When AI compresses the information edge and hands everyone roughly the same high-quality financial insights, pure “knowing more” stops being a reliable source of alpha.
The remaining edge shifts to the parts that are still scarce.
Judgment under uncertainty,
creative synthesis,
structural positioning,
behavioral discipline, and the
ability to act when others cannot.
The Ken Griffin / Leopold Aschenbrenner episode illustrates this cleanly. The underlying AI-infrastructure names (Nebius, SanDisk, Bloom Energy, IREN, CoreWeave, etc.) were not obscure secrets. The thesis around them was widely discussed.
What created the multi-hundred-million-dollar one-day swing was not a superior fundamental insight. It was Extreme leverage meeting a sudden liquidity shock and margin calls.
Forced selling at the wrong moment (classic “storage business” turning into a fire sale).
A counterparty with the balance sheet, experience, and willingness to step in precisely when panic peaks.
Ken Griffin on rule that he used with Leopold deal: “humans will exhibit the same behavior in a bear market – they’ll tend to panic out of very good investments at the very wrong moment”
this is him laying out the playbook he ran on Leopold – and how leverage nearly killed… https://t.co/WyBAMvd9tG pic.twitter.com/30g5NR8Qpc
— bodila (@51bodila) July 31, 2026
Where alpha migrates once the insight layer is commoditized
Creative recombination and second-order thinking. AI can surface correlations, forecasts, and narratives. Humans (or tightly coupled human-AI teams) still decide which signals to weight, which to ignore, how they interact under regime shifts, and what the market has already priced in versus what it has not.
Capital structure and risk architecture. Leverage, liquidity buffers, prime-broker relationships, and the ability to absorb forced flows are not pure information problems. They are design and discipline problems.
Behavioral and institutional edges. Knowing that others will be margin-called, face redemptions, or hit risk limits is different from knowing the fair value of a stock. Timing and sizing around those constraints still require judgment.
Execution, implementation, and speed of adaptation. Even identical insights produce different P&L depending on how positions are built, hedged, and exited—especially in crowded, high-vol names.
Proprietary data, unique framing, or domain creativity. The edge often sits in the questions asked, the data sources combined, or the narrative constructed, not just the answers the model returns.
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
Known for identifying cutting edge technologies, he is currently a Co-Founder of a startup and fundraiser for high potential early-stage companies. He is the Head of Research for Allocations for deep technology investments and an Angel Investor at Space Angels.
A frequent speaker at corporations, he has been a TEDx speaker, a Singularity University speaker and guest at numerous interviews for radio and podcasts. He is open to public speaking and advising engagements.
