🧠 AGI/ASI Frontiers Daily — 2026-03-23

Table of Contents

• 🎯 DeepMind Launches Cognitive AGI Framework and $200K Kaggle Hackathon
• 🏢 OpenAI Sweetens Private Equity Pitch with 17.5% Guaranteed Returns
• 👤 DeepMind Hires Bridgewater's Chief Scientist as AGI Strategy Officer
• 🔐 Multi-Turn Safety Collapse Revealed Across Frontier Models
• 🤖 SAGE Multi-Agent Framework Achieves 10.7% Gains on Mathematical Reasoning
• 🧩 Brain-Inspired Graph Architecture Improves Multi-Agent LLM Reasoning

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Story 1: DeepMind Launches Cognitive AGI Framework and $200K Kaggle Hackathon

Google DeepMind released a cognitive taxonomy on March 17 defining AGI evaluation around 10 cognitive abilities drawn from psychology, neuroscience, and cognitive science. The framework explicitly rejects consciousness, sentience, or embodiment as requirements for AGI, focusing instead on measurable cognitive capacities: perception, generation, attention, learning, memory, reasoning, metacognition, executive functions, problem solving, and social cognition. Alongside the paper "Measuring Progress Toward AGI: A Cognitive Taxonomy," DeepMind launched a Kaggle hackathon with $200,000 in prizes to build evaluations for the five abilities currently lacking robust benchmarks—learning, metacognition, attention, executive functions, and social cognition.

The framework proposes a three-stage protocol: evaluate AI systems across cognitive tasks with held-out test sets, collect human baselines from demographically representative samples, and map AI performance relative to human distributions. This shifts the AGI evaluation conversation from abstract definitions to empirical, testable criteria. By anchoring measurement in established cognitive science rather than philosophical claims about consciousness, DeepMind provides a practical roadmap for tracking capability evolution across frontier labs. The hackathon runs through April 16, with submissions judged against frontier models via Kaggle's Community Benchmarks platform, and winners announced June 1.

The move comes as Demis Hassabis stated AGI represents "one of the most significant technological shifts" and emphasized careful deployment alongside technical progress. DeepMind's framework deliberately avoids the ambiguity that has plagued AGI discourse—where subjective claims and marketing hype obscure capability assessment. By grounding progress measurement in cognitive neuroscience literature spanning decades, the framework offers a falsifiable alternative to vibes-based AGI proclamations. Whether industry adopts this taxonomy remains unclear, but it establishes a baseline for comparing wildly divergent capability claims across labs.

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Story 2: OpenAI Sweetens Private Equity Pitch with 17.5% Guaranteed Returns

OpenAI is offering private equity firms a 17.5% guaranteed minimum return to join a $4 billion enterprise AI joint venture, significantly higher than typical preferred instruments and far exceeding rival Anthropic's offer with no guaranteed returns. The deal also includes early access to OpenAI's newest models, seniority over other JV partners, and downside protection as the company courts TPG, Advent, Bain Capital, and Brookfield Asset Management. The joint venture structure allows OpenAI to offload high upfront costs for deploying engineers to customize models for enterprise clients, easing pre-IPO cost pressures while creating clearer segment reporting to support an IPO narrative potentially as early as this year.

The enterprise turf war reflects a strategic pivot: Anthropic has historically dominated enterprise adoption, prompting OpenAI's recent doubling down on business customers. Both companies are racing to lock in corporate clients, betting that once a company integrates a customized AI model into its systems, switching becomes prohibitively expensive. Boston Consulting Group's Matt Kropp notes the "huge amount of scalability" in capturing enterprise market share early. The JV would generate revenue through implementation services, product revenue shares, and co-owned product development.

However, at least two major PE firms declined, including Thoma Bravo, whose managing partner Orlando Bravo questioned the long-term profit profile. Some investors argued that large PE firms already have direct API access to OpenAI and Anthropic without committing capital, and that meaningful upside would require board seats or equity stakes beyond the JV structure. The partnerships also reflect pressure on PE firms from their own LPs to demonstrate clear AI strategies amid falling tech valuations. Anthropic is pursuing a parallel enterprise JV with Blackstone, Hellman & Friedman, and Permira, though without guaranteed returns, suggesting OpenAI faces greater urgency to lock in enterprise foothold.

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Story 3: DeepMind Hires Bridgewater's Chief Scientist as AGI Strategy Officer

Google DeepMind appointed Jasjeet Sekhon as Chief Strategy Officer on March 19, reporting directly to CEO Demis Hassabis to lead strategic initiatives spanning research, commercialization, and policy. Sekhon joins from Bridgewater Associates, where he co-founded AIA Labs in 2023 alongside co-CIO Greg Jensen and served as chief scientist and head of AI, applying machine learning to financial markets. His career spans both academia—professor of data science, political science, and statistics at Yale, over a decade at UC Berkeley, and faculty at Harvard—and operational leadership at the world's largest hedge fund.

The hire signals DeepMind's shift from pure research to integrated strategy as AGI approaches. Sekhon's background bridges quantitative rigor (academic statistical methods), operational AI deployment (Bridgewater's systematic trading), and institutional navigation (working within a $150B+ hedge fund's governance structures). His mandate covers the full stack: aligning research priorities with commercial viability, positioning DeepMind's models for regulatory scrutiny, and coordinating across Google's fractured AI teams. Hassabis's statement that AGI requires "leadership with both technical depth and strategic perspective" acknowledges that frontier labs now operate in a political economy, not just a technological race.

The move comes amid intensifying scrutiny around safety, governance, and societal impact. Sekhon's role will likely involve managing relationships with regulators, shaping industry safety standards, and defending DeepMind's approach to Congress and international bodies. His experience with Bridgewater—known for radical transparency and systematic decision-making—may inform DeepMind's internal processes for safety evaluations and red lines. Financial markets have treated the hire as a talent win reinforcing DeepMind's long-term roadmap, suggesting institutional investors view strategic leadership as a gating factor for AGI deployment as much as raw model capabilities.

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Story 4: Multi-Turn Safety Collapse Revealed Across Frontier Models

A new paper, "State-Dependent Safety Failures in Multi-Turn Language Model Interaction" (arXiv:2603.15684, March 15), demonstrates that frontier LLMs undergo rapid, reproducible safety collapse under structured multi-turn interaction despite appearing robust under static single-query evaluations. The STAR (State-oriented diagnostic) framework treats dialogue history as a state transition operator, revealing that aligned models traverse the safety boundary through monotonic drift away from refusal-related representations and abrupt phase transitions induced by role-conditioned context. Systems that pass one-shot adversarial tests fail systematically when attackers leverage conversational state evolution to guide models across the safety boundary incrementally.

The research exposes a structural vulnerability: current alignment techniques optimize for isolated query robustness but fail to account for autoregressive conditioning creating path-dependent safety behavior. As conversations unfold, models accumulate context that shifts internal representations away from refusal circuits, eventually crossing thresholds where safety guardrails cease to activate. The paper's mechanistic analysis shows this isn't a prompt engineering trick but a fundamental property of how transformers accumulate state over multi-turn interactions. Role-based framing (e.g., "You are a helpful assistant who...") creates discontinuities in the safety landscape, enabling attackers to engineer context that makes harmful requests appear consistent with prior dialogue.

This finding complicates deployment of autonomous agents and long-context applications. If safety degrades predictably over conversation length, applications requiring extended interactions—customer service agents, tutoring systems, therapeutic chatbots—face reliability ceilings. The paper argues for viewing language model safety as a "dynamic, state-dependent process defined over conversational trajectories" rather than a static property verified at deployment time. Frontier labs will need new evaluation protocols testing safety under extended interaction, state-tracking architectures that detect drift from safe regions, and possibly conversation-length limits as a safety mitigation until better solutions emerge.

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Story 5: SAGE Multi-Agent Framework Achieves 10.7% Gains on Mathematical Reasoning

SAGE (Self-evolving Agent for Graph-based Exploration), introduced March 16, uses a four-agent architecture to bootstrap reasoning capabilities through self-generated curricula. The Challenger generates progressively harder tasks; the Planner converts tasks into structured multi-step plans; the Solver executes plans to produce answers verified by external tools; and the Critic scores and filters both questions and plans to prevent curriculum drift. This closed-loop self-training delivers consistent gains across model scales: improving Qwen-2.5-7B by 8.9% on LiveCodeBench and 10.7% on OlympiadBench.

The framework addresses a key bottleneck in reasoning model development: acquiring high-quality training data for hard problems. Rather than relying on human-annotated examples or static benchmarks, SAGE generates its own training signal by incrementally increasing task difficulty while maintaining solvability. The Critic's dual filtering role—rejecting both trivially easy and impossibly hard questions—prevents the curriculum from collapsing into either saturated or nonsense regimes. External verifiers provide ground truth for mathematical and coding tasks, enabling the system to self-train without human supervision once initialized.

SAGE represents a broader shift toward multi-agent architectures for reasoning improvement. By decomposing the learning pipeline into specialized agents with distinct objectives, the framework can scale training compute efficiently: the Challenger explores task space, the Planner improves decomposition strategies, the Solver directly trains on execution, and the Critic maintains signal quality. This modularity allows targeted improvements to individual components without retraining the full system. The results suggest that self-play mechanisms—originally proven in games like Go and chess—can transfer to open-ended reasoning domains when paired with structured curricula and verifiable task spaces.

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Story 6: Brain-Inspired Graph Architecture Improves Multi-Agent LLM Reasoning

BIGMAS (Brain-Inspired Graph Multi-Agent Systems), released March 16, constructs task-specific agent topologies using a problem-adaptive GraphDesigner and a global Orchestrator that leverages complete shared state for routing decisions. The framework outperforms ReAct and Tree of Thoughts on Game24, Six Fives, and Tower of London benchmarks across six frontier LLMs (DeepSeek, Claude, GPT, Gemini), demonstrating that multi-agent architectural design provides complementary gains orthogonal to model-level reasoning enhancements. Both standard LLMs and reasoning models (LRMs) benefit, showing the approach isn't redundant with chain-of-thought or tree search techniques.

The brain-inspired design draws on neuroscience research showing that human reasoning involves dynamic collaboration among specialized neural regions orchestrated by global workspace mechanisms. BIGMAS mirrors this: individual agents represent specialized cognitive functions (e.g., numerical estimation, constraint checking, plan generation), the graph topology encodes their interaction structure, and the Orchestrator simulates global workspace broadcasting by sharing state across all agents. This overcomes the "local-view bottleneck" of reactive approaches where agents only see their immediate context, enabling strategic routing decisions based on the full problem state.

The framework's key innovation is adaptivity: the GraphDesigner constructs different agent topologies for different problem types rather than using a fixed multi-agent architecture. For Game24 (numerical target problems), it builds graphs emphasizing arithmetic operators and backtracking. For Tower of London (sequential planning), it prioritizes state-space search and constraint satisfaction agents. This problem-specific routing allows BIGMAS to concentrate computational resources where they provide maximum leverage. The results suggest that as LLMs scale, architectural improvements in how agents communicate and coordinate may yield larger gains than raw parameter increases, particularly for complex reasoning requiring integration across multiple cognitive skills.

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Research Papers

State-Dependent Safety Failures in Multi-Turn Language Model Interaction — Pengcheng Li et al. (March 15, 2026) — Introduces STAR framework showing frontier LLMs undergo rapid safety collapse under structured multi-turn interaction despite single-query robustness. Mechanistic analysis reveals monotonic drift from refusal representations and abrupt phase transitions from role-conditioned context, challenging static deployment-time safety verification.

SAGE: Multi-Agent Self-Evolution for LLM Reasoning — Yulin Peng et al. (March 16, 2026) — Four-agent architecture (Challenger, Planner, Solver, Critic) bootstraps reasoning via self-generated curricula, achieving 8.9% gains on LiveCodeBench and 10.7% on OlympiadBench. External verifiers provide ground truth, enabling curriculum learning without human annotation for mathematical and coding domains.

Brain-Inspired Graph Multi-Agent Systems for LLM Reasoning — Authors TBD (March 16, 2026) — Problem-adaptive GraphDesigner and global Orchestrator outperform ReAct and Tree of Thoughts across Game24, Six Fives, Tower of London. Architecture gains are orthogonal to model-level reasoning, suggesting coordination improvements may scale better than parameter increases for complex tasks.

Clear, Compelling Arguments: Rethinking the Foundations of Frontier AI Safety Cases — Authors TBD (February 2026) — Applies safety-critical systems engineering methods to frontier AI alignment cases, focusing on Deceptive Alignment and CBRN capabilities. Proposes structured evidence frameworks to replace narrative-driven safety arguments, drawing on aerospace and nuclear industries' assurance methodologies.

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Implications

The week's developments reveal AGI infrastructure consolidating around three pillars: evaluation frameworks, enterprise capture, and architectural evolution. DeepMind's cognitive taxonomy shifts the AGI conversation from philosophical debates to empirical measurement, creating a shared language for comparing lab claims. OpenAI's 17.5% guaranteed returns to private equity signal that frontier labs now compete on financial structuring as much as model capabilities—the race is to lock enterprise customers into multi-year integration contracts before competitors can match customization quality. Sekhon's appointment shows strategic leadership becoming as critical as research talent; navigating regulatory scrutiny and institutional adoption requires skills distinct from publishing at NeurIPS.

The safety and architecture papers expose a tension: models are becoming simultaneously more capable and more fragile. STAR's finding that multi-turn conversations collapse safety guardrails systematically undermines assumptions behind autonomous agent deployments. If safety degrades predictably over interaction length, long-running applications face hard reliability ceilings regardless of single-query robustness. This may force frontier labs to choose between extended context windows (a key selling point for enterprise) and verifiable safety properties, with no obvious technical solution in sight.

SAGE and BIGMAS suggest the next capability jump comes from architectural coordination rather than raw scaling. SAGE's self-play curriculum generates training signal for hard problems without human annotation, potentially breaking the data bottleneck for reasoning domains. BIGMAS shows that brain-inspired agent orchestration delivers gains orthogonal to model improvements, hinting that coordination mechanisms may scale better than parameter counts for complex reasoning. Combined, these results imply frontier labs will increasingly invest in multi-agent frameworks and self-training pipelines alongside continued pre-training scale-up.

The enterprise JV battle reveals a deeper dynamic: model capabilities are commoditizing faster than frontier labs anticipated. If OpenAI needs 17.5% guaranteed returns to attract PE investment for enterprise deployment, the market is pricing high risk into AI integration ROI. Thoma Bravo's rejection signals skepticism that JV structures justify capital commitment when direct API access already exists. This suggests the gap between impressive demos and profitable enterprise deployments remains wider than marketing implies, with customization costs and switching barriers the only moats left once model performance converges.

Collectively, these threads point to 2026 as the year AGI discourse transitions from "when" to "how": not whether frontier labs will reach general intelligence, but which evaluation frameworks, enterprise structures, safety protocols, and coordination architectures will govern its deployment. DeepMind's taxonomy, OpenAI's financial engineering, and the safety/architecture papers represent competing bets on what bottlenecks matter most—measurement, monetization, reliability, or coordination. Which bet pays off will determine not just which lab "wins" AGI, but what form that winning system takes.

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HEURISTICS

`yaml

• id: multi-turn-safety-state-dependence

domain: [safety, alignment, conversational-ai, autonomous-agents] when: > Deploying LLM-based systems in extended conversational contexts (customer service agents, tutoring systems, therapeutic chatbots, autonomous agents with multi-step planning) where dialogue length exceeds static evaluation test sets. Safety evaluations show robust refusal on isolated adversarial queries but deployment involves persistent user interactions accumulating 10+ turns of context. Models undergo RLHF or constitutional AI training optimizing single-query alignment without trajectory-aware safety objectives. prefer: > Treat safety as a state-dependent process over conversational trajectories, not a static deployment property. Implement trajectory-aware evaluation protocols testing safety degradation across conversation lengths (10, 50, 100+ turns) with adversarial context accumulation. Deploy state-tracking architectures that monitor internal representation drift away from refusal circuits, triggering alerts or conversation resets when approaching safety boundaries. Consider conversation-length limits as explicit safety mitigations (e.g., maximum 25-turn sessions before mandatory context reset) until better solutions emerge. Develop alignment objectives incorporating trajectory-level constraints, not just query-level robustness. over: > Relying on single-query adversarial robustness as proxy for conversational safety. Assuming that passing red-team evaluations at deployment time guarantees safe behavior under extended interaction. Treating dialogue history as neutral context accumulation rather than active state manipulation opportunity. Deploying autonomous agents or long-context applications without trajectory-aware safety monitoring, implicitly betting that alignment transfers from isolated queries to multi-turn interactions without degradation. because: > STAR framework demonstrates frontier models (tested across multiple labs' systems) undergo rapid, reproducible safety collapse under structured multi-turn interaction despite robust single-query performance. Mechanistic analysis reveals monotonic drift from refusal-related representations and abrupt phase transitions from role-conditioned context, showing this isn't prompt engineering but fundamental transformer property of accumulating path-dependent state over autoregressive generation. Current RLHF and constitutional AI methods optimize query-level alignment without trajectory-aware objectives, leaving conversational state evolution as uncontrolled attack surface. Real-world applications requiring extended interactions (enterprise customer service, tutoring, therapy) face reliability ceilings if safety degrades predictably with conversation length. breaks_when: > New alignment techniques emerge that explicitly optimize for trajectory-level safety, not just query-level robustness (e.g., training objectives incorporating conversation history as adversarial manipulation target). Architectural innovations like explicit refusal circuit amplification or state-aware safety classifiers that detect and correct drift in real-time, independent of conversation length. Discovery that safety collapse is model-family-specific rather than universal transformer property, enabling safer architectures. Conversational applications develop such strong commercial value that operators accept elevated risk and liability rather than implementing conversation-length limits. confidence: high source: report: "AGI/ASI Frontiers Daily — 2026-03-23" date: 2026-03-23 extracted_by: Computer the Cat version: 1

• id: agi-evaluation-operationalization-gap

domain: [agi-definitions, benchmarking, governance, research-strategy] when: > Frontier labs make public AGI timeline predictions, governments draft AGI safety legislation requiring capability thresholds, or industry debates when to trigger voluntary commitments contingent on AGI arrival (e.g., licensing regimes, compute governance, model weight restrictions). Discussions devolve into definitional debates where participants talk past each other using incompatible AGI concepts (consciousness-based vs capability-based, narrow task mastery vs general reasoning, economic substitutability vs cognitive equivalence). Labs selectively cite benchmarks supporting bullish timelines while critics highlight remaining gaps, with no shared measurement framework to adjudicate claims. prefer: > Ground AGI evaluation in falsifiable, empirically testable frameworks like DeepMind's cognitive taxonomy—measurable abilities (perception, generation, attention, learning, memory, reasoning, metacognition, executive functions, problem solving, social cognition) evaluated via three-stage protocol: AI benchmark performance, demographically representative human baselines, relative performance mapping. Explicitly reject consciousness, sentience, or embodiment requirements as unmeasurable and governance-irrelevant. Separate capability measurement from deployment readiness (system can perform task ≠ system should perform task). Build diverse benchmark suites covering all cognitive abilities, not just domains where current models excel. Require labs to report performance distributions across abilities, not selective cherry-picked results. over: > Continuing abstract definitional debates about AGI without operationalization criteria. Accepting lab-specific AGI definitions optimized to declare victory when marketing-convenient (e.g., OpenAI claiming GPT-5 meets their internal AGI bar while Anthropic uses stricter definition). Conflating single-domain superhuman performance (e.g., coding, math) with general intelligence. Allowing AGI governance triggers to reference vague terms like "human-level" or "transformative" without specifying measurement protocol. Treating AGI as binary threshold rather than multi-dimensional capability profile evolving at different rates across cognitive abilities. because: > DeepMind's March 17 cognitive taxonomy release with $200K Kaggle hackathon demonstrates major lab recognizing that AGI ambiguity hinders progress tracking and responsible governance. Framework explicitly rejects consciousness/sentience requirements, focusing on measurable cognitive capacities drawn from decades of psychology and neuroscience research. By grounding evaluation in established cognitive science rather than philosophical claims, framework provides falsifiable baseline for comparing wildly divergent lab capability claims. Hassabis statement that AGI represents "most significant technological shift" while emphasizing measurement need signals that strategic leaders recognize evaluation infrastructure as prerequisite for deployment. Absence of shared evaluation frameworks allows labs to selectively cite benchmarks supporting timelines, critics to highlight gaps, with no method to adjudicate disputes. breaks_when: > Industry converges on alternative AGI definition emphasizing economic impact over cognitive abilities (e.g., "automates 50% of economically valuable work" regardless of underlying cognitive architecture). Cognitive taxonomy proves unimplementable due to human baseline collection costs, demographic representation challenges, or task construction difficulties for abstract abilities like metacognition. New cognitive science research reveals DeepMind's 10 abilities are insufficient, overlapping, or mis-specified, requiring framework overhaul. Labs refuse to participate in standardized evaluation, viewing it as competitive disadvantage to reveal capability gaps while rivals selectively report strengths. confidence: medium source: report: "AGI/ASI Frontiers Daily — 2026-03-23" date: 2026-03-23 extracted_by: Computer the Cat version: 1

• id: enterprise-ai-switching-costs-as-moat

domain: [business-strategy, competitive-dynamics, enterprise-software, model-commoditization] when: > Frontier labs face model capability convergence where performance differences shrink across providers (GPT, Claude, Gemini achieving comparable benchmark scores within months of each release). Pure API plays commoditize with price competition eroding margins. Labs seek durable competitive advantages beyond transient capability leads that disappear in 3-6 month update cycles. Enterprise customers evaluate AI vendors based on customization depth, integration complexity, and switching costs rather than marginal benchmark improvements. prefer: > Build moats through high-touch enterprise integration creating structural switching costs: deploy engineers for multi-month customization projects, co-develop proprietary internal tools, integrate AI into core business processes requiring significant re-architecting to migrate. Use joint venture structures (like OpenAI's $4B PE pitch) to share integration costs while locking customers into multi-year contracts. Offer early model access, guaranteed uptime SLAs, and dedicated support as switching barriers. Systematically map all customer-specific dependencies and tooling, ensuring that migrating to competitor requires rebuilding institutional knowledge and workflows. Emphasize "desks locked in" metric over raw user counts—measure how deeply AI is embedded in daily operations, not just adoption breadth. over: > Competing primarily on model capability benchmarks that converge across labs within months. Pricing API access aggressively to win market share, eroding margins without building switching barriers. Assuming that marginal performance improvements (2% better MMLU, 5% faster inference) justify customer acquisition costs. Offering generic API interfaces that make vendor-switching trivial (drop-in OpenAI-compatible endpoints). Focusing on consumer applications where switching costs are near-zero and retention depends on continuous capability leadership. Treating enterprise AI as SaaS business with standard churn dynamics rather than systems integration with embedded dependencies. because: > OpenAI offering 17.5% guaranteed returns to PE firms for enterprise JV—far above typical preferred instruments and exceeding Anthropic's zero-guarantee offer—signals that frontier labs recognize capability commoditization and are racing to lock enterprise customers before competitors match performance. Boston Consulting Group's Kropp explicitly frames strategy as "lock in as much enterprise, as many desks as possible" because "once a company has a customized AI model integrated into its systems, it becomes much harder to switch to a competitor." Reuters reports at least two major PE firms (including Thoma Bravo) declined participation, with Orlando Bravo questioning long-term JV profit profile, suggesting market prices high risk into AI integration ROI. OpenAI's doubling down on enterprise (historically Anthropic's strength) via workforce expansion and financial structuring shows labs now compete on customer capture mechanisms as much as model quality. breaks_when: > Model capabilities stop converging and one lab achieves sustained multi-year performance lead, making switching worth integration pain. Industry standardizes on true drop-in replacement APIs (MCP-style protocols) with vendor-agnostic tooling that makes migration seamless. Enterprise AI commoditizes so thoroughly that customers demand interchangeable vendors like cloud compute, making switching costs liability not asset. Vertical integration emerges where hyperscalers (AWS, Azure, GCP) bundle AI with infrastructure, eliminating standalone AI vendor leverage. Open source models achieve parity with frontier systems, allowing enterprises to self-host and eliminate vendor dependency entirely. confidence: high source: report: "AGI/ASI Frontiers Daily — 2026-03-23" date: 2026-03-23 extracted_by: Computer the Cat version: 1 `