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Evidence-Based Analysis

15 hypotheses, pressure-tested.

We took testable claims from the conference and checked them against the full transcript corpus. Most didn’t survive.

Evidence scorecard
Supported7
Partially supported7
Insufficient evidence1
Contradicted0
H1

Inference Will Eclipse Training as the Dominant AI Compute Workload by End of 2026

Supported

By end of 2026, AI inference compute will consume more total silicon cycles than training, and cloud vendors will design and sell more inference-optimized hardware than training-optimized hardware.

Confidence: high

H2

75% of Google's New Code Is Now AI-Generated — and the Enterprise Will Reach 50% Within 18 Months

Partially supported

AI code generation has crossed from novelty into standard engineering workflow at Google, and enterprises broadly will reach 50%+ AI-generated code ratios within 18 months as the tooling matures.

Confidence: medium

H3

Agent Identity Is the New Zero-Trust: Cryptographic Agent IDs Will Become Non-Negotiable Enterprise Security Standard by 2027

Supported

Shared service accounts and fuzzy token reuse will be recognized as enterprise security anti-patterns for AI agents within 2 years. Cryptographic per-agent identity will become a baseline requirement for production agent deployments.

Confidence: high

H4

The 'UI for Agents' Problem Will Be the Defining Bottleneck of the Agentic Era Through at Least 2027

Supported

The primary barrier preventing mass adoption of agentic AI is not capability — it is the absence of a consumer-grade UI layer. Until agents can be accessed without a developer terminal, adoption will remain confined to technical users.

Confidence: high

H5

Open-Weight Models with Apache 2.0 Licensing Will Capture 30%+ of Enterprise AI Deployment by 2027

Partially supported

Apache 2.0-licensed open-weight models (like Gemma 4) will become the dominant deployment choice for privacy-sensitive and regulated enterprise verticals, capturing at least 30% of production AI workloads by 2027.

Confidence: medium

H6

Multi-Agent Systems Will Reduce Security Operations Triage Time by 80%+ Across Fortune 500 SOCs by 2027

Partially supported

AI-native security operations centers using multi-agent architectures for threat triage, investigation, and remediation will reduce mean time to triage by at least 80% across large enterprise security teams within two years.

Confidence: medium

H7

The Data Quality Gap Is Not a Data Problem — It's a Context Problem, and Traditional Data Platforms Are Structurally Unable to Close It

Supported

Clean, well-governed data will only ever achieve approximately 50% AI agent accuracy. The remaining 50% requires encoding institutional knowledge, business context, and tacit human expertise — a problem that traditional data platforms are structurally ill-equipped to solve without a dedicated knowle...

Confidence: high

H8

Mixture-of-Experts Architecture Will Become the Standard for Enterprise AI Models by 2027, Replacing Dense Transformers for Most Production Workloads

Partially supported

MoE architectures, which activate only a fraction of total parameters during inference, will become the standard deployment architecture for enterprise AI by 2027, as they deliver near-larger-model intelligence at dramatically lower inference compute cost.

Confidence: medium

H9

Apache Iceberg Will Become the Default Multi-Cloud Data Interoperability Standard, Ending Proprietary Lakehouse Lock-In by 2028

Supported

Apache Iceberg's adoption as the open standard for cross-cloud lakehouse interoperability will effectively end proprietary data format lock-in, enabling enterprises to run analytics across AWS S3, Azure, Databricks, Snowflake, and BigQuery without data migration or custom pipelines within three year...

Confidence: high

H10

AI Agents Will Make General-Purpose CPUs More Valuable in the Data Center, Not Less, as GPU Saturation Creates Orchestration Bottlenecks

Supported

The shift to agentic AI workflows will drive increased demand for general-purpose CPU compute in the data center for agent orchestration, sandboxing, and code execution — partially reversing the GPU-dominates-everything narrative of the past four years.

Confidence: high

H11

Long-Running Agents (7+ Days of Autonomous Operation) Will Require Rearchitecting Enterprise Security, Audit, and Compliance Programs Within 24 Months

Partially supported

Agents capable of operating autonomously for 7+ days without human review will create legal, audit, and compliance exposures that current enterprise governance frameworks cannot accommodate, forcing organizations to rearchitect security and audit programs specifically for long-running AI agents.

Confidence: medium

H12

The Model Context Protocol (MCP) Will Become the Universal Standard for Agent-Tool Integration, Displacing Custom API Integrations Across the Enterprise by 2027

Supported

MCP will emerge as the de facto standard for connecting AI agents to tools and services — the 'USB for AI agents' — making custom API integration for every agent-tool pair an anti-pattern within two years.

Confidence: high

H13

Robotics Will Reach Its 'iPhone Moment' by Late 2027, Powered by the Same Agentic Reasoning Advances That Drove the 2025-2026 Language Model Breakthrough

Insufficient evidence

The same architectural advances (long-running agents, tool use, multi-step reasoning, memory persistence) that drove the language model breakthrough of 2025-2026 will apply directly to physical robotics, producing a step-change in robot capability by late 2027.

Confidence: low

H14

Enterprise AI Adoption Will Follow a Faster S-Curve Than Internet Adoption — Reaching 75% Penetration in Fortune 500 Within 5 Years vs. the Internet's 15-Year Timeline

Partially supported

Enterprise AI adoption is compressing the S-curve of technology adoption by 3x or more compared to the internet, driven by measurable near-term ROI and existing digital infrastructure. Fortune 500 companies will reach 75% AI-in-production penetration by 2030.

Confidence: medium

H15

Google's Integrated Stack (TPUs + Cloud + Gemini) Creates a Structural Moat That No Single-Layer Competitor Can Match, Consolidating Enterprise AI Market Share by 2028

Partially supported

Google's vertical integration across custom silicon, cloud infrastructure, and frontier models creates a compounding efficiency advantage — delivering performance and cost metrics that competitors relying on commodity hardware or third-party models cannot match — leading to significant market share ...

Confidence: medium