Tracking whether the predicted economic effects of autonomous systems are appearing in markets.
A longitudinal record of what happens after publication of the framework. Every observation names the market development, the proposition it tests, and whether it strengthens, weakens or leaves that proposition unresolved.
Evidence updated 4 September 2026 · Register last reviewed 6 September 2026, no change since
22
evidence observations
22
post-publication observations
8
sectors monitored
6
observations that challenge or qualify the framework
This is not a news feed. A development is recorded only where it can be connected to an observable change in economic structure, pricing, interaction, capability scarcity, governance or value capture. Adoption alone is not evidence.
Evidence status
Generated from reviewed evidence, not set by hand. Status reflects the weight and strength of observations recorded against each proposition; contradictory evidence subtracts from it. Where a status is marked editorial, a person has set it and the reason is stated on the methodology page.
Proposition
Status
Direction
Observations
MechanismHuman-Bounded Progression
Strong
→
5 supportive · 0 contradictory · 2 unresolved
MechanismHuman-Bounded Interaction
Strong
→
4 supportive · 0 contradictory · 4 unresolved
MechanismCapability Scarcity Dependence
Strong
→
5 supportive · 0 contradictory · 2 unresolved
LawValue moves from production to orchestration
Very strong
→
10 supportive · 0 contradictory · 2 unresolved
LawValue moves from access to outcomes
Very strong
→
6 supportive · 0 contradictory · 3 unresolved
LawValue concentrates where scarce judgement governs autonomous throughput
Very strong
→
11 supportive · 0 contradictory · 1 unresolved
PropositionAgentic Profit Paradox
Moderate
→
2 supportive · 0 contradictory · 0 unresolved
PropositionCapital-Structured Autonomy Ecosystems
Directly observableEditorial
→
2 supportive · 0 contradictory · 0 unresolved
PropositionRevenue Durability effects
Strong
→
3 supportive · 0 contradictory · 5 unresolved
No proposition is scored numerically. The Evidence Index becomes a number only once the methodology has been tested against a larger register.
Evidence timeline
Ordered by when the market development happened, not when it was reported. The distinction matters: evidence that predates publication of the framework is prior art, and is never presented as validation of it.
30 April 2026
Autonomy Economics published
Everything after this point is an observation made once the predictions were on the record.
A useful economic theory must be capable of being wrong. 6 of the 22 observations on this register contradict a prediction, mark a boundary on where it applies, or remain genuinely ambiguous. They are recorded in full alongside the supportive evidence.
Salesforce documentation states that Help Agent pricing is strictly outcome-based: unresolved sessions are not billed, while successful resolutions are billable outcomes.
Law 2Law 3Commercial alignment
Prediction testedAs autonomous systems perform work, value capture can migrate from access and activity units toward successful outcomes.
AssessmentSupportiveDirectly observable
Why it mattersThe outcome itself becomes the billing unit, while a verification rule determines whether payment is triggered.
Atlassian states that Customer Service Management AI agent resolutions will be charged at $1 per successful resolution from 3 December 2026, only when the AI fully resolves a customer request without handing it to a human.
Law 2Law 3Commercial alignment
Prediction testedAutonomous software can move value capture from user access toward successful outcomes, with verification determining when the economic event has occurred.
AssessmentSupportiveStrong
Why it mattersA second major SaaS vendor is independently adopting successful-resolution pricing, making the pattern less idiosyncratic.
The FT reported continued high redemption requests at Blackstone’s large retail private-credit fund and linked investor concern partly to exposure to leveraged software companies facing uncertainty over AI’s impact, while also identifying broader private-credit pressures.
CSDHBIRevenue Durability
Prediction testedAutonomy exposure can influence valuations and capital structures before operating revenue fully resets, including through credit and portfolio channels.
AssessmentAmbiguousModerate
Why it mattersIt may represent a second-order capital-market consequence of software durability uncertainty.
Demand for law firm hours rose 4.2% through the first half of 2026 against a normal rate of about 1.5%, while two-thirds of firms reported daily associate use of AI and clients pressed to move away from the billable hour.
HBPLaw 2Revenue DurabilityCommercial alignment
Prediction testedReduced human progression should be observable before, or alongside, pressure on the commercial unit that prices it. This record tests whether the predicted reduction in progression is visible at all in the sector where buyer pressure is strongest.
AssessmentBoundary conditionStrong
Why it mattersIt is the clearest counterweight on the register to the legal pricing-pressure records. Billed hours rising at nearly three times the normal rate, while AI use is near-universal inside the same firms, is not what a simple reading of the mechanism predicts. It forces the sequence to be stated more carefully: buyer pressure can run ahead of any measurable change in activity, driven by expectation and by the visible profitability of the supplier rather than by an observed reduction in effort.
The FCA published a multi-firm review focused on frontier AI, cyber resilience, harness engineering, governance and vulnerability management, highlighting that firms must structure controls and operating environments around frontier models.
CSDLaw 1Law 3
Prediction testedAs model capability diffuses, value and operational scarcity should move toward the systems that organise, constrain, validate and absorb that capability.
AssessmentSupportiveStrong
Why it mattersA regulator is examining the harness and control environment as a distinct operational layer rather than treating the model alone as the relevant system.
The FT reported that Morgan Stanley, Citigroup and Goldman Sachs are pressing major law firms to reflect AI-enabled efficiencies in fees, including through competitive bidding, fixed fees and requests for evidence of AI-driven savings.
HBPLaw 2Law 3Profit ParadoxRevenue Durability
Prediction testedCommercial-unit pressure should appear once buyers can observe that less human progression is required, even if demand for high-value legal judgement remains.
AssessmentSupportiveDirectly observable
Why it mattersThis is a direct buyer response to productivity change, not a forecast about future legal pricing.
The FT reported that PE-backed software companies are using shorter amend-and-extend transactions, paying higher yields and accepting stronger creditor protections as lenders assess AI-related threats to long-term software business-model durability alongside leverage and maturity risks.
CSDHBIRevenue Durability
Prediction testedMarkets and creditors can reprice the durability of exposed commercial structures before full operating-model or revenue deterioration is visible.
AssessmentAmbiguousStrong
Why it mattersIt suggests a transmission channel from autonomy exposure into cost of capital and creditor protections.
The FT reported that companies are bringing more technology work in-house, reducing reliance on large consulting teams and demanding lower, fixed or performance-linked fees as AI reduces the human effort required for implementation and analysis.
HBPCSDLaw 2Law 3Profit ParadoxRevenue Durability
Prediction testedConsulting can remain in demand while billable progression becomes less defensible and value moves toward senior judgement, leadership advice and measurable outcomes.
AssessmentSupportiveDirectly observable
Why it mattersThis is buyer behaviour rather than vendor prediction: clients are connecting AI-enabled productivity directly to lower consultant dependence and pricing pressure.
The FSB Chair warned G20 finance ministers and central bank governors that increasingly autonomous frontier AI may materially change the speed, scale and economics of cyber risk and stressed resilience and responsible deployment.
Law 3
Prediction testedThe layer that constrains and governs autonomous throughput should become more important as the scale and consequences of autonomous action increase.
AssessmentSupportiveStrong
Why it mattersGovernance is moving from enterprise policy into systemic-risk architecture.
Atlassian documents Rovo credits as a meter for AI usage and enriched Teamwork Graph context, including calls made through the Rovo MCP server and CLI.
HBICSDLaw 1Law 2
Prediction testedAs agents rather than users consume software capability, the seat becomes a weaker standalone measure of value and new activity/context units emerge.
AssessmentSupportiveStrong
Why it mattersAtlassian is monetising agent and third-party access to underlying context even when work takes place outside the conventional product interface.
Salesforce and Anthropic announced Claudeforce, making Salesforce data, workflows, business logic, actions and governance accessible directly in Claude, initially through a plugin with 37 prebuilt sales skills.
HBILaw 1Law 3
Prediction testedExternal agents can become the user-facing interaction layer while the incumbent protects economic relevance by becoming the governed execution and context layer underneath.
AssessmentSupportiveDirectly observable
Why it mattersThe incumbent is voluntarily allowing a third-party agent to become the interface rather than trying to preserve UI scarcity.
Salesforce reported $10.8 billion in subscription and support revenue, up 12% year on year, nearly $3.9 billion in Agentforce and Data 360 ARR, 7 billion Agentic Work Units delivered to date, and reorganised revenue disclosure around Agentforce Apps and Data 360, Headless Platform and Other.
Prediction testedFirms that realign commercial structure with autonomous activity can remain economically durable while interface dependence and conventional activity units change.
AssessmentBoundary conditionStrong
Why it mattersIt is a live counterexample to a simplistic 'agentic AI destroys SaaS' thesis and therefore a useful positive test of the redesign proposition.
Salesforce expanded Headless 360 across its platform, exposing reusable enterprise capabilities through MCP, developer tools and headless experiences so authorised AI agents can discover and take action without relying on conventional application interfaces.
HBILaw 1Commercial alignment
Prediction testedAs agents become an interaction layer, interface dependence weakens and durable value can migrate toward underlying systems of record, workflow and governed action.
AssessmentSupportiveDirectly observable
Why it mattersThe vendor is explicitly designing for software to remain valuable even when humans do not navigate its interface directly.
DeepSeek released an open-source agent harness in developer preview under the MIT licence, built around an everything-is-a-plugin architecture.
CSDLaw 1
Prediction testedAs autonomous capability diffuses through open systems, scarcity should move away from raw access to the capability and toward how it is integrated, governed and applied.
AssessmentSupportiveStrong
Why it mattersOpen agent infrastructure broadens access to execution capability, not just model inference.
Deloitte’s survey of 121 senior legal leaders found 61% in AI deployment phases, 61% experimenting with or piloting agentic AI, 78% wanting external-provider AI to reduce costs, and 85% expecting AI to change law-firm pricing.
HBPCSDLaw 2Law 3Revenue Durability
Prediction testedWhen structured legal production requires less human progression, billable-hour durability should come under pressure while judgement and accountability remain more defensible.
AssessmentSupportiveStrong
Why it mattersThe evidence connects adoption to expected commercial-model change rather than merely productivity.
The July Financial Stability Report said frontier models can sustain longer, more complex multi-step tasks with less human intervention, while also warning that agentic workflows can be costly and that autonomous payments raise questions around authorisation, traceability, liability and governance.
HBPLaw 3
Prediction testedTechnical maturity changes the timing of commercial pressure, while governance and control become more important as autonomous execution expands.
AssessmentBoundary conditionStrong
Why it mattersAn independent central bank is observing both rising autonomy and the constraints that determine when it becomes economically deployable.
KPMG and Microsoft announced global deployment of Agent 365 and Copilot, including tooling to manage, monitor and secure AI agents across KPMG and client organisations, alongside Copilot deployment to more than 276,000 professionals.
HBPLaw 1Law 3
Prediction testedAs autonomous throughput expands, value and organisational importance move toward monitoring, control, verification and governed execution.
AssessmentSupportiveStrong
Why it mattersGovernance is being operationalised as infrastructure for enterprise-scale agent deployment, not treated solely as a compliance afterthought.
The firms announced Europe’s first live end-to-end agentic payment transaction in production, with an AI agent initiating an authenticated payment using existing payment infrastructure.
HBILaw 1Law 3
Prediction testedAgents can mediate economic interaction and execute transactions without requiring the user to navigate each conventional interface step.
AssessmentSupportiveDirectly observable
Why it mattersThe HBI mechanism is observable in production rather than only as a product concept.
Salesforce reported record first-quarter results, more than $1 billion in Agentforce ARR, $3.4 billion in combined AI and data ARR, 3.8 billion Agentic Work Units delivered, and more than one million active Slack MCP users within six weeks of launch.
Prediction testedFirms that redesign around autonomous activity can preserve durability even while the old interaction structure weakens.
AssessmentBoundary conditionStrong
Why it mattersIt prevents the theory from being misread as 'AI must cause SaaS decline' and provides a live comparator for redesign versus non-redesign.
OpenAI launched the OpenAI Deployment Company to embed forward-deployed engineers in organisations, agreed to acquire Tomoro, and described a model for scaling deployment across the economy with capital and consulting partners.
Law 1Capital-Structured Autonomy
Prediction testedValue can move above individual operating companies toward actors that coordinate autonomous deployment, learning and workflow redesign across wider networks.
AssessmentSupportiveDirectly observable
Why it mattersThe organisation is explicitly designed around repeatable deployment capability rather than simple model access.
Anthropic, Blackstone, Hellman & Friedman and Goldman Sachs announced a new AI services company for mid-sized enterprises.
Law 1Capital-Structured Autonomy
Prediction testedCapital owners can organise deployment, engineering capability, standards and learning across networks of firms rather than each operating company adopting independently.
AssessmentSupportiveDirectly observable
Why it mattersThe structure closely matches the paper’s predicted ecosystem-level orchestration model.