Will tailcalled think that the Shard Theory alignment research program has achieved something important by October 20th, 2026?
One-OffAI1moManifold MarketsNo KYCVerified resolution dataFairly calibratedCurrent community forecastNo 88.2%
Forecasters
25
Question type
binary
Methodology
Play-money forecasting platform
Source type
Forecast
Market data
Updated 2 hours ago
StaleOct 20, 22, 1:04 PM UTCOct 20, 26, 9:59 PM UTC
Trends
Rules
The Shard Theory research program by Team Shard is based on the idea that we should directly study the what types of cognition/circuitry are encouraged by reinforcement learning, and reason mechanistically about how reward leads to them, rather than the classic Outer Alignment/Inner Alignment factorization which aims to create a reward function that matches human values and somehow give an AI a goal of maximizing that reward.
- A hope is that understanding how reward maps to behavior is more tractable than Outer Alignment/Inner Alignment, and that we therefore might be able to solve alignment without solving Outer Alignment/Inner Alignment.
- In 4 years, I will evaluate Shard Theory and decide whether there have been any important good results since today.
- I will probably ask some of the alignment researchers I most respect (such as John Wentworth or Steven Byrnes) for advice about the assessment, unless it is dead-obvious.
- About me: I have been following AI and alignment research on and off for years, and have a somewhat reasonable mathematical background to evaluate it.
- I tend to have an informal idea of the viability of various alignment proposals, though it's quite possible that idea might be wrong.
Open markets
AI
KALSHITop AI model in September?
- claude-fable-5.1-max-12.0pt72%
- claude-opus-5-high+2.0pt6%
- claude-opus-4-6-thinking4%
+3 more outcomes
- Total Vol
- $157.2K
- 24h Vol
- $119.4K
POLYMARKETWill CMI declare a Millennium Prize Problem solved by which date?
- 2028+10.0pt60%
- 2027+2.0pt31.4%
- 2026+2.0pt8.6%
POLYMARKETAnthropic IPO by which date?
- December 31,-1.0pt90%
- December 15,+3.5pt90%
- November 30,85%
+7 more outcomes
- Total Vol
KALSHIBest AI at the end of 2026?
- Claude-0.7pt61.4%
- ChatGPT-2.7pt22.8%
- Grok-0.1pt7.9%
+5 more outcomes
- Total Vol
- $10.6M
KALSHIBest AI in Sep 2026?
- Claude+3.0pt81.3%
- ChatGPT-4.0pt11.2%
- Grok2.8%
+4 more outcomes
- Total Vol
- $377.7K
- 24h Vol
POLYMARKETOpenAI’s Astra Model: Text Arena Debut?
- 1480+-2.5pt66%
- 1490+-9.0pt51%
- 1500+-3.5pt23%
+2 more outcomes
- Largest IPO by market cap in 2026?
- OpenAI IPO Closing Market Cap
- When will Anthropic officially announce an IPO?
- Anthropic IPO Closing Market Cap
- When will OpenAI achieve AGI?
- Will Anthropic’s valuation hit __ by December 31?
- Will OpenAI's valuation hit __ by December 31?
- Next Google Gemini Pro Model released by which date?
- Best Chinese AI Company end of September?
- What will OpenAI's IPO valuation be?
Frequently asked questions
- What are the current odds for Will tailcalled think that the Shard Theory alignment research program has achieved something important by October 20th, 2026??
- Manifold Markets prices No at 88% as of September 8, 2026.
- When does Will tailcalled think that the Shard Theory alignment research program has achieved something important by October 20th, 2026? resolve?
- Will tailcalled think that the Shard Theory alignment research program has achieved something important by October 20th, 2026? is scheduled to resolve on October 20, 2026.
- How is Will tailcalled think that the Shard Theory alignment research program has achieved something important by October 20th, 2026? settled?
- Manifold Markets settles this market as follows: The Shard Theory research program by Team Shard is based on the idea that we should directly study the what types of cognition/circuitry are encouraged by reinforcement learning, and reason mechanistically about how reward leads to them, rather than the classic Outer Alignment/Inner Alignment factorization which aims to create a reward function that matches human values and somehow give an AI a goal of maximizing that reward.