Generative AI
What generative AI is worth to a business depends on the work it makes possible, more than on user numbers. METR's 2026 survey finds technical workers reporting gains themselves, which is short of evidence for productivity across the economy. Model quality, workflow design and distribution still have to be weighed against the cost and reliability of a useful result.
Research snapshot · updated Nov 12, 2026
Core thesis
Text, image, video, audio, and code generation are production capabilities embedded in everyday consumer and enterprise workflows. Frontier model quality has largely converged across the leading labs, so the strategic value is migrating up the stack – to agentic, tool-using workflows and to whoever controls distribution, proprietary data, and the end-user relationship. Undifferentiated apps that merely wrap a model API face commoditisation as inference prices fall; the durable layer is platforms with embedded workflows, distribution, or data.
State of the art (2026)
Generative AI is in everyday use at work and at home. The leading models come from Anthropic, OpenAI, Google and xAI, and each is updated every few months, so rankings change often. The Artificial Analysis Intelligence Index, a widely quoted ranking, mainly tests English text. Image, speech and other languages are scored separately, so a lead on that index is a lead in one area. Value is moving from chat to tools that carry out tasks inside products such as Microsoft 365 Copilot and Salesforce Agentforce. The open questions are what a useful result costs to produce, how the copyright cases against model makers are decided, and whether the large cloud companies' reach squeezes independent apps.
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Signal stack
Evidence stacked leading → lagging
12 signals
Signal stack
Evidence stacked leading → lagging
Technology-native KPIs
Metrics that predict trajectory, tracked over time
4 tracked
Technology-native KPIs
Metrics that predict trajectory, tracked over time
Landscape map
Who builds what, and who depends on whom
217 players · 6 layers
Landscape map
Who builds what, and who depends on whom
Catalyst calendar
Dated events that will move the position
4 ahead
Catalyst calendar
Dated events that will move the position
Technology roadmap
Milestones on the path to maturity
8 milestones
Technology roadmap
Milestones on the path to maturity
Watchlists
Companies, people and papers, each with a condition for removing it
20 · 3
Watchlists
Companies, people and papers, each with a condition for removing it
Decision frameworks
What our view means for your job
Locked
Decision frameworks
What our view means for your job
Thesis changelog
When our view changed, and why
8 updates
Thesis changelog
When our view changed, and why
Change our mind
3 disconfirming conditions
You've read the verdict. The file is much deeper.
The full signal stack, technology-native KPIs tracked over time, the landscape of who depends on whom, the dated catalyst calendar, decision frameworks for every desk, live watchlists and a record of every time our view on Generative AI has changed. All of it is in CanaryIQ.