Monitoring and tracking artificial intelligence development and risk assessment in real-time.

Skynet Chance (-0.05%): Government intervention and model holding patterns reduce the likelihood of releasing an uncontrolled, highly capable model prematurely. This oversight, despite its current lack of structured testing, adds a layer of friction against sudden, catastrophic AI deployments.

Skynet Date (+1 days): Mandatory customer-by-customer reviews and pre-release holds delay the deployment of frontier models, effectively decelerating the timeline toward potential advanced safety risks. This regulatory friction buys more time for safety research and the establishment of robust evaluation frameworks.

AGI Progress (-0.04%): Heavy-handed government intervention and delayed releases limit the commercial viability and iterative deployment of next-generation models, directly hindering the practical progress toward AGI. The resulting threat to laboratory revenue could also stifle the capital-intensive infrastructure scaling necessary for AGI development.

AGI Date (+1 days): The introduction of a bottlenecked, customer-by-customer approval process for frontier models decelerates the deployment pace and pushes the expected timeline for achieving and releasing AGI further into the future. It also threatens to slow down the data center buildouts essential for training larger, more capable models.

Skynet Chance (-0.05%): Active government oversight and restricted release protocols reduce the likelihood of highly capable models being immediately leaked or autonomously deployed without safety guardrails. This intervention establishes a precedent of external vetting, which mitigates the risk of sudden, uncontrolled AI proliferation.

Skynet Date (+1 days): Imposing pre-release evaluations and customer-by-customer vetting cycles slows down the rapid deployment and scaling of potentially dangerous autonomous agents. This regulatory friction delays the timeline for when an uncontrollable AI system could be widely distributed.

AGI Progress (+0.01%): The development of GPT 5.6, with advanced capabilities that trigger government concern, confirms ongoing technical progress toward highly capable systems. However, deployment restrictions slightly dampen the immediate real-world feedback loop required for further refinement.

AGI Date (+0 days): Government-mandated safety reviews and restricted rollouts introduce bureaucratic delays that slow down the iterative deployment cycle of frontier models. This regulatory bottleneck extends the timeline for achieving and deploying fully realized AGI.

Skynet Chance (-0.08%): Automated stress-testing in realistic digital simulations helps identify unpredictable AI agent behaviors and shortcuts before real-world deployment. This improves system alignment and safety, reducing the likelihood of catastrophic failure or loss of control.

Skynet Date (+1 days): By providing systematic evaluation environments, this technology allows developers to patch vulnerabilities and align agent behavior before deployment, slowing down the timeline toward uncontrolled risk scenarios.

AGI Progress (+0.03%): Creating high-fidelity digital simulation worlds allows AI agents to train autonomously on complex, long-horizon tasks via reinforcement learning. This overcomes current benchmarking limitations and directly accelerates the development of highly reliable, AGI-like capabilities.

AGI Date (-1 days): Automated simulation testing bypasses slow human-in-the-loop evaluation, significantly speeding up the iterative development and deployment cycle of sophisticated AI agents.

Skynet Chance (+0.01%): Drastically reducing energy requirements could democratize the deployment of highly advanced AI models, making oversight and safety regulation harder to enforce globally. This decentralization slightly increases the long-term risk of uncontrollable or malicious AI deployments.

Skynet Date (-1 days): By potentially removing the energy constraints that threaten to stall AI growth, this technology could accelerate the development timeline toward potentially hazardous, autonomous systems.

AGI Progress (+0.03%): Energy consumption is currently a primary constraint on AI scaling, making a potential 1,000x efficiency improvement a major leap forward for running AGI-scale workloads.

AGI Date (-1 days): Solving the power bottleneck would allow massive, rapid expansion of compute infrastructure, significantly pulling forward the timeline for achieving AGI.

Skynet Chance (+0.04%): Training general AI models on action-labeled simulated environments dramatically lowers the barrier to creating highly capable physical agents. Although the company bans military use, the underlying technology of cross-domain embodiment increases the long-term risk of uncontrollable physical AI agents.

Skynet Date (-1 days): Using vast libraries of video game data as a training shortcut bypasses the slow and expensive process of collecting real-world physical data. This methodological acceleration brings the timeline for highly capable, physically embodied autonomous systems closer.

AGI Progress (+0.03%): The ability of a single model to generalize spatial-temporal reasoning from game engines to real-world physical embodiments marks a significant leap toward generalized physical agency. By leveraging action-labeled gameplay, the model successfully bridges the gap between digital reasoning and physical execution.

AGI Date (-1 days): A massive capital injection coupled with a virtually infinite, pre-labeled dataset of human actions accelerates the timeline for achieving general physical intelligence. This bypasses traditional data bottlenecks in robotics, potentially bringing AGI-like physical capabilities closer.

Skynet Chance (+0.01%): Expanding global compute infrastructure increases the capacity to train more powerful, potentially uncontrollable AI systems. However, infrastructure growth itself does not inherently alter the alignment paradigm, resulting in only a minor increase in overall risk.

Skynet Date (-1 days): The massive influx of capital into global data center infrastructure accelerates the hardware scaling necessary to train advanced models, potentially shortening the timeline to uncontrollable AI. This competitive rush to expand physical compute capabilities brings the risk horizon closer.

AGI Progress (+0.02%): Massive hardware and infrastructure expansion directly fuels the compute-scaling laws necessary for progressing toward AGI. By broadening data center capacity globally, tech giants are building the physical foundation required for next-generation AI.

AGI Date (-1 days): This significant capital deployment accelerates the timeline to AGI by rapidly overcoming the physical compute bottlenecks currently limiting model training. The competitive infrastructure race among tech giants in India will likely compress the time needed to develop advanced systems.

Skynet Chance (0%): The migration of researchers between leading AI companies redistributes expertise but does not inherently alter the fundamental likelihood of creating uncontrollable or hostile AI systems.

Skynet Date (+0 days): Concentrating top talent in highly competitive, fast-moving startups could accelerate frontier AI capabilities, potentially outstripping the development of necessary safety and alignment frameworks.

AGI Progress (+0.01%): Consolidating elite researchers from Google into focused, mission-driven labs like Anthropic and OpenAI is likely to enhance collective research synergy and lead to incremental capability gains.

AGI Date (+0 days): The influx of experienced developers to agile startups preparing for public offerings will likely hasten the timeline to AGI by streamlining the development of next-generation models.

Skynet Chance (+0.01%): The widespread commercial deployment of physical humanoid robots increases the potential real-world impact surface if control is ever lost. The growth of physical AI infrastructure provides the necessary hardware substrate for potential future physical disruption.

Skynet Date (-1 days): Massive capital injection into humanoid manufacturing accelerates the timeline for deploying physical AI systems into the human environment. This rapid commercialization outpaces the development of robust physical safety frameworks, potentially accelerating physical AI risk timelines.

AGI Progress (+0.02%): Securing substantial funding to scale humanoid robots provides a critical physical testing ground and data pipeline for embodied AI, which is essential for general intelligence. Real-world feedback from thousands of deployed units will accelerate the training of more generalized physical foundation models.

AGI Date (-1 days): The rapid commercialization and deployment of humanoid hardware will significantly speed up the collection of real-world interaction data. This abundance of physical interaction data is expected to accelerate the timeline for achieving physically grounded AGI.

Skynet Chance (+0.01%): Lowering inference costs and optimizing hardware enables wider, more pervasive deployment of agentic AI systems, marginally increasing the systemic risks of uncontrolled model behavior.

Skynet Date (-1 days): By reducing costs and increasing efficiency of running complex models, this development accelerates the deployment timelines of sophisticated AI agents, potentially hastening the arrival of safety-critical scenarios.

AGI Progress (+0.02%): Designing custom, highly efficient silicon tailored for OpenAI's specific workloads provides the computational foundation necessary to run increasingly complex and agentic AI models closer to real-time.

AGI Date (-1 days): Accelerating inference speeds and lowering operational costs will likely speed up the deployment, refinement, and testing cycles of frontier models, bringing the achievement of AGI closer.