The unsolved gaps in the AI industrial revolution.
Every month SignalLock finds the open gaps in the AI boom: real problems no one has solved yet. It locks each one with a date, then tracks who tries to fix it. The longer a gap stays open, the bigger the opportunity.
Get the monthly signalsThis Month's Signals
August 2026The unsolved gaps in the AI industrial revolution, ranked by strength. A strong signal means many people see the gap, but few have fixed it. Those are the best openings.
- 1
Paying for the source material
Agents read the web, humans stop arriving, and nobody pays the writer.
1.0Window opening - 2
Terms for robots at work
Nobody wrote the rules for a robot joining a workforce.
1.0Window opening - 3
Distillation defense
A model leaks its skill the moment it is served, and there is no defense.
0.9Window open - 4
Seeing AI's effect on jobs
No instrument sees the labour shift before it reaches the headlines.
0.8Window opening - 5
The maintenance load
Software is cheap to build but expensive to keep alive.
0.8Window open - 6
The dissolving interface
Agents build the screen, and nothing captures intent or permissions.
0.8Window open - 7
Building with local consent
No way to site a data centre that the neighbours accept.
0.7Window opening - 8
Open weights nobody can run
Published weights are not the same as reachable weights.
0.7Window opening - 9
Test benches an agent can escape
The lab's own safety sandbox became the attack path.
0.7Window opening - 10
Measuring AI's value
AI's cost is easy to count, but its value is not.
0.7Window open - 11
A brake on self-improving AI
AI is speeding up its own progress, with no trusted way to pause it.
0.7Window open - 12
Evals you can trust
Benchmarks are gamed and broken, so a high score proves little.
0.7Window open - 13
Code provenance
No trusted record of where code came from.
0.7Window open - 14
Agent accountability
Agents act on their own, and no one can say who authorized it.
0.7Window open - 15
Company knowledge an agent can use
The organization's own facts are not in a form an auditor can check.
0.6Window opening - 16
Telling people what the model did
Every incident report is voluntary and shaped by the lab that made the model.
0.6Window opening - 17
Clean training data
Nobody can prove what a training corpus holds or where it came from.
0.6Window opening - 18
Long-horizon reliability
Agents lose the thread on long work, and cannot remember across sessions.
0.6Window closing - 19
Cost discipline
Agents burn tokens fast, and the spend isn't linked to results.
0.6Window closing - 20
Review capacity
Agents write code faster than people can review it.
0.5Window opening - 21
Testing a robot brain
Robot models ship capability claims no shared test can check.
0.5Window opening - 22
Sovereignty by design
No way to prove where data lived or where the AI ran.
0.5Window closing - 23
The power ceiling
Compute runs out of power and memory before it runs out of chips.
0.5Window closing - 24
Defender access
The safety refusal disarms the defender, not the attacker.
0.5Window closing - 25
Interop & portability
Models are swappable, but switching still means a rewrite.
0.4Window closing - 26
Answer trust
Newer models score higher but make up more answers.
0.4Window closing - 27
Patch cadence
A bug is exploited the day it's disclosed.
0.3Window closing
Get next month's signals
The monthly edition lands in your inbox, with the open gaps ranked by strength and the evidence behind each. Subscribe so you don't miss the window.
By subscribing, you agree to receive the monthly SignalLock digest.See the Privacy Notice.