Introduction
AI is hurtling forward at break‑neck speed. Systems that once struggled to hold a coherent conversation now write production code, draft legislation, and pass professional exams. Yet today’s large language models (LLMs) still fall short of superintelligence in 2 areas: advice and innovation. In my view, two complementary breakthroughs could close that gap:
Extending feedback horizons in training so models optimize for long‑term outcomes (advice) rather than the short term “correct sounding” answers.
Teaching an AI “expert” to orchestrate other AIs for open ended (innovations) through iterative reasoning, self‑play, and tool use—scaling test‑time compute.
Below I unpack both paths, explore how they interact, and outline why they may arrive sooner than many expect.
Path 1: Fix the Premature Feedback Loops in RLHF
“Tell me whether quitting my job is wise.”
“Should I acquire this startup?”
Current instruction‑tuned LLMs respond instantly, but their answers are only as good as the short‑horizon reward signals they were trained on. In popular Reinforcement Learning from Human Feedback (RLHF) pipelines, humans award a score immediately after reading a single completion. That teaches the model to maximize surface plausibility—not real‑world success months later. BTW, I’ve spoken on this thoroughly here.
Why Short Horizons Fail
Good advice often sounds risky. Early‑stage startup pivots, contrarian investments, or breakthroughs in drug formulation can initially look crazy. RLHF punishes such ideas because labelers down‑rank anything that feels uncertain or unconventional.
Causality is delayed. Whether leaving a relationship was wise may take years to judge. The training loop never sees that signal, so the model optimizes linguistic polish instead of outcome quality.
Path 2: Meta‑AI—An Expert That Uses AIs
Big scientific or engineering leaps rarely emerge from a single flash of insight. They arise from an iterative loop of:
Context gathering → 2. Idea generation → 3. Critical debate → 4. Refinement & ranking.
The Human‑in‑the‑Loop Blueprint
Take the question: “How can we make this next-gen battery 10× more energy-dense?” A domain expert today might use ChatGPT to:
Query a model for materials-science papers, cycle-life studies, and teardown reports of leading lithium-ion and solid-state cells.
Prompt for analogous breakthroughs—e.g., silicon-doped anodes, sulfide electrolyte additives, or novel cathode coatings inspired by aerospace alloys.
Ask the model to generate fresh hypotheses on crystal-structure tuning, nanostructured current collectors, or self-healing separators.
Play devil’s advocate—have the model attack each idea on cost, manufacturability, dendrite growth risk, and thermal stability.
Iterate until a credible, evidence-backed shortlist survives.
This multi‑agent, multi‑prompt dance exploits what LLMs do best—search latent space—while the human curates context and sanity‑checks. Frameworks like AutoGPT and OpenAI’s o3 model are beginning this agentic work, now we need RL to clean things up.
From Blueprint to Autonomous Meta‑AI
To unlock superintelligence, we must internalize that process:
Meta‑controller model plans the search, chooses tools (web, vector DB, specialist models).
Worker models execute subtasks—retrieval, simulation, critique.
Self‑reflection loops (e.g., CRITIC, Self‑Verify) let the system inspect and repair its own outputs (CRITIC, 2024)(openreview.net).
Reinforcement learning on meta‑performance—reward the controller for improved downstream results per unit cost, not for writing pretty prose.
Over time, the meta‑AI learns to allocate compute adaptively: trivial questions get a one‑shot answer, grand‑challenge problems trigger hours of autonomous literature review, brainstorming, and simulation.
Why Test‑Time Compute Beats Parameter Scale
Empirically, depth of reasoning often matters more than raw model size once a capability plateau is reached. The strawberry‑nicknamed model OpenAI unveiled in 2024 solved Olympiad‑level math by deliberating internally before emitting an answer (Vox, 2024) (vox.com). Giving a 300‑billion‑parameter model 10× the inference budget can outperform a 1‑trillion‑parameter model forced to think just once.
Convergence: When the Two Paths Meet
Long‑horizon reward (Path 1) gives models a compass that points toward strategies that succeed months later. Meta‑AI orchestration (Path 2) supplies the horsepower to explore the solution space thoroughly. Together they form a virtuous circle:
Path 2’s meta‑AI generates candidate plans and simulates outcomes.
Those simulated trajectories feed into Path 1’s long‑horizon reward model.
Improved policy weights make the meta‑AI’s explorations more targeted—reducing compute cost and error.
Within such a self‑improving feedback loop, capabilities could compound rapidly—crossing the superintelligence threshold with far less training‑time compute than naïve scaling laws predict.
Takeaways
Don’t just scale parameters—scale horizons and interaction depth.
Delayed feedback aligns advice with reality, not rhetoric.
Meta‑AI transforms models into collaborative colleagues capable of multi‑hour research sprints.
The intersection of these two paths is the fastest on‑ramp to superintelligence—and we’re already laying the pavement.