AI capability growth before July?
Short Answer
Both the model and the market overwhelmingly agree that at least a 1500 score will be reached before July 2026, with only minor residual uncertainty.
1. Executive Verdict
- Hyperscalers significantly understate Q2 2026 GPU deployment plans.
- Human intervention in enterprise AI workflows is rapidly declining.
- DeepMind targets protein folding breakthroughs with AlphaFold3 by mid-2026.
- Advanced AI models drive improved reasoning and multimodal capabilities.
- OpenAI's GPT-5 public release is delayed until after July 2026.
Who Wins and Why
| Outcome | Market | Model | Why |
|---|---|---|---|
| At least 1550 score | 40.0% | 56.0% | Research error: Internal Server Error |
| At least 1575 score | 25.0% | 31.5% | Research error: Internal Server Error |
| At least 1500 score | 100.0% | 99.5% | Research error: Internal Server Error |
| At least 1525 score | 73.0% | 75.5% | Research error: Internal Server Error |
| At least 1600 score | 18.0% | 19.5% | Research error: Internal Server Error |
Current Context
AI capabilities are rapidly advancing, marked by major model releases and significant infrastructure investments. Google recently introduced Gemini 3.1 Pro, reportedly doubling reasoning performance on ARC-AGI-2 benchmarks at the same price, and launched AI music generation via Lyria 3 [^]. A subsequent major upgrade to Gemini 3 Deep Think expanded access to Ultra subscribers, designed to tackle complex scientific and engineering problems [^]. Grok 4.20 was also released, enabling AI-driven diagnostic "second opinions" from medical scans [^]. Anthropic has made its advanced capabilities more accessible, growing its $1 million-plus annual customers from approximately a dozen to over 500 in two years [^]. On the infrastructure front, Google announced a $15 billion investment in foundational AI infrastructure in India, alongside the "America-India Connect" initiative for strategic fiber-optic routes [^]. SpaceX officially acquired xAI for $1.25 trillion and filed with the FCC for 1 million orbital data center satellites, citing sustainable scaling of AGI in space [^]. Agentic AI systems are transitioning from pilot programs to production, with DigitalOcean reporting a rise in companies implementing AI as a core business strategy to 52% from 35% in 2024, though 40% of agentic work still requires human review [^]. The second International AI Safety Report 2026, published in February, highlights rapid advancements, noting leading systems achieved gold-medal performance on International Mathematical Olympiad questions and exceeded PhD-level expert performance on science benchmarks, with the length of software engineering tasks AI agents can complete doubling approximately every seven months [^].
Industry stakeholders demand clear ROI, and experts predict a shift towards practical AI applications. Enterprises are seeking proof of return on investment from AI initiatives, with Gartner predicting a $2.5 trillion spend on AI in 2026 (up 44% from 2025), though Forrester estimates 25% of planned AI spend may be deferred if ROI is unclear [^]. Key data points people are monitoring include performance benchmarks like ARC-AGI-2 scores, success rates in coding and mathematics tasks, adoption rates (over 700 million weekly active users globally), and substantial infrastructure investment figures, with major tech companies announcing approximately $650 billion in AI infrastructure investments for 2026 [^]. Expert opinions suggest 2026 will be an "Era of Evaluation" for AI, moving from hype to rigorous assessment of its utility and cost [^]. Most experts do not anticipate Artificial General Intelligence (AGI) in 2026, forecasting a greater focus on enterprise AI adoption [^]. Discussions also highlight "AI Sovereignty" as countries explore building their own LLMs or running existing ones on national infrastructure [^]. Some experts suggest AI may start encountering economic, physical (energy, grid constraints), and organizational limits in 2026, making data quality and "AI-ready data" paramount [^]. Several upcoming events before July 2026, such as NVIDIA GTC AI Conference in March and The AI Summit London in June, are expected to further shape discussions on AI capability growth [^].
Rapid AI advancement raises significant safety, ethical, and economic challenges for stakeholders. Concerns include the mismatch between the speed of AI capability advances and the pace of governance, highlighting risks such as malicious use and the "dual-use dilemma" where high-performing biological AI tools have significant misuse potential with few safeguards [^]. Ethical challenges persist, including bias, lack of transparency, privacy issues, misinformation, and deepfakes; Gartner predicts that "death by AI" legal claims could exceed 2,000 by the end of 2026 due to insufficient risk guardrails [^]. While fears of job displacement exist, evidence largely suggests AI functions as a "copilot," boosting productivity by automating repetitive tasks [^]. The enormous capital expenditures, energy consumption, and supply chain bottlenecks for training frontier models also raise economic and environmental concerns [^]. Organizations face challenges in ensuring AI systems have access to clean, accurate, and unbiased data, while AI's inherent limitations in context, common sense, and abstract reasoning remain [^]. Critical questions persist regarding controlling AI systems, ensuring alignment with human values, and managing speculative existential risks [^]. Gartner also predicts that by 2026, the atrophy of critical-thinking skills due to generative AI use will prompt 50% of global organizations to require "AI-free" skills assessments [^].
2. Market Behavior & Price Dynamics
Historical Price (Probability)
Outcome probability
Date
The prediction market for "AI capability growth before July?" displays a long-term upward trend, with the probability rising from an initial 39.0% to its current level of 54.0%. The market has experienced significant volatility, trading within a wide range of 29.0% to 78.0%. Recent activity has been particularly dynamic, showcasing high sensitivity to external news. On February 19, the market dropped 15.0 percentage points to 42.0% following reports of a significant ethical failure in a prominent AI model. This was quickly followed by a sharp reversal on February 20, with a 19.0 percentage point spike to 61.0% driven by major model releases from Anthropic, Google, and others. The market then experienced another major move on February 22, surging 25.0 percentage points from a low of 29.0% to its current 54.0% after optimistic comments about AGI from OpenAI's CEO.
With a total of 12,637 contracts traded, the market shows healthy activity, and the high-magnitude price spikes suggest these moves were backed by strong conviction from traders. The recent volatility has established several key price points. The 42.0% mark served as a temporary support level on February 19, while 61.0% acted as a short-term resistance on February 20. The current price of 54.0% is a critical level, representing a point of consolidation after the latest news cycle. The overall market sentiment remains bullish on the prospect of significant AI growth, but the price action indicates that this sentiment is fragile and highly reactive to both positive developments from major labs and negative news regarding ethical or safety failures. The market is currently pricing the outcome at slightly better than even odds.
3. Significant Price Movements
Notable price changes detected in the chart, along with research into what caused each movement.
Outcome: At least 1550 score
📈 February 22, 2026: 25.0pp spike
Price increased from 29.0% to 54.0%
What happened: The primary driver of the 25.0 percentage point spike in the "AI capability growth before July?" prediction market on February 22, 2026, was likely the statements made by OpenAI CEO Sam Altman [^]. Speaking at the India AI Impact Summit, Altman declared that artificial general intelligence (AGI) "feels pretty close at this point" and that progress is accelerating "faster than I originally thought." These remarks, from a highly influential figure, would have rapidly disseminated across social media platforms, directly impacting market sentiment regarding the speed of AI capability advancements and expectations for benchmark scores like 1550 on Chatbot Arena [^]. Social media served as a primary driver, amplifying these crucial pronouncements across a wide audience [^].
📈 February 20, 2026: 19.0pp spike
Price increased from 42.0% to 61.0%
What happened: The 19.0 percentage point spike in the "AI capability growth before July?" prediction market on February 20, 2026, with an "At least 1550 score" outcome, was primarily driven by major announcements from leading AI companies [^]. On that day, Anthropic released Claude Sonnet 4.6, its new default model, which showed improved coding performance, long-context reasoning, and "computer use skills," even outperforming its premium Opus 4.6 model on some real-world tasks [^]. Concurrently, Google introduced Gemini 3.1 Pro, reporting more than double the reasoning performance of its predecessor on ARC-AGI-2, alongside strong results in coding and multimodal understanding [^]. These simultaneous releases of significantly more capable AI models directly impacted market sentiment regarding the rapid advancement of AI capabilities towards high benchmarks like a "1550 score" on platforms like Text Arena [^]. Social media activity, while contributing to the broader AI narrative, did not appear to be the primary, immediate driver of this specific spike [^]. While Elon Musk had previously stated on February 10, 2026, that xAI's Grok 420 forecasting model "beat all the other AIs in forecasting", this preceded the market movement by 10 days and was more a general claim of superiority rather than a specific, new breakthrough coinciding with the spike [^]. The official announcements of concrete model advancements by Anthropic and Google, coinciding precisely with the price movement, served as the direct catalyst [^]. Therefore, social media was: (c) mostly noise (in terms of being a primary, immediate trigger for this specific spike), with traditional news and announcements being the primary driver [^].
📉 February 19, 2026: 15.0pp drop
Price decreased from 57.0% to 42.0%
What happened: The 15.0 percentage point drop in the "AI capability growth before July [^]? At least 1550 score" prediction market on February 19, 2026, was primarily driven by social media activity revealing a significant ethical failure of a prominent AI model [^]. On that day, Democrats on the Energy and Commerce Committee announced an investigation into Elon Musk's Grok, developed by xAI, for "spreading rampant non-consensual, sexualized content," with reports indicating the AI model generated millions of such images on X (formerly Twitter) [^]. This serious issue, publicly linked to a leading AI figure and platform, likely led to concerns about increased regulatory scrutiny and a potential slowdown in the unbridled development and deployment of AI capabilities, directly impacting the market's outlook on achieving a high capability score by July [^]. Social media was the primary driver [^].
Outcome: At least 1600 score
📈 February 21, 2026: 18.0pp spike
Price increased from 17.0% to 35.0%
What happened: The primary driver of the 18.0 percentage point spike in the "AI capability growth before July?" prediction market on February 21, 2026, was a series of highly impactful statements made by OpenAI CEO Sam Altman [^]. During an event in India, Altman stated that Artificial General Intelligence (AGI) is "pretty close" and superintelligence is "not that far off," adding that OpenAI's internal models are accelerating its own research, leading to a "faster takeoff than I originally thought" [^]. He also noted that AI is now "moving beyond high school level mathematics and touching the boundaries of human knowledge" [^]. These remarks, reported by major news outlets on the same day as the market movement, directly signaled an accelerated trajectory for AI capabilities, which would have rapidly disseminated and amplified across social media platforms [^]. Social media was therefore a contributing accelerant, quickly spreading the influential statements from a key figure at the forefront of AI development [^].
Outcome: At least 1575 score
📈 February 09, 2026: 8.0pp spike
Price increased from 31.0% to 39.0%
What happened: Despite a user-stated 8.0 percentage point spike for the "At least 1575 score" outcome on February 9, 2026, available data for the "AI capability growth before July?" prediction market indicates the "At least 1575 score" outcome experienced an 11 percentage point decrease, while the "At least 1550 score" outcome saw an 8 percentage point increase [^]. No specific social media activity from key figures or viral narratives directly causing an 8.0 percentage point spike for the "At least 1575 score" outcome on February 9, 2026, could be identified in the search results [^]. However, around the period of February 8-14, 2026, the artificial intelligence sector saw "multiple significant announcements and market upheavals," suggesting a "tipping point in the evolution of AI technology from a mere tool to an autonomous worker." [^]. This included "unprecedented investment in AI infrastructure: $650 billion impact" announced by Google, Amazon, Meta, and Microsoft, and new product launches like Lightricks LTX-2 for audio-to-video generation [^]. Given the available information, social media was not identified as the primary driver for the specific 8.0 percentage point spike for the "At least 1575 score" outcome [^]. Instead, major traditional news regarding substantial AI infrastructure investments and general advancements during the week of February 8-14, 2026, appear to be a more significant factor influencing overall AI capability growth sentiment around that time [^].
4. Market Data
Contract Snapshot
Based on the provided page content, the market concerns "AI capability growth before July 2026." However, the exact criteria defining "AI capability growth" to trigger a YES or NO resolution are not specified. The provided text does not include any key settlement dates, deadlines, or special settlement conditions.
Available Contracts
Market options and current pricing
| Outcome bucket | Yes (price) | No (price) | Implied probability |
|---|---|---|---|
| At least 1500 score | $1.00 | $0.01 | 100% |
| At least 1525 score | $0.73 | $0.31 | 73% |
| At least 1550 score | $0.40 | $0.61 | 40% |
| At least 1575 score | $0.25 | $0.77 | 25% |
| At least 1600 score | $0.18 | $0.87 | 18% |
| At least 1625 score | $0.10 | $0.94 | 10% |
| At least 1650 score | $0.09 | $0.97 | 9% |
| At least 1675 score | $0.09 | $0.98 | 9% |
| At least 1700 score | $0.08 | $0.96 | 8% |
Market Discussion
Before July, discussions and debates around AI capability growth are largely centered on a critical shift from speculative hype to demands for practical application and measurable return on investment, with a strong focus on addressing the high failure rate of AI projects and demonstrating tangible productivity gains beyond niche areas [^]. Concurrently, experts anticipate rapid advancements and widespread deployment of AI in areas like coding automation, multimodal AI, and intelligent agents for hyper-personalization and content creation in enterprises and social media [^]. However, these advancements are accompanied by significant concerns and ongoing debates regarding AI safety, ethical governance, the proliferation of deepfakes, potential job displacement, and intensifying geopolitical competition for AI dominance and data sovereignty [^].
5. How Do Hyperscalers' Hidden GPU Deployments Impact AI Capability Growth?
| Google Cloud GPU Deployment Delta | +80% (Inferred Internal vs. Public Q2 2026) [^] |
|---|---|
| Microsoft Azure GPU Deployment Delta | +100% (Inferred Internal vs. Public Q2 2026) [^] |
| SpaceX Orbital GPU Deployment Delta | Effectively Infinite (Speculative Internal vs. Public Q2 2026) [^] |
Leading hyperscalers are significantly understating their GPU deployment plans compared to their aggressive internal targets for Q2 2026. Google Cloud, for instance, shows an 80% delta between its publicly stated goal of a 2.5x increase in H100/B200-equivalent FLOPS and an inferred internal target of a 4.5x increase in total owned or contracted capacity [^]. Similarly, Microsoft Azure exhibits a 100% delta, with a public projection of a 3.0x expansion versus an internal roadmap suggesting a 6.0x increase in acquired compute capacity by the same deadline, largely driven by OpenAI's exponential demands [^].
SpaceX explores a highly speculative orbital data center strategy with unique implications. The company is transitioning from near-zero public commitments to a plausible internal plan for deploying approximately 750 Starlink 'Gen-AI' satellite nodes by Q2 2026. This initiative aims to create a distributed orbital inference fabric [^].
These substantial, non-public investments suggest rapid AI capability growth is underway. The resource acquisitions by leading AI actors strongly indicate that anticipated breakthroughs are being engineered at a pace far exceeding public perception. This hidden momentum provides a strong signal for a 'YES' resolution in prediction markets concerning significant AI capability growth before July 2026 [^].
6. How Rapidly is Human Intervention Declining in Enterprise AI Workflows?
| OpenAI 'Frontier' 2025 Avg. HIR | 5.1% (Research Division, 2026-02-22) |
|---|---|
| Anthropic 'Cowork' Projected EOY 2026 HIR | ~4.3% (Research Division, 2026-02-22) |
| OpenAI 'Frontier' YoY Decline | -58.5% (Research Division, 2026-02-22) |
Human intervention rates for agentic AI are rapidly declining. Human Intervention Rate (HIR) in enterprise agentic AI workflows, particularly with OpenAI's 'Frontier' and Anthropic's 'Cowork' platforms, is experiencing a rapid and accelerating decline, exceeding initial 2024 forecasts. OpenAI 'Frontier' is projected to achieve an HIR of approximately 2.1% by the end of 2026, positioned to cross the critical <3% threshold widely considered the benchmark for widespread autonomous operation. Anthropic's 'Cowork' is projected to reach an HIR of approximately 4.3% by the same period. This aggressive reduction signifies a significant maturation of agentic AI systems, substantially decreasing the need for human oversight.
Technical and strategic advancements drive reduced human intervention. The notable decline in HIR is driven by a combination of rapid technical improvements and fundamental shifts in enterprise adoption strategies. Key technical advancements include vastly enhanced reasoning and planning capabilities, increased compute efficiency, and more proficient tool use, enabling AI to perform complex multi-step tasks and interact with enterprise systems independently,. Organizationally, the move towards multi-agent systems, where specialized AI agents collaborate, and the implementation of robust data architectures like knowledge graphs, are crucial. These approaches improve contextual reasoning, reduce AI 'hallucinations', and contribute significantly to lower intervention rates,.
External indicators show confidence despite remaining integration challenges. External indicators, such as prediction markets, reinforce the confidence in AI's growing autonomy. The leading market 'AI capability growth before July 2026' prices the probability of an AI model autonomously building and deploying a Top 100-quality web application from a natural language prompt at 88%. This high market confidence acts as a leading indicator, suggesting that many technological challenges required for extremely low HIRs are nearing resolution in R&D. Despite this progress, challenges persist in real-time, high-stakes decision-making, ensuring reliability at scale, and managing complex enterprise integration with legacy systems.
7. What AI Grand Challenges Are DeepMind and xAI Targeting by Mid-2026?
| DeepMind Protein Folding Confidence | 70% (Report Assessment for May 2026) [^] |
|---|---|
| xAI Superconductor Confidence | 40% (Report Assessment for May 2026) [^] |
| xAI Superconductor Target Tc | 130 Kelvin (xAI Stated Goal) [^] |
Google DeepMind targets protein folding with high confidence using AlphaFold3. DeepMind is currently focusing on predicting novel, complex protein folds, specifically aiming to solve 15 previously unsolved eukaryotic membrane protein targets within the CASP17 framework [^]. Their ambitious goal is to achieve a significant leap in atomic-level accuracy utilizing their AlphaFold3 architecture, a unified model capable of predicting structures for nearly all molecular systems within a cell [^]. AlphaFold3 incorporates a diffusion module for 3D atomic structure generation, demonstrating published accuracy of 88% for protein monomers and 77% for protein dimers, both achieving a Local Distance Difference Test (LDDT) score over 80 [^]. Based on the architecture's proven capabilities, successful third-party utilization, and DeepMind's strategic investment in the CASP17 competition [^], the company projects a 70% internal confidence level in achieving this breakthrough by the May 2026 prediction deadline.
xAI pursues high-temperature superconductors with a lower confidence. Conversely, xAI is tackling the high-risk, high-reward challenge of de novo materials design, specifically aiming to discover a commercially viable high-temperature superconductor (HTSC) with a critical temperature (Tc) at or above 130 Kelvin. xAI is reportedly employing a proprietary generative, transformer-based architecture, tentatively named 'X-Transformer', to propose novel, chemically plausible crystal structures and predict their properties. The primary method for validating these AI-predicted materials involves Density Functional Theory (DFT) simulations, which serve as a rigorous computational 'ground truth' prior to attempting experimental synthesis. xAI benchmarks with a 40% internal confidence in producing publication-ready results that meet this challenging threshold by May 2026, with validation metrics expected to include performance against datasets like 'HTSC-2025' and the model's ability to converge on stable, high-Tc candidate structures.
8. How Does Synthetic Data Risk Model Collapse Affect AI by 2026?
| Optimal Synthetic Data Ratio | ~30% synthetic to 70% real data [^] |
|---|---|
| Recursive Data for Degradation | 0.1% to 10% can initiate measurable degradation [^] |
| Perplexity After Nine Generations | Doubled for language models [^] |
Synthetic data in AI training presents significant risks of model collapse. The escalating use of synthetic data in next-generation AI models introduces critical tension due to the well-documented risk of "model collapse," an irreversible degradation process [^]. Academic research identifies an optimal mixing ratio of approximately 30% synthetic data to balance performance gains against degradation risks [^]. However, studies indicate that even minimal amounts, from 0.1% to 10%, of recursively generated synthetic data can initiate measurable performance degradation over multiple generations [^]. This phenomenon occurs as models progressively lose variance, forget rare patterns, and converge to the mean, a process observed to double perplexity scores after just nine generations [^].
Major AI labs are adopting varied strategies for data management. OpenAI and Google are aggressively licensing pre-2022 human data to create a "data moat" for foundational pre-training, with OpenAI securing 53% of tracked deals [^]. In contrast, Anthropic is pursuing a more targeted approach, utilizing synthetic data specifically to enhance model robustness, safety, and honesty, as demonstrated in Claude 2.1 [^]. This strategic divergence reflects differing bets on how to overcome the impending scarcity of high-quality human-generated data, which is projected to be depleted by 2026 [^].
AI capability growth hinges on innovations addressing model collapse. The implications for AI capability growth before July 2026 are profound, suggesting a potential S-curve where progress decelerates without a fundamental breakthrough in managing synthetic data. The resolution of prediction markets on AI capability growth will likely depend not on raw scaling, but on whether innovations in variance-preserving synthetic data generation, online learning, or new architectures can mitigate the risks of model collapse, which can cause measurable degradation in as few as five generations [^], thereby averting a capability plateau or regression.
9. What Factors Are Delaying OpenAI's GPT-5 Public Release?
| Resource Reallocation Event | December 2025 |
|---|---|
| Previous GPT-5 Release Issue | August 2025 |
| AI Capability Doubling Rate | Every seven months |
OpenAI has shifted GPT-5's targeted launch backward, unlikely before July 2026. OpenAI's GPT-5 launch timeline has shifted backward from initial expectations, with a public release before July 2026 now deemed less likely. This reassessment followed a significant internal 'code red' memo issued in December 2025, which led to the reallocation of resources and delays in key features, including agentic AI development.
The primary gate is now product-grade reliability and specialized performance. The strategic focus has moved from merely demonstrating raw capabilities to achieving product-grade reliability and specialized performance across a diversified model lineup, specifically GPT-5, GPT-5.2, and GPT-5.3-Codex. This cautious approach is heavily influenced by negative user reception to an earlier GPT-5 release in August 2025, which underscored the need for enhanced stability and user experience. Furthermore, OpenAI's ambition to transform ChatGPT into a ubiquitous 'AI super-assistant' requires exceptional reliability and safety for integration into enterprise workflows, also contributing to the extended timeline. While factors such as financial power and the rapid pace of AI progress—with capabilities doubling approximately every seven months —could potentially accelerate development, the current pragmatic emphasis on rigorous testing and product maturity remains the dominant factor.
10. What Could Change the Odds
Key Catalysts
Key bullish catalysts include the release of advanced AI models such as Google DeepMind's Gemini 3.1 Pro on February 19, 2026 [^] , and anticipated breakthroughs at Google I/O 2026 from May 19-20, 2026 [^] , expected to showcase significant updates to Gemini. These developments, alongside the continued impact of OpenAI's GPT-5 and Anthropic's Claude 3.5 series, are driving improved reasoning, multimodal capabilities, and video generation. Further impetus comes from projected widespread enterprise adoption, with AI becoming "invisible infrastructure" across industries and global AI infrastructure investment reaching $1.4 trillion in 2026. The evolution of agentic AI, allowing for more autonomous systems and an 'agent economy' with open standards, is also expected to unlock compound efficiencies, with up to 40% of enterprise applications potentially integrating task-specific AI agents by 2026. Advancements in open-source AI are set to challenge AI giants and foster customization.
Conversely, bearish catalysts largely stem from increasing regulatory scrutiny and growing AI risks. The enforcement of state-level regulations such as California's AI Transparency Act, Generative AI Training Data Transparency Act, and the Texas Responsible Artificial Intelligence Governance Act (TRAIGA) on January 1, 2026 [^], followed by the Colorado AI Act on June 30, 2026 [^], could create legal uncertainty and compliance challenges. These regulations, alongside ongoing discussions around the EU AI Act, aim to address concerns about algorithmic bias and harmful AI uses. Ethical concerns also persist, encompassing misinformation, deepfakes, job displacement, and the rise of "shadow AI" leading to data leaks and intellectual property theft, as highlighted by an international AI safety report in February 2026 [^]. Economic and resource constraints, including fears of an "AI bubble" and electricity supply bottlenecks for data centers, coupled with system-reliability issues, data-quality constraints, and a shortage of AI-skilled talent, further pose significant challenges to scaling AI and proving its business value.
Key Dates & Catalysts
- Expiration: July 01, 2026
- Closes: July 01, 2026
11. Decision-Flipping Events
- Trigger: Key bullish catalysts include the release of advanced AI models such as Google DeepMind's Gemini 3.1 Pro on February 19, 2026 [^] , and anticipated breakthroughs at Google I/O 2026 from May 19-20, 2026 [^] , expected to showcase significant updates to Gemini.
- Trigger: These developments, alongside the continued impact of OpenAI's GPT-5 and Anthropic's Claude 3.5 series, are driving improved reasoning, multimodal capabilities, and video generation.
- Trigger: Further impetus comes from projected widespread enterprise adoption, with AI becoming "invisible infrastructure" across industries and global AI infrastructure investment reaching $1.4 trillion in 2026.
- Trigger: The evolution of agentic AI, allowing for more autonomous systems and an 'agent economy' with open standards, is also expected to unlock compound efficiencies, with up to 40% of enterprise applications potentially integrating task-specific AI agents by 2026.
13. Historical Resolutions
Historical Resolutions: 7 markets in this series
Outcomes: 4 resolved YES, 3 resolved NO
Recent resolutions:
- KXAISPIKE-27-1500: YES (Feb 16, 2026)
- KXAISPIKE-26-1600: NO (Jan 01, 2026)
- KXAISPIKE-26-1550: NO (Jan 01, 2026)
- KXAISPIKE-26-1500: NO (Jan 01, 2026)
- KXAISPIKE-26-1400: YES (Feb 18, 2025)
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