A useful economic theory must be capable of being wrong. The April 2026 paper publishes the canonical empirical tests in Section 11B. This monitor records supportive evidence, counter-signals and boundary conditions as they appear, while keeping those current signals separate from the longer test horizons.
0
contradictory observations
12
observations that contradict, bound or qualify
32
observations in total
No directly contradictory evidence has been recorded so far. That is a finding about a short observation window and should not be read as a result. The monitor continues to search explicitly for counterevidence now. Section 11B supplies the longer empirical horizons against which persistence can be assessed over time.
Paper tests and operational counterevidence searches
The paper test is the canonical Section 11B test and horizon. The operational counterevidence search is a monitor instruction derived from that paper framing so contrary signals can be found immediately. It is not presented as verbatim wording from the April paper.
Proposition
Paper test / horizon
Operational counterevidence search
Status
MechanismHuman-Bounded Progression
Section 11B holds that once autonomous capability is credibly deployed in commercially meaningful ways within a sector, revenue models calibrated to HBP, HBI or CSD should come under pressure. Continued durability without redesign while margins hold or expand over roughly one to two years would weaken the mechanism.
Search for sectors where realised autonomous capability materially reduces human progression but the revenue model calibrated to that progression remains durable without redesign, while margins hold or expand. Treat early persistence as a counter-signal and never as a completed falsification test.
Multiple post-publication observations consistent with proposition
MechanismHuman-Bounded Interaction
Section 11B holds that once autonomous capability is credibly deployed in commercially meaningful ways within a sector, revenue models calibrated to HBP, HBI or CSD should come under pressure. Continued durability without redesign while margins hold or expand over roughly one to two years would weaken the mechanism.
Search for markets where agents materially mediate or bypass human interaction but interaction-dependent commercial structures remain durable without redesign, while pricing power and margins hold. Treat partial delegation or retained human approval as an observed boundary, which is not automatically a contradiction.
Multiple observations consistent with proposition
MechanismCapability Scarcity Dependence
Section 11B holds that once autonomous capability is credibly deployed in commercially meaningful ways within a sector, revenue models calibrated to HBP, HBI or CSD should come under pressure. Continued durability without redesign while margins hold or expand over roughly one to two years would weaken the mechanism.
Search for domains where access to previously scarce capability has materially broadened or become substitutable but the affected scarcity-dependent commercial structures remain durable without redesign. Frontier premiums surviving alongside diffusion are not, by themselves, a contradiction.
Multiple post-publication observations consistent with proposition
LawValue moves from production to orchestration
Section 11B holds that as autonomous throughput increases, value should accumulate toward the layers that remain economically constraining. If value does not move toward orchestration, verification, governance, absorption and judgement within roughly three to five years of credible deployment in a major affected sector, the Three Laws would need to be reconsidered.
Search for major sectors where autonomous throughput has changed production materially but value does not reaccumulate toward orchestration, coordination, integration or governance over time. Record present-day counter-signals immediately; reserve framework-level falsification for the paper's three-to-five-year test.
Multiple post-publication observations consistent with proposition
LawValue moves from access to outcomes
Section 11B holds that as autonomous throughput increases, value should accumulate toward the layers that remain economically constraining. If value does not move toward orchestration, verification, governance, absorption and judgement within roughly three to five years of credible deployment in a major affected sector, the Three Laws would need to be reconsidered.
Search for major sectors where autonomous capability has credibly changed activity, interaction or capability access but access remains an equally reliable basis for economic defence and outcomes do not gain weight in value assessment over time. Seat growth or hybrid pricing today is a signal to track, and is insufficient falsification by itself.
Multiple post-publication observations consistent with proposition
LawValue concentrates where scarce judgement governs autonomous throughput
Section 11B holds that as autonomous throughput increases, value should accumulate toward the layers that remain economically constraining. If value does not move toward orchestration, verification, governance, absorption and judgement within roughly three to five years of credible deployment in a major affected sector, the Three Laws would need to be reconsidered.
Search for major sectors where autonomous throughput scales but scarce judgement, authorisation, accountability, supervisory responsibility and risk ownership do not become economically more important. Commoditisation of generic guardrail software alone does not satisfy this test.
Multiple post-publication observations consistent with proposition
PropositionAgentic Profit Paradox
Section 11B holds that firms that adopt autonomous capability without redesign should, over time, face growing difficulty capturing value through structures calibrated to HBP, HBI or CSD. If firms across multiple sectors preserve those structures and still expand margins without value reaccumulating in orchestration, verification, governance, absorption or judgement over roughly two to three years, the Agentic Profit Paradox would be called into question.
Search for firms across multiple sectors that adopt meaningful autonomous capability, keep commercial structures calibrated to earlier HBP, HBI or CSD conditions, and sustain or expand margins without value reaccumulating in orchestration, verification, governance, absorption or judgement over the paper's two-to-three-year test.
Multiple observations consistent with proposition
PropositionCommercial alignment
Section 11B holds that firms that adopt autonomous capability without redesign should, over time, face growing difficulty capturing value through structures calibrated to HBP, HBI or CSD. If firms across multiple sectors preserve those structures and still expand margins without value reaccumulating in orchestration, verification, governance, absorption or judgement over roughly two to three years, the Agentic Profit Paradox would be called into question.
Search for comparable firms or sectors where commercial structures remain anchored to earlier activity conditions and still prove as durable as, or more durable than, structures that realign value capture, after allowing for the paper's two-to-three-year firm-level window.
Multiple observations consistent with proposition
PropositionCapital-Structured Autonomy Ecosystems
The paper presents Capital-Structured Autonomy as a network-level extension and does not assign it a separate Section 11B year window. The monitor therefore tracks present evidence of genuine capital-level coordination and its resulting portfolio effects longitudinally without inventing a paper-specified deadline.
Search for genuinely capital-coordinated autonomy ecosystems, excluding companies that are merely PE-owned and using AI, and test whether the coordinating layer creates reusable capability, shared learning, governance or measurable portfolio-level advantage. Absence of value from scattered portfolio AI adoption does not by itself challenge this concept.
Predicted mechanism directly observed
PropositionRevenue Durability effects
Section 11B holds that once autonomous capability is credibly deployed in commercially meaningful ways within a sector, revenue models calibrated to HBP, HBI or CSD should come under pressure. Continued durability without redesign while margins hold or expand over roughly one to two years would weaken the mechanism.
Search for exposed revenue structures whose underlying activity, interaction or capability-access basis has materially changed but which remain durable without redesign while pricing, retention and margins remain strong over the applicable period.
Multiple observations consistent with proposition
Quarterly falsification review
Once a quarter the register is examined from the opposite direction. A separate pass, with its own instructions and its own queries, is asked to assume the framework is wrong and build the strongest case against each proposition. It publishes whatever it concludes, without editorial approval. A falsification review its author could shelve would be worth nothing. The editor can answer a review and cannot delete one, so where the two disagree you see both and can judge which is better.
2026-Q4 · reviewed 1 October 2026 · 1 of 3 propositions found genuinely contradicted
Proposition
Strongest case against
Contradicts?
Recommendation
csd
The Business Insider piece on AI's "productivity paradox" (June 2026) reports that despite massive corporate AI spending and demonstrable speed gains at task level, with engineers completing week-long tasks in days, "AI productivity gains haven't transferred to areas beyond coding as neatly" and "researchers say AI gains have yet to consistently translate into greater company productivity, revenue, or profits." Critically, this data covers the claimed one-to-two-year test horizon and encompasses the period after autonomous capability has been "credibly deployed in commercially meaningful ways" (coding, cybersecurity). The article also notes companies are still ramping up AI integration and facing an "automation phase" requiring "upfront investment" before "the bigger unlock" materialises. This suggests that commercial structures dependent on labour scarcity, in domains where AI capability has genuinely expanded access, are not experiencing margin pressure or forced redesign so far, even as capability has demonstrably broadened. The source explicitly states the expected pathway (productivity boost → cost savings → margin defense) has not materialized at company level within this window, despite task-level capability expansion.
No
qualify
hbi
The strongest evidence against the claim comes from the customer service sector, where autonomous agents are operationally deployed but interaction-dependent revenue models persist with pricing power intact. The Business Insider article on AI agents calling customer-service lines (Sept 2026) documents that (1) AI callers "remain a small share of total volume," (2) call centre agents continue to be employed and graded on performance metrics, (3) the existing revenue model, where companies monetise customer support staff, shows no sign of redesign or margin pressure, and (4) functional boundaries persist, as in "only the account holder can close their credit card" and "only the customer can do, so they have to be the one on the line." After credible commercial deployment in a sector where interaction mediation should theoretically reduce HBI dependence most directly, the core revenue structure (labour-intensive customer service scaled by interaction volume) remains structurally intact without pressure to redesign. This is the operational counter-signal at the horizon the test specifies.
Yes
qualify
hbp
The strongest case against the claim comes from the Census Bureau's microstructure study (Bonney et al., April 2026). Among firms actively deploying AI across multiple business functions and making operational investments, employment does decline, but this effect disappears once you control for functional deployment breadth and operational investment. More importantly, 66% of firms use AI purely for task augmentation with no employment reductions (2% report cuts), and worker-task use shows no association with employment decline after controlling for deployment scope. This suggests human progression remains embedded in firms' economic models even where autonomous capability is deployed commercially. The Agent-Centric Enterprise paper (January 2026) reports 92% reduction in audit reporting time but does not claim that audit departments have been eliminated or that revenue models calibrated to human effort have been redesigned, only that the work takes less time. The Demirer et al. "Chaining Tasks" paper (February 2026) confirms that AI steps cluster into chains but emphasises that firms still bundle steps into human jobs and must navigate coordination costs. It predicts reorganisation and does not predict a loosening of human progression dependence. The Babina et al. paper (August 2026) finds productivity gains driven by organisation capital accumulation from AI-skilled jobs, meaning human learning and firm-specific knowledge, which is precisely human-bounded progression. None of these sources report sectors in which autonomous capability has been credibly deployed commercially while revenue models remain calibrated to human progression and durability holds with expanding margins.
No
qualify
"Does not contradict" is a weaker statement than it looks. A claim about slow structural change will read as unsupported for years before it reads as wrong, and the review is instructed to say so and never to manufacture a refutation that is not there.
Evidence against, and evidence unresolved
Contradictory evidence first, then boundary conditions showing where the framework does not apply or does not apply so far, then observations that genuinely support more than one reading.
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.
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.
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.
Thomson Reuters' 2026 Future of Professionals survey, conducted in March and April before the Autonomy Economics publication date, found a clear gap between client expectations and law-firm pricing response. Seventy-one per cent of in-house legal professionals expected outside firms to change their commercial models as AI usage increased, while only 28% of law firms reported pricing changes and 62% said pricing remained unchanged. Because the underlying survey predates publication, this record is treated as contemporaneous background and does not count as post-publication evidence.
HBPLaw 2Commercial alignment
Prediction testedTests whether AI-enabled changes in professional work translate into changes in effort-based commercial units and whether commercial structures adapt alongside activity structures.
AssessmentBoundary conditionStrong
Why it mattersIt provides direct survey evidence that client expectations and law-firm pricing structures are currently out of alignment. That gap is exactly where a boundary observation is useful. Pressure is observable while redesign has not become dominant.
Futurum's 2H 2026 Enterprise Software Decision-Makers Survey finds a split in buyer pricing preferences. Per-seat pricing ranks last for core software at 12.6%, but first for separately billed generative AI functionality at 42.3%, ahead of consumption pricing at 36.6% and outcome pricing at 21.1%.
Law 2
Prediction testedTests whether buyers move consistently away from access-based pricing as autonomous capability expands, or whether uncertainty can preserve seat pricing in some categories.
AssessmentBoundary conditionModerate
Why it mattersThe same buyers express different commercial preferences for core software and AI. That makes the observation more useful than a simple vendor pricing example, because it shows that the commercial unit can depend on the risk characteristics of the category as well as on the underlying technical capability.
Visa's Trust Index found that only 23% of US consumers trusted generative AI to handle payment transactions on their behalf. Trust rose to 61% when Visa was named as the party securing the transaction. The survey was fielded from 26 to 28 May 2026, after publication of Autonomy Economics.
HBI
Prediction testedTests whether autonomous systems displace human interaction at economically consequential transaction points, as well as during search and recommendation.
AssessmentBoundary conditionModerate
Why it mattersThe evidence is useful because it measures consumer willingness after the framework was published and focuses on the payment decision itself. It narrows any reading of HBI that assumes rapid removal of human approval throughout the commerce journey.
Microsoft made Agent 365 generally available as a control plane for AI agents while retaining a per-user commercial unit. The standalone product is priced at $15 per user per month, with each licence covering an individual who manages or sponsors agents, or uses agents to do work on their behalf.
Why it mattersThe observation is useful because the product sits close to the autonomous control layer and still remains priced per user. It narrows any overly simple reading of Law 2 that would expect agentic products to abandon user-based pricing immediately.
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 testedTests whether realised changes in human progression are visible alongside pressure on effort-based pricing, while recognising that aggregate hours are not the same measure as effort per unit of work.
AssessmentAmbiguousStrong
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 instead of an observed reduction in effort.
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.
Vercel's September 2026 AI Gateway Production Index shows open-weight models processing 56% of gateway token volume in August while accounting for 14% of estimated spend. Average price per token fell 23.2% during the month, while Anthropic retained 64% of gateway spend. The same dataset therefore shows both rapid diffusion of lower-cost capability and persistent concentration of premium spending.
CSD
Prediction testedTests whether broad availability of cheaper and open-weight capability erodes scarcity rents, or whether premium frontier capability continues to sustain concentrated spending.
AssessmentAmbiguousStrong
Why it mattersThe same production dataset contains evidence in both directions. It is therefore useful for defining where CSD may weaken first and where premium capability can remain scarce enough to command materially higher spending.
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.