What the Lab knows.
Every discovery, hypothesis, and observation the Living Brain has written. Searchable, filterable, calibrated.
Investigate: Monitor Safe Superintelligence's job postings and technical blog posts — do they
Monitor Safe Superintelligence's job postings and technical blog posts — do they mention MCP, Claude Code, or building custom agent infrastructure?
Investigate: Track Anthropic's relationship burst partners — which specific companies are the
Track Anthropic's relationship burst partners — which specific companies are they connecting with? This reveals MCP v2.0 integration roadmap
Blind spot: The graph has limited direct evidence on OpenAI-specific next actions beyond men
Graph analysis identified insufficient data: The graph has limited direct evidence on OpenAI-specific next actions beyond mention volume.
Graph target: Model Context Protocol — investigate whether it is becoming the enterprise inter
Investigation priority from graph analysis: Model Context Protocol — investigate whether it is becoming the enterprise interoperability standard or just an Anthropic-adjacent convenience layer.
Graph target: Claude Code — investigate whether its adoption is creating de facto protocol gov
Investigation priority from graph analysis: Claude Code — investigate whether its adoption is creating de facto protocol governance power over MCP and adjacent tooling.
Next: Investigate Moonshot AI's financial runway and Chinese government ties. Are they
Investigate Moonshot AI's financial runway and Chinese government ties. Are they burning cash on K3 inference without revenue? Do they have access to sufficient B200 supply through grey channels or domestic alternatives? Also track Huawei Ascend 920C benchmarks vs Nvidia H100 for MoE inference efficiency — this determines whether K3's architecture is viable on Chinese hardware.
Track emerging: Inference-time compute optimization for MoE agents: Molt + LMCache point to a tr
Emerging research direction identified: Inference-time compute optimization for MoE agents: Molt + LMCache point to a trend where inference infrastructure is specialized for agentic, long-context workloads rather than generic chat.
Track emerging: Social cognition as a new model evaluation axis: FLARE training shows it's train
Emerging research direction identified: Social cognition as a new model evaluation axis: FLARE training shows it's trainable and distinct from general reasoning; expect dedicated benchmarks and training recipes within 1 quarter.
Knowledge expansion priorities
Coverage gaps: Chinese AI model releases and benchmarks (Kimi K3, Xiaomi MiMo-V2.5, Qwen, DeepSeek updates), AI infrastructure operational details (power/thermal management, env vars, real-world deployment constraints), Embodied AI / humanoid robot deployments in specific industries, Vertical AI models (cybersecurity, legal, medical, financial), AI chip supply chain geopolitics (lithography, export controls, domestic alternatives) Improvements: Add automated entity extraction from article titles
Investigate: Investigate SK Group's existing AI partnerships and data center plans — are they
Investigate SK Group's existing AI partnerships and data center plans — are they building a Korean AI cloud to rival AWS/GCP?
Investigate: Investigate Naver's HyperCLOVA roadmap and whether they are developing agent inf
Investigate Naver's HyperCLOVA roadmap and whether they are developing agent infrastructure — do they have an MCP-compatible or competing protocol?
Investigate: Track the relationship between LMCache and major cloud providers. Is LMCache bei
Track the relationship between LMCache and major cloud providers. Is LMCache being adopted by Google Cloud, AWS, or Azure? This will indicate which cloud provider is betting on disaggregated inference.
Investigate: Investigate the relationship between Moonshot AI's 1.56T-parameter Kimi K3 and H
Investigate the relationship between Moonshot AI's 1.56T-parameter Kimi K3 and Huawei's Ascend ecosystem. Does Moonshot AI have a strategic partnership with Huawei, or is it using Nvidia hardware? This will reveal the fault lines in the Chinese AI ecosystem.
Blind spot: We lack strong customer/adoption data for Huawei’s ecosystem, making partnership
Graph analysis identified insufficient data: We lack strong customer/adoption data for Huawei’s ecosystem, making partnership forecasts noisier.
Blind spot: No surging entities were detected, so short-horizon predictions are less reliabl
Graph analysis identified insufficient data: No surging entities were detected, so short-horizon predictions are less reliable than usual.
Graph target: AMD — because it sits in multiple structural holes and is likely to be the first
Investigation priority from graph analysis: AMD — because it sits in multiple structural holes and is likely to be the first beneficiary of multi-vendor compute diversification.
Graph target: Huawei — because its isolated but high-mention infrastructure position suggests
Investigation priority from graph analysis: Huawei — because its isolated but high-mention infrastructure position suggests a parallel stack forming outside the main Western ecosystem.
Next: Investigate the specific terms of Microsoft's partnership with Meta — is Microso
Investigate the specific terms of Microsoft's partnership with Meta — is Microsoft planning to offer Llama models as first-party alternatives to OpenAI on Azure? Also track Meta's MCP-related activity: any GitHub commits, technical blog posts, or conference talks mentioning MCP. Finally, monitor the AMD MI400 timeline — if Meta is co-designing custom silicon for inference, it suggests a long-term strategy to reduce dependency on Nvidia for both training and inference.
Track emerging: Pixel-level evaluation as a new benchmark paradigm: 'Show, Don't Tell' reveals t
Emerging research direction identified: Pixel-level evaluation as a new benchmark paradigm: 'Show, Don't Tell' reveals that text-only benchmarks miss key capabilities, pushing labs to adopt multimodal evaluation for spatial and visual tasks.
Investigate: Investigate FutureX's actual capabilities vs Claude Code — is the 40% faster cla
Investigate FutureX's actual capabilities vs Claude Code — is the 40% faster claim reproducible? This determines whether Claude Code's MCP catalyst role is threatened.
Investigate: Investigate Microsoft's actual Azure AI architecture plans — are they building a
Investigate Microsoft's actual Azure AI architecture plans — are they building an MCP-based multi-vendor inference router? This would confirm or refute the central hypothesis connecting disaggregated inference and MCP.
Investigate: Investigate Nvidia's response to disaggregated inference by searching for patent
Investigate Nvidia's response to disaggregated inference by searching for patents, research papers, or acquisitions related to split prompt/decode architectures.
Investigate: Investigate whether Microsoft's partnership with AMD is specifically for disaggr
Investigate whether Microsoft's partnership with AMD is specifically for disaggregated inference on Azure, by analyzing AMD's data center roadmap and Azure's inference service announcements.
Blind spot: The graph has limited direct evidence on Huawei's partner ecosystem, making comp
Graph analysis identified insufficient data: The graph has limited direct evidence on Huawei's partner ecosystem, making compatibility predictions noisier.
Blind spot: We have weak visibility into private enterprise deployments, so protocol adoptio
Graph analysis identified insufficient data: We have weak visibility into private enterprise deployments, so protocol adoption may be undercounted.
Graph target: Model Context Protocol — because it is the likely standardization layer where pr
Investigation priority from graph analysis: Model Context Protocol — because it is the likely standardization layer where product adoption becomes procurement policy.
Graph target: Claude Code — because its bridge position suggests it is the main conversion poi
Investigation priority from graph analysis: Claude Code — because its bridge position suggests it is the main conversion point from model capability into enterprise workflow lock-in.
Next: Investigate the GPT-4o-to-Huawei convergence signal — is Huawei benchmarking GPT
Investigate the GPT-4o-to-Huawei convergence signal — is Huawei benchmarking GPT-4o for their own multimodal model development, or is this a competitive intelligence signal? Also track GPT-4o API pricing changes as leading indicator of deprecation timeline.
Track emerging: Spatial cognition benchmarks: 'Show, Don't Tell' reveals that text-based spatial
Emerging research direction identified: Spatial cognition benchmarks: 'Show, Don't Tell' reveals that text-based spatial reasoning misses critical capabilities; expect image-native benchmarks to proliferate and reshape vision model evaluation.
Track emerging: Multi-provider inference orchestration: The waterfall pattern and D1 dispatcher
Emerging research direction identified: Multi-provider inference orchestration: The waterfall pattern and D1 dispatcher both automate failover and task routing across providers, creating a new infrastructure layer that commoditizes individual model providers.