every claim above is a conjecture; we publish the conditions under which we would update. New whitepaper sections ship with each Monday newsletter drop. Subscribe to get the upgrade, or contribute on GitHub.
Who would back this
Capital allocators with a stated thesis or deployed portfolio in this domain. This is a starting list — Exa Websets enrichment will expand it to direct check-writers per company.
Emergent Ventures
Fast grants. High-variance, unconventional, talent-first.
Thiel Fellowship
$100k to stop out of school and build something important.
O'Shaughnessy Fellowships
$100k for creators across domains, art, science, history, startups.
Activate Fellowship
Two-year salary and national-lab access for scientists taking PhD research to market.
Speculative Technologies
ARPA-style coordinated research programs for big-if-true platform tech.
Deep Science Ventures
Outcome-first venture creation. Define the holy-grail outcome, then recruit founders.
Lux Capital
Counter-conventional science at the edges of physics and biology.
Fifty Years
Every portfolio company must solve a UN SDG. Profit with purpose, no exceptions.
What the thinkers say
“Progress depends on the unhindered creation of good explanations. Institutions that reward criticism outperform those that reward consensus.”
“First-principles reasoning beats reasoning by analogy. Most industries are throttled by process, not physics. The idiot-index gap is where opportunities live.”
“Research productivity has been falling for decades and we under-study the process of progress itself. Metascience and institution design matter as much as any single discovery.”
“We need a new philosophy of progress that makes technological and scientific advancement a positive cultural project again, not something to apologize for.”
“The Great Stagnation thesis: since roughly 1973 the low-hanging fruit of science and industry has been picked. Restoring the innovation engine is the meta-problem.”
Where this is wrong, tell us
Every number on this page carries a source and a confidence tag. Every section open to refutation. If a citation is wrong, a number is stale, or a conjecture is unfounded — file a correction.
corrections → use the feedback widget in the nav · open issue at github.com/adamtpang/optimism.fun