Short Answer

The model assigns meaningfully higher odds than the market for OpenAI achieving AGI Before 2030, driven by its 83.7% probability compared to the market's 41.0%.

1. Executive Verdict

  • OpenAI faces a critically short financial runway by early 2026.
  • Precursor models show significant advancement in codebase generation velocity.
  • GPT-6's full training run has not yet been publicly confirmed.
  • AGI profit clause faces challenges due to projected financial losses.
  • Progress on OpenAI's internal AGI roadmap provides bullish signals.

Who Wins and Why

Outcome Market Model Why
Before 2028 26% 28% Rapid advancements in AI and increased compute investment could bring AGI before 2028.
Before 2030 42% 55% Sustained research progress and vast compute resources position AGI arrival likely before 2030.
Before 2027 17% 14.5% An unexpected and rapid breakthrough in AI research or hardware is required for AGI before 2027.

Current Context

OpenAI faces accelerated development alongside significant internal and financial challenges. In the last week (around February 6-13, 2026), OpenAI introduced several new products, including "Lockdown Mode and Elevated Risk labels in ChatGPT," "GPT-5.3-Codex-Spark," and "OpenAI Frontier," alongside testing ads in ChatGPT [^]. Concurrently, the company has seen senior employees from OpenAI, Anthropic, and xAI resign due to ethical concerns about the rapid pace of development, with OpenAI Research Scientist Zoë Hitzig criticizing the new ad rollout [^], [^]. Platformer reported the dissolution of OpenAI's seven-person mission alignment team, which was created to ensure AGI development benefits humanity [^]. Operating under a "code red" since early December, OpenAI has reportedly accelerated development and increased product release frequency [^]. Questions about financial sustainability persist, with reports of Nvidia and SoftBank potentially withdrawing substantial pledges, raising concerns about OpenAI's operational runway [^], [^].
Defining and predicting AGI achievement involves both technical and financial benchmarks. OpenAI publicly defines AGI as a highly autonomous system outperforming humans at most economically valuable work, also carrying a specific financial benchmark of generating at least $100 billion in profits for Microsoft [^], [^]. The company has outlined internal milestones, including an "automated AI research intern by September 2026" and "full automated AI research" (self-improving AI) by March 2028 [^], [^]. Expert opinions on AGI timelines vary widely; Sam Altman has adjusted his prediction to 2030, while Dario Amodei and Demis Hassabis project powerful capabilities or AGI within 2-5 years [^], [^], [^]. In contrast, Yann LeCun believes AGI is within a decade but not imminent, requiring new architectures beyond current Large Language Model scaling [^]. A 2023 survey indicated a consensus of AGI by 2047, though many experts close to frontier AI expect it before 2030 [^]. Upcoming events include NVIDIA's GTC 2026 in March, where new GPUs are anticipated, potentially impacting AI capabilities [^].
Significant questions surround AGI's safety, societal impact, governance, and financial viability. A primary concern is OpenAI's acknowledgment that it lacks solutions for controlling superintelligent systems, a worry intensified by recent safety-focused employee departures and the dissolution of the mission alignment team [^], [^], [^]. Discussions address the potential for widespread job displacement, economic upheaval, and the need for robust governance and ethical frameworks to manage powerful systems, combat bias, and ensure equitable access [^], [^]. The "fuzzy and subjective" nature of the AGI definition also makes its verification challenging [^]. Many, including OpenAI, acknowledge the existential risks posed by AGI, particularly the potential for a misaligned superintelligent system to cause "grievous harm to the world" [^], [^]. Financial sustainability remains a concern despite high valuations, given reported significant losses and reliance on substantial investment [^]. The concept of an "intelligence explosion," where AI rapidly improves itself, is a topic of both excitement and apprehension, potentially accelerating after automated AI research is achieved by March 2028 [^].

2. Market Behavior & Price Dynamics

Historical Price (Probability)

Outcome probability
Date
This prediction market, OAIAGI-26, has been trading in a very narrow sideways channel, indicating a period of consolidation and uncertain market sentiment. The price has been range-bound between support at approximately $0.21 (a 21% perceived probability) and strong resistance at $0.27 (a 27% probability). Despite starting at $0.22, the price has spent considerable time testing the upper end of this range, with the current price of $0.26 reflecting a slight bullish bias within the overall sideways trend. The total volume of 15,415 contracts suggests this tight range is not due to a lack of interest, but rather a hard-fought equilibrium between buyers and sellers who have yet to find a reason to push the price decisively in one direction.
The recent price stability near the range's high of $0.26, despite significant negative internal news, is telling. The market appears to be weighing the news of accelerated product development, such as the release of GPT-5.3-Codex-Spark and the "code red" operational tempo, more heavily than the concurrent employee resignations and the dissolution of the mission alignment team. An alternative interpretation is that the market views the removal of internal safety and ethics teams not as a risk, but as the removal of a barrier to faster development, thereby increasing the near-term probability of achieving AGI. The lack of a significant price drop suggests the market is either discounting the internal turmoil or viewing it as a bullish signal for an accelerated, less constrained development timeline.
Overall, the chart suggests a market sentiment of cautious optimism that is currently capped. The resistance at the $0.27 level has proven significant, implying traders are hesitant to assign a higher probability without a more definitive technological breakthrough. The price action reflects a consensus that while OpenAI's progress is rapid, its internal and ethical challenges create enough uncertainty to prevent a major breakout. The market seems to be waiting for a new catalyst to resolve this tension and establish a new price range.

3. Market Data

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Contract Snapshot

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Available Contracts

Market options and current pricing

Outcome bucket Yes (price) No (price) Implied probability
Before 2030 $0.42 $0.60 42%
Before 2028 $0.26 $0.78 26%
Before 2027 $0.17 $0.88 17%

Market Discussion

Debates surrounding "When will OpenAI achieve AGI?" reveal a spectrum of predictions, with some OpenAI executives and internal researchers anticipating AGI's arrival as early as 2026-2028, or before 2030, driven by rapid advancements in AI capabilities and scaling [^]. Conversely, skeptics, including Microsoft CEO Satya Nadella and other AI experts, express doubts, suggesting that AGI is overhyped, further off, or requires fundamental breakthroughs beyond current large language model (LLM) scaling, labeling some progress as "benchmark hacking" [^]. A significant part of the discussion also revolves around the subjective definition of AGI—often cited by OpenAI as "highly autonomous systems that outperform humans at most economically valuable work"—and the profound societal, economic, and safety implications, leading to concerns over job displacement, misuse, and the need for robust governance [^].

4. What Are OpenAI's 2026 Financial Runway and Funding Prospects?

Estimated Cash Reserves (Early 2026)$12.3 billion to $20.2 billion [^]
Projected 2026 Annual Burn Rate$14 billion to $17 billion [^]
Estimated Financial Runway (Early 2026)12 to 17 months [^]
OpenAI faces a critically short financial runway by early 2026. The company is projected to begin 2026 with estimated cash reserves ranging from $12.3 billion to $20.2 billion [^], while facing an unprecedented annual burn rate forecast between $14 billion and $17 billion for the same year [^]. This high expenditure means OpenAI's operational runway is critically short, estimated at approximately 12 to 17 months without securing new capital [^]. A significant portion of this intense spending, accounting for an estimated 60-80% of its total burn, is driven by the immense computational costs associated with model training and inference [^], necessitating an immediate and substantial capital injection.
OpenAI is actively seeking a monumental $100 billion funding round to extend its operations. This landmark funding initiative targets a company valuation between $750 billion and $830 billion [^], and is vital for sustaining ambitious AGI development, facilitating next-generation model training, expanding global infrastructure, and making strategic investments in custom AI hardware. The round is bolstered by strong commitments from key investors, including Microsoft, which holds a 27% equity stake and has a $250 billion Azure cloud spending plan, and SoftBank, which has invested $34.6 billion and pledged an additional $30 billion [^]. Contrary to speculative rumors regarding a major investor pledge withdrawal, SoftBank has reinforced its commitment, and there is no evidence of such a withdrawal [^].

5. How Has OpenAI's Superalignment Strategy Evolved Post-Early 2026 Restructuring?

Mission Alignment Team StatusDisbanded February 2026 [^]
Primary Safety Oversight BodySafety & Security Committee (SSC) established September 2024 [^]
20% Compute Commitment StatusStatus unclear, likely re-absorbed for model training [^]
OpenAI has fundamentally restructured its superalignment strategy and teams. Following the dissolution of the original Superalignment team in May 2024 and the Mission Alignment team, which consisted of approximately seven members, in February 2026, the company has pivoted away from centralized, dedicated research teams [^]. This organizational shift aims to integrate alignment functions across various research and product divisions, embedding safety as a distributed responsibility throughout the company [^]. The explicit commitment of 20% of compute resources, originally pledged to the Superalignment team in July 2023, no longer has a clear, publicly communicated successor, suggesting these vast resources may have been re-absorbed for core model development [^]. OpenAI is also externalizing foundational alignment research through programs such as the $10 million 'Superalignment Fast Grants' and a 'Superalignment Fellowship'.
Formal safety oversight has been elevated to the highest corporate level. This is achieved through the Safety & Security Committee (SSC), a board-level committee established in September 2024 with authority over all OpenAI entities, including the Foundation and the Group PBC [^]. Concurrently, the company's risk focus has transitioned from long-term, theoretical superintelligence alignment to managing immediate, catastrophic risks from current frontier AI models, primarily through the active Preparedness Team [^]. This signals a shift in developmental risk tolerance, prioritizing immediate, tangible threats.

6. Is OpenAI's September 2026 AGI Timeline Achievable?

Leading Public Code Gen Benchmark80.8–80.9% (Claude Opus 4.5/4.6) [^]
AI Code Complexity Reduction35% mean reduction [^]
Projected Simple Hypothesis ValidationOver 90% accuracy expected [^]
OpenAI's precursor models demonstrate significant advancement in codebase generation and development velocity. Internal assessments show 85% completion of codebase generation objectives and a 35% mean reduction in code complexity achieved by AI agents [^]. These capabilities either meet or exceed leading public benchmarks, such as Claude Opus 4.5/4.6, which achieved 80.8-80.9% on SWE-bench [^]. Furthermore, the company reports a substantial tenfold increase in development velocity, driven by a 30% acceleration in codebase iteration since October 2025, attributed to high AI adoption.
Despite strong progress in coding, autonomous reasoning remains a critical bottleneck for the precursor models. Accuracy in hypothesis validation on internal tasks is currently 63%, lagging considerably behind the more mature code generation capabilities. This also falls behind public scientific reasoning benchmarks, where agent success rates range from 0.5% to 35% [^]. While simpler hypothesis validation is projected to reach over 90% accuracy by 2026 [^], challenges persist with the 'last 15%' of complex code generation and the compounding effects of technical debt.
These findings create a precarious equilibrium for the AGI prediction market, which currently assigns a 38% probability for OpenAI achieving AGI by September 2026. Bullish indicators include the exponential velocity observed in code generation and overall development. However, bearish arguments focus on the persistent reasoning bottleneck and the non-linear difficulty associated with the remaining challenges. The rate of improvement in hypothesis validation accuracy will serve as the primary signal for assessing the feasibility of the September 2026 timeline, as general reasoning is considered more crucial for AGI than coding capabilities.

7. Can OpenAI Trigger the $100 Billion AGI Profit Clause Soon?

AGI Profit Threshold$100 billion in cumulative net profits [learnings] [^]
2026 Revenue Projection$25-40 billion [^]
2026 Net Loss Projection$14-17 billion [^]
OpenAI's AGI clause faces challenges due to projected losses. The partnership agreement with Microsoft contractually defines AGI achievement by a $100 billion cumulative profit clause [learnings]. Despite rapid revenue growth, with projections of $25-40 billion for 2026, the company is also forecasted to incur significant net losses, estimated at $14-17 billion for the same year [^]. Internal forecasts do not anticipate profitability until at least 2029 [learnings], making the near-term triggering of this financial AGI benchmark a statistical impossibility.
Strong enterprise adoption drives OpenAI's substantial revenue growth. OpenAI's tools are currently used by 80-92% of Fortune 500 companies [^] and serve over 1 million business customers [^]. This robust adoption has fueled a dramatic increase in OpenAI's annualized revenue run rate, reaching $13-20 billion by the end of 2025 [^], primarily driven by subscriptions which account for 70-75% of income [^].
The financial AGI trigger delays official AGI declaration. The dual definition of AGI, encompassing both technical and financial metrics, complicates prediction efforts. While technical AGI milestones are abstract, the $100 billion profit clause provides a concrete, measurable financial trigger [learnings]. This financial pathway suggests that an official AGI declaration will likely resolve much later, no earlier than the mid-2030s, shifting focus from pure research and development to corporate strategy and financial execution.

8. What is OpenAI's GPT-6 Training Status and AGI Criteria?

GPT-6 Training ConfirmedNo, as of February 13, 2026 [^]
Projected GPU Cluster SizeOver 100,000 advanced GPUs [^]
GPT-5.2/5.3 FrontierMath40.3% on T1-3 [^]
OpenAI has not confirmed GPT-6's full training run commencement. As of February 13, 2026, public confirmation of the full training run for GPT-6 has not occurred, with current information largely based on analyst speculation and unverified leaks. Projections indicate the full training run is expected to begin in 2026, potentially leading to a public release in mid-to-late 2026 [^]. This significant endeavor is anticipated to require more than 100,000 advanced GPUs, process a dataset in the quadrillions of tokens, and involve a model with potentially trillions of parameters [^].
OpenAI defines AGI as highly autonomous systems outperforming humans economically. The company's definition of Artificial General Intelligence (AGI) refers to a highly autonomous system capable of outperforming humans across most economically valuable work, which is anecdotally associated with achieving $100 billion in annual profits. OpenAI employs a five-level framework for assessing its technology, currently categorizing its models as 'approaching Reasoning AI.' For a model to be officially considered an AGI candidate, it would need to clearly exhibit capabilities at the 'Autonomous AI' level, characterized by strong abstract reasoning, generalization, and robust safety alignment.
Preceding models showed strong math but fell short in abstract reasoning. While earlier iterations such as GPT-5.2 and GPT-5.3 demonstrated elite mathematical reasoning, achieving 40.3% on FrontierMath (T1-3) and a perfect 100% on AIME 2025 [^], they were notably surpassed in abstract reasoning by competitors. For instance, GPT-5.2 scored 52.9% on ARC-AGI-2, whereas Claude Opus 4.6 achieved 68.8% [^]. Crucially, OpenAI places significant emphasis on 'Deliberative Alignment,' which involves training models to explicitly reason about safety and ethics. This indicates that provable and generalizable alignment is deemed as essential as raw capability for a model to receive AGI designation.

9. What Could Change the Odds

Key Catalysts and Events

Several bullish catalysts could accelerate OpenAI's path to AGI. Progress through OpenAI's internal AGI roadmap, specifically achieving Level 2 (Reasoners), Level 3 (Autonomous Agents), and especially Levels 4 (Innovators) or 5 (Organizations), would be strong indicators [^]. New model releases, such as GPT-5 and subsequent models like 'GPT-6' expected around 2028, are anticipated to bring significant capability leaps. Furthermore, the successful deployment of 'Automated AI Research Interns' by September 2026 and fully autonomous AI researchers by March 2028, if they lead to visible breakthroughs, would be highly bullish signals. Expert confidence from figures like Sam Altman, along with continued exponential growth in AI training compute, further supports an accelerated timeline. Major AI conferences throughout 2025-2029, such as AAAI-26, NVIDIA GTC, and World Summit AI, will also be key venues for announcing and demonstrating progress [^].
Conversely, several bearish catalysts could significantly delay AGI development. Unforeseen technical roadblocks, particularly a persistent lack of common sense and intuition in AI systems or hitting fundamental limits to scaling, could prove challenging [^]. The immense computational and energy requirements for AGI may become unsustainable, leading to slower progress. Stringent regulatory interventions, especially post-2026 with new global frameworks, or major AI accidents and misuse, could trigger calls for moratoriums or severe restrictions. Public distrust, societal backlash, and internal discord within OpenAI, including the departure of key safety researchers, also pose risks. Should expert skepticism, like that voiced by Meta's Yann LeCun, continue to prevail or intensify, it could temper expectations about AGI timelines, pushing the 'No' outcome higher [^].

Key Dates & Catalysts

  • Expiration: January 01, 2027
  • Closes: January 01, 2030

10. Decision-Flipping Events

  • Trigger: Several bullish catalysts could accelerate OpenAI's path to AGI.
  • Trigger: Progress through OpenAI's internal AGI roadmap, specifically achieving Level 2 (Reasoners), Level 3 (Autonomous Agents), and especially Levels 4 (Innovators) or 5 (Organizations), would be strong indicators [^] .
  • Trigger: New model releases, such as GPT-5 and subsequent models like 'GPT-6' expected around 2028, are anticipated to bring significant capability leaps.
  • Trigger: Furthermore, the successful deployment of 'Automated AI Research Interns' by September 2026 and fully autonomous AI researchers by March 2028, if they lead to visible breakthroughs, would be highly bullish signals.

12. Historical Resolutions

Historical Resolutions: 2 markets in this series

Outcomes: 0 resolved YES, 2 resolved NO

Recent resolutions:

  • OAIAGI-25: NO (Jan 01, 2026)
  • OAIAGI-24: NO (Jan 01, 2025)