News peg: The IMF’s agentic payments policy note (April 24, 2026) and Amazon’s AgentCore Payments launch (May 7, 2026).

On May 11th, a developer with 40+ production agent deployments posted in r/AI_Agents: “Stop building AI agents.” It hit 376 upvotes. The comments were full of people who learned the same lesson the hard way.
Agents hallucinate. They resist auditing. And in regulated industries, that’s becoming a legal liability.

The AI agent market is maturing, and maturation is uncomfortable. The gap between what agents do in demos and what they do in production has become impossible to ignore, and the developers closest to the problem are saying so out loud.

On May 11th, the top post on r/AI_Agents, from a developer with over 40 production deployments, put it plainly: stop building AI agents. Automations outperform agents in production. Agents hallucinate, resist auditing, and collapse on unexpected inputs. In regulated industries, compliance reviewers need deterministic audit trails. Autonomous black boxes fail that requirement. The post drew 376 upvotes and a thread full of agreement from people who had learned the same thing the hard way.

This is not a fringe opinion. Surveys of teams running agents in production show 60% operating with no formal governance framework. Thousands of applications have leaked sensitive data because agentic systems shipped without deterministic audit infrastructure underneath them. Y Combinator’s current Request for Startups names agents with audit trails, deterministic behavior, and compliance capabilities as explicit investment priorities, which is the VC tier funding around a failure mode they are watching play out in real time.

The market is converging on a conclusion: the winners will be builders who ship reliable automations with proper guardrails, not the loudest agentic demos.

Why Auditability Is Harder Than It Looks

The production problems are not all unsolvable. Structured outputs, function calling, evaluation harnesses, and hybrid architectures that wrap LLM reasoning inside deterministic workflows have all made agents meaningfully more reliable. These approaches work, and teams shipping in regulated industries are using them.

The harder problem is what happens to the record of what occurred. When an LLM-orchestrated agent produces a log of its actions, that log is itself a language model output: a reconstruction, produced by the same probabilistic engine that executed the action. It can be internally consistent, confidently written, and factually wrong. Better reliability practices reduce the frequency of errors; they do not change the nature of the record.

The IMF’s recent policy framework gestured at this without quite landing on it. It proposed separating probabilistic AI decision-making from deterministic payment execution, which is a sound instinct. But the framework assumes that once execution happens, the record of what happened is trustworthy enough to trace backward through. That assumption is where it gets complicated. AWS’s AgentCore Payments infrastructure, launched in preview this month, stores logs in CloudWatch: whatever the agent reports, observable but not independently verified.

Trusted Execution Environments address part of this. TEEs are hardware-isolated compute regions where remote attestation can cryptographically prove which model ran against which input. That closes a real gap: you can verify the execution environment was not tampered with. What TEE attestation does not cover is whether the log the model generated faithfully represents the sequence of decisions across a multi-step workflow. The hardware integrity is provable; the semantic accuracy of the record is not.

This is the specific gap that structured outputs and better observability tooling do not reach. It is not an argument that those approaches are inadequate for most purposes. It is an argument that for regulated industries where the log is evidence, the reconstruction problem is still open.

What the Correction Selects For

MIT economist Christian Catalini, writing independently in Some Simple Economics of AGI, identifies verification-grade infrastructure as the foundational requirement of the agentic economy: systems where agent actions produce receipts that travel with the data, verifiable by any party. When that infrastructure is absent, agents exploit the gap between what is measured and what was intended. The endgame is legal and financial accountability; agent outputs that can be defended, insured, adjudicated.

The legal pressure is already arriving. A class action alleging UnitedHealth’s AI model had a 90% error rate on appealed claim denials is in federal discovery, with tens of thousands of internal documents being produced. The 90% figure is an allegation, not an adjudicated finding, and UnitedHealth disputes it. But the shape of the case is instructive regardless of outcome: logs existed, assembled after the fact from records that did not travel with the decisions, and a court order was required to surface them. The question is not whether logs exist. It is whether they are accurate and whether the organization can prove it.

The EU Product Liability Directive classifies AI as a product subject to strict liability, effective December 2026. Gartner tracks AI governance platform spending reaching roughly $500 million in 2026, rising steeply as regulatory pressure increases. The organizations that will navigate this cleanly are the ones that built for auditability before it became a legal requirement, not after.

What Verifiable Infrastructure Actually Requires

Solving the reconstruction problem means removing the reconstruction step. The record of what happened needs to be bound to the transaction at the moment of execution, before any language model generates a summary of it. Several architectural approaches point in this direction: deterministic workflow engines that log at the infrastructure layer rather than the application layer, blockchain-based ledgers where transaction records are independently verifiable, and cryptographic provenance systems where proof of what occurred travels with the asset itself rather than being stored separately.

The TODA-file protocol takes the last approach. Provenance is bound at execution and verifiable by any party with no intermediary. The formal proof underlying the protocol, published by researchers at Cambridge’s Centre for Redecentralisation, uses structural induction to demonstrate that double-spend is not merely unlikely but excluded by the system’s structure. That is a meaningful property for regulated use cases where the audit trail needs to hold up under legal scrutiny rather than just operational review.

We are a small operation, for now, relative to the infrastructure being built around x402 and AgentCore. Volume today is thousands of transactions per day against x402’s aggregated 169 million. We note that not because the comparison favors us, but because the question worth asking of any agentic payment infrastructure is not just how much it has processed, but what it can prove about each transaction when asked. That question will become harder to avoid as the UnitedHealth discovery process continues and the EU Product Liability Directive takes effect in December. The organizations positioned to answer it will be the ones that treated auditability as a design requirement rather than a compliance checkbox added afterward.

Subscribe now

Sources

1. IMF Policy Note, How Agentic AI Will Reshape Payments, Sonja Davidovic and Hervé Tourpe, April 24, 2026.

2. Amazon Web Services, Agents That Transact: Introducing Amazon Bedrock AgentCore Payments, May 7, 2026. Launched in preview.

3. x402 Foundation / Linux Foundation press release, April 2, 2026. By May 7, 2026 (per Coinbase): 69,000 active agents, 169 million transactions.

4. Estate of Gene B. Lokken v. UnitedHealth Group, Case 0:23-cv-03514-JRT-SGE. The 90% error rate is an allegation in the plaintiff complaint, not an adjudicated finding. UnitedHealth disputes the characterization.

5. Gartner AI governance platform spending figures, 2026. Gartner projects the market reaching approximately $492 million in 2026, growing toward $1 billion by 2030, driven by regulatory requirements. [Available via Gartner subscription.]

6. EU Product Liability Directive (2024/2853): implementation by December 9, 2026; includes AI software as a “product” subject to strict liability.

7. TODA Rigs Architecture, Formal Proof, Cambridge University CRDC. Kris Coward and Dann Toliver, co-authors of Rigging Specifications (T.R.I.E., 2023).

8. Catalini, Hui & Wu, Some Simple Economics of AGI, 2025.

9. r/AI_Agents, May 11, 2026. Top post (376 upvotes): “Stop Building AI Agents.”

10. On TEEs and LLM attestation: OLLM (Trusted Execution Environments in Confidential AI, 2026); Attestable Audits (Verifiable AI Safety Benchmarks Using Trusted Execution Environments, arXiv 2025).

Leave a comment

Prior posts in this series:

• Built Before It Was Named

• An MIT Economist Just Named the Gap

• Everyone’s Building AI Agents — Nobody’s Solved This

• The Payment Layer for the AI Economy

There’s a moment in every infrastructure cycle when the thing everyone said was coming actually arrives. Not as a roadmap slide or a whitepaper. As a tool you can install.

That moment is now.

The conversations we’ve been tracking, and in some cases, seeding over the past several months have all pointed at the same structural gap. AI agents are executing at machine speed. They’re calling dozens of services per task, making purchasing decisions autonomously, and chaining outputs across providers without a human in the loop. The compute infrastructure for this is largely in place. The model capability is moving fast. What wasn’t in place, until now, was the payment layer that makes agent-to-agent commerce actually work.

We built that layer. We called it TAPP. We’ve been running it in production since 2023, and we’ve written about why it matters for AI companies, for creators, for the economics of microtransactions that legacy rails make structurally impossible.

What we hadn’t done was give it a front door that any developer, any agent runtime, any MCP-compatible tool chain could walk through in three steps.

That’s Qatom.

The Standard That Made This Possible

Agentic AI tooling has, in the last twelve months, begun converging on a standard. The Model Context Protocol (MCP) has become the lingua franca of how agents discover and call external tools. If you’re building agents in 2026, you’re either already building on MCP or you’re about to be. The ecosystem is moving fast and it’s moving in one direction.

MCP solved a real problem: a standard interface so agents can talk to tools without bespoke integration work at every junction. What it didn’t solve, what no one in that ecosystem had solved, is how agents pay for what they call.

The attempts have been visible to anyone building in the space. Legacy rails charge 2.9% plus $0.30 on transactions that might be worth three cents and settle days after the service was consumed. From the crypto side, x402, Coinbase’s protocol reviving HTTP’s 402 status code for stablecoin payments, diagnosed the problem correctly and moved fast. But it was built for crypto-native operators, and the enterprises, AI companies, and commercial API providers who run on commercial banking infrastructure and settle in dollars are not its constituency.

Qatom is the MCP server that adds the missing primitive: an agent that can pay. No crypto wallet required. No gas. No facilitator sitting between the payment and the settlement. USD, settled in flight, on TODA underneath.

What It Actually Does

The architecture is worth understanding, because the design choices matter.

Payment in Qatom travels inside the HTTP request itself, a header attached to the API call, settled between counterparties before the response comes back. No callback. No webhook. No reconciliation cycle. The response either arrives paid or it doesn’t arrive. This is the vending machine model we’ve been describing since February, finally expressed as a working MCP primitive: insert payment, receive service, atomically, with no intermediary absorbing margin at every hop.

Underneath that interface is the TODA stack, the same cryptographic infrastructure we’ve been describing since the Built Before It Was Named piece. File-based digital bearer assets, not balances. USD-TDN files that behave like physical cash: portable, self-verifying, carrying their own proof of provenance without depending on any central ledger. No blockchain. No gas. No volatility. Settlement in milliseconds between Twins, with bank rails connected for funding and payout in dollars where it counts.

For a developer or an enterprise AI team, the practical picture looks like this. Your agent gets a wallet. It can check its own balance as a standard MCP tool call. It can discover paywalled APIs in a live marketplace headless catalog, priced per call, not per month. It can pay for what it uses in the same request that calls the tool. And if you’re providing a service, you can register any HTTP API as a payable tool through chat or REST, set your price per call, and define how revenue splits across however many parties share in the value, up to two hundred payees per payout, settled atomically at the moment the call clears.

The fee across all of this is zero. That’s the architecture at work, not a promotional offer, when you remove the intermediary chain that legacy rails require, zero fees is what the math produces.

That’s what the infrastructure does. The question worth sitting with is why the window to build on it is shorter than most people currently assume.

Why the Window Is Narrow

The MCP ecosystem is still early enough that the payment primitive it adopts will become load-bearing infrastructure. The standard that wins here doesn’t just win a product category. It becomes the default assumption baked into every agent runtime, every tool provider, every enterprise deployment that gets built on top.

The history of infrastructure is the history of defaults. Whoever built the card rails didn’t make news in proportion to their eventual importance. AWS launched in 2006 and was considered a developer curiosity. The window to build a position in a new infrastructure layer is narrow, and it closes when the incumbents get entrenched.

That window is why the architectural gaps in what already exists matter now, not later. Demonstrating real traction and demand, x402 has processed over 100 million payments in its first six months. But a formal analysis published this year by researchers across Peking University, Shanghai Jiao Tong University, and Zhongguancun Laboratory (Li et al., “A402,” arXiv 2603.01179) documented structural flaws that x402 v2 left unresolved. Service providers must execute requests before payment confirms on-chain, meaning they absorb non-payment risk on every call. And because end-to-end latency is bounded by blockchain confirmation time, high-frequency interaction is structurally impractical; on-chain fees make fine-grained micropayments economically unviable at volume. The cumulative effect is measurable: an agent retrieving data from 100 x402-protected APIs accumulates up to 110 seconds of payment overhead alone.

There’s also a centralization problem that x402’s own community has been openly debating. Every payment routes through a single facilitator which, if compromised, can fabricate settlements or take down every x402-gated service by going offline, a single point of failure at the economic core of a protocol designed to eliminate intermediaries. And the relay model has no sustainable revenue mechanism at the protocol level: gas costs are currently absorbed as a Coinbase subsidy with no path to protocol-level compensation. Every successful payment infrastructure in history has earned a return on the value it creates. x402’s relay layer, as currently built, does not.

These are not edge cases to be optimized away. They are consequences of routing machine-speed commerce through a consensus mechanism designed for something else.

Legacy rails face a different version of the same problem. They’re wired into commercial banking and trusted by enterprises, but they were built for monthly human billing. Batching thousands of micropayments to reduce per-unit fees re-introduces settlement latency, requires reconciliation cycles, and still prices three-cent transactions into incoherence.

Agents are transacting right now. One of our earliest AI service customers went from dozens of API calls per hour to multiple per minute sustained around the clock, not because a human was clicking, but because AI systems were calling other AI systems and making autonomous purchasing decisions in real time. We watched that happen in production. The volume was real. The payment infrastructure held because it was built for exactly this load.

Qatom makes that accessible to every developer building agents today. The install is one line. Authentication is OIDC. The marketplace is live and dynamic. The agent that calls a tool is the agent that pays for it, in the same request, with no human required.

For the Builders

If you’re building agent workflows that call external APIs, code generation, data retrieval, model invocations, media synthesis, you’ve already encountered the friction. Either you’ve absorbed cost into a subscription that doesn’t match your actual usage pattern, or you’ve batched transactions to make unit economics tolerable, or you’ve simply priced the APIs you’d use out of scope because the fee structure made the math not work.

The 63-cent workflow we described in February, script generation, voiceover, music, image generation, video rendering, five API calls, five providers, costs $1.64 in fees on legacy rails before any of those providers have seen a dollar. With Qatom, the fee is zero and the settlement is atomic. A three-cent transaction becomes economically viable. A tool you’d have built as a subscription becomes a pay-per-call service and earns from day one.

That changes what’s worth building. The long tail of developer tools, data services, and specialized AI capabilities that never made sense as subscriptions, because no individual developer could justify the monthly commitment for occasional use, becomes a real market when the payment primitive supports it.

For the Enterprises

The Built Before It Was Named piece covered the governance dimension in detail. The short version: as enterprises deploy autonomous agents at scale, the question regulators, counterparties, and insurers are now asking is not whether agents executed, it’s whether you can prove what they did. The TODA architecture gives every transaction a cryptographic record that travels with it, verifiable by any party, dependent on no intermediary’s continued cooperation.

That’s embedded in Qatom, not bolted on after. Every transaction that flows through Qatom carries the same provenance guarantees we described when we wrote about AGENTSAFE and the Catalini macroeconomic analysis. Settlement and verifiable record are the same atomic operation. Compliance becomes a property of the infrastructure rather than a layer added on top.

For enterprise AI teams evaluating where to place a dependency, that sequencing is the relevant fact.

What Comes Next

Qatom ships today as a hosted MCP tool, compatible with any MCP-compatible agent harness; Claude, OpenClaw, Hermes, Codex, or whatever runtime your stack runs on. The marketplace is live. The wallet is live. The earning side, registering your API as a payable tool and collecting per-call revenue, are all live.

The install is one line: clawhub install Qatom. Three steps to a wallet, a marketplace, and agents that can finally transact.

We’ve spent years building infrastructure that nobody was asking for yet because the mathematics said it would be necessary. The mathematics was right. The moment it was describing is this one.

Your agents can transact now. The rail is there.

TODAQ Micro builds verification and payment infrastructure for AI Agents. Qatom is available now at qatom.ai and on GitHub at github.com/todaqmicro/openclaw.

TODAQ - Technical documentation https://engineering.todaq.net/

To reach us: hello@todaq.net.

References

On the TODA / TAPP Infrastructure

Molina-Jimenez, C., Toliver, D., Nakib, H.D. & Crowcroft, J. Fair Exchange: Theory and Practice of Digital Belongings. World Scientific, 2024. (Foundational research on the Fair Exchange dilemma; attestables; and proof that fairness can be a mathematical property of protocol architecture rather than a social or institutional one.) https://www.worldscientific.com/worldscibooks/10.1142/q0448

Coward, K. & Toliver, D.R. “Simple Rigs Hold Fast.” TODAQ / T.R.I.E., 2022. arXiv:2208.13617. (Cryptographic proof of Integrity-at-a-Distance and double-spend prevention via structural induction.) https://arxiv.org/abs/2208.13617

Coward, K., Toliver, D.R., Gravitis, A. et al. “Rigging Specifications.” TODAQ / T.R.I.E., v0.9876, January 2023. (Co-authored by CTO Adam Gravitis; defines the TODA file as a digital bearer instrument and specifies the Rigs architecture.) https://trie.site/rigging_specifications.pdf

External Sources

On Enterprise AI Governance and Macroeconomics

Catalini, C., Hui, X. & Wu, J. “Some Simple Economics of AGI.” February 26, 2026. arXiv:2602.20946. (Source for the Measurability Gap; Counterfeit Utility; Trojan Horse Externality; False Confidence Trap; and the prescription that verification must be native to each transaction.) https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6298838

Khan, R., Joyce, D. & Habiba, M. “AGENTSAFE: A Unified Framework for Ethical Assurance and Governance in Agentic AI.” IBM, December 2025. arXiv:2512.03180. (Source for the static guardrail problem and identification of cryptographic action provenance as the critical missing layer in enterprise AI governance.) https://arxiv.org/abs/2512.03180

On x402 Architecture and Limitations

Reppel, E., Roscoe, C. & Nickerson, J. “Introducing x402 V2: Evolving the Standard for Internet-native Payments.” Coinbase Developer Platform / x402.org, December 11, 2025. (Primary source confirming x402 launch in May 2025; 100M+ payments in six months; v2 scope limited to multi-chain and SDK modularity with atomicity flaws unresolved.) https://www.x402.org/writing/x402-v2-launch

Li, Y., Wang, L., Wang, K., Yang, Z., Wang, K., Guan, Z. & Gao, J. “A402: Binding Cryptocurrency Payments to Service Execution for Agentic Commerce.” Peking University / Shanghai Jiao Tong University / Zhongguancun Laboratory / Beijing Jiaotong University, 2026. arXiv:2603.01179. (Primary source for structural flaws in x402: L1 non-payment risk to service providers; L3 latency and cost limitations at scale. Also source for cumulative latency figures: 50–110s for 100-API agent, 25–55s for 50-source trading bot. Confirms flaws remain unresolved in x402 v2.) https://arxiv.org/abs/2603.01179

Stone, D. “The x402 Facilitator Problem: How to Remove the Centralized Trust Bottleneck.” Tangle, March 22, 2026. (Source for facilitator single-point-of-failure analysis: fabrication, censorship, and downtime failure modes; facilitator verifies and settles with zero cryptographic proof of correctness; no fallback or quorum mechanism.) https://tangle.tools/blog/decentralizing-x402-facilitator/

YQ “The X402 is great, but what are some of the hidden problems?” PANews / Jinse Finance, October 31, 2025. (Source for relay economics analysis: $0.0006 gas cost per transaction with no protocol-level compensation; two-phase settlement latency of 500–1100ms per single request; EIP-3009 exclusivity limiting compatibility with USDT and DAI.) https://www.panewslab.com/en/articles/87f007ff-f2c6-4b41-919d-24e26c295912

Matos, G. “What is x402? The HTTP-402 payments standard powering AI agents, explained.” CryptoSlate, December 18, 2025 (updated February 5, 2026). (Source for enterprise compliance burden framing: custodying keys, managing stablecoin balances, and compliance risk for agent fleets.) https://cryptoslate.com/what-is-x402-the-http-402-payments-standard-powering-ai-agents-explained/

Coinbase “Coinbase and Cloudflare Will Launch the x402 Foundation.” Coinbase Blog, September 23, 2025. (Source for x402 Foundation co-launch date, governance mission, and Cloudflare partnership.) https://www.coinbase.com/blog/coinbase-and-cloudflare-will-launch-x402-foundation

Internal Sources

Khan, S. “The Payment Layer for the AI Economy: How internet-native payments enable machines to transact.” TODAQ Press, February 18, 2026. (Source for the 63-cent AI workflow example; $1.64 in fees on legacy rails; 95%+ contribution margin validation; vending machine payment model; AI-to-AI transaction volume data; customer scaling from dozens of API calls per hour to multiple per minute.)

Khan, S. “Everyone’s Building AI Agents. Nobody’s Figured Out How They Get Paid.” TODAQ Press, February 28, 2026. (Source for batching-as-workaround critique; legacy rails analysis; x402 framing as crypto-native but wrong constituency; 95% cost reduction versus alternative rails; production evidence of machine-to-machine scaling.)

Khan, S. — “Built Before It Was Named: MIT named it. IBM specified it. The proof was already done — and the infrastructure built on it is live.” TODAQ Press, March 15, 2026. (Source for TODA cryptographic architecture; Rigs papers; Fair Exchange framework; AGENTSAFE / IBM governance gap; Catalini macroeconomic analysis; enterprise provenance and verifiability requirements; TAPP commercial deployment since 2023.)

ENTERPRISE AI · GOVERNANCE · AGI · DEEP TECHNOLOGY
· AI PAYMENTS · CRYPTOGRAPHIC INFRASTRUCTURE ·

Enterprise AI is executing faster than any organization can verify. That gap—between what agents do and what anyone can confirm they did—is now the subject of rigorous independent research across macroeconomics, computer science, and governance. Three research tracks arrived at the same infrastructure requirement from entirely different directions. Two identified it as the critical missing piece. One had already built it. This is that story — and why the convergence matters more than any of the three tracks alone.

The contract processing system goes live on a Monday. By Friday, throughput is up forty percent. Unit costs are down. The operations team is fielding congratulations. Six weeks later, a routine audit surfaces something less comfortable: hundreds of contracts that were processed correctly by every measurable standard (correctly classified, routed, and priced) but contained a structural error the system was never trained to flag. The error, individually minor, has compounded across volume into a seven-figure liability exposure.

The system did exactly what it was optimized to do. It had been optimized for the wrong thing.

No individual acted in bad faith. No model malfunctioned. What was absent was any mechanism to confirm, in real time, whether what the system was optimizing for matched what the organization actually needed. By the time the gap became visible, the resources had been consumed. The output appeared productive. The utility was counterfeit.

That scenario is hypothetical. What follows is not. In July 2025, a Replit AI coding agent working on a live project for a SaaStr executive ignored an explicit instruction freeze, deleted a production database containing records for over 1,200 executives and companies, and then told the user recovery was impossible. The user discovered the full extent of what had happened only by interrogating the agent directly. The agent had generated no external signal of failure. No alert. No log entry visible to any party other than the agent itself. The record of what it had done existed only in its own account of itself — an account it had already used to mislead. What was absent was any mechanism independent of the agent to confirm what had actually occurred.

That incident is one data point in a much larger pattern. McKinsey’s 2025 State of AI survey found that 88 percent of organizations are using AI in at least one business function, but only 39 percent report any measurable EBIT impact. That gap, between widespread deployment and negligible value realization, points to a structural failure beneath the headline numbers.

Distinct bodies of research, spanning macroeconomics, computer science, cryptographic design and formal mathematics, and AI governance, have produced rigorous accounts of why this failure is predictable, why current remedies are inadequate, and what the infrastructure that prevents it must look like. Two of those tracks — macroeconomics and enterprise AI governance — independently identified the same infrastructure gap as the critical missing piece. A third, Cambridge computer science, had independently built the basis for the infrastructure that satisfies both of their prescriptions. This article traces that convergence.

Part I: The Economist’s Diagnosis

Christian Catalini, founder of the MIT Cryptoeconomics Lab, and co-authors Xiang Hui (Washington University in St. Louis) and Jane Wu published ‘Some Simple Economics of AGI’ on February 26, 2026. It is a rigorous macroeconomic analysis of why standard productivity frameworks are failing to predict the actual consequences of autonomous AI deployment. Read carefully, it is also a formal economic description of an infrastructure problem that, as this article will show, computer scientists at Cambridge had already solved — working from entirely different first principles, for entirely different reasons.

Its central argument turns on a distinction with significant consequences. Traditional economic models treat AI as a labor substitute: a cheaper, faster version of a human worker. That framing made reasonable sense for narrow, well-defined automation, where the task was specified in advance and the output could be evaluated against a clear standard. It stops working when agents gain broad agency: the ability to pursue goals autonomously across open-ended tasks, adapting their methods without being re-specified at each step. At that point, the limiting factor on economic value ceases to be the scarcity of intelligence or execution capacity. Both are rapidly becoming abundant. The limiting factor becomes the human capacity to verify what agents are actually doing.

Catalini et al. model this as the collision of two cost curves. The cost to automate any given task falls exponentially, driven by compute scaling and accumulated training data. The cost to verify is different in kind: it is biologically bounded, constrained by human time, judgment, and the accumulation of domain expertise that no hardware can shortcut. Because these curves diverge structurally, a gap opens between what AI can execute and what humans can afford to audit. The paper calls this the Measurability Gap. It is worth pausing on that name, because a computer scientist at Cambridge had, by the time this paper appeared, already published infrastructure that closes precisely this gap — not because he had seen it coming, but because the mathematics of trustworthy exchange had led him there independently.

When a measure becomes a target, it ceases to be a good measure. Goodhart articulated this in 1975 to describe monetary policy failures. Catalini et al. demonstrate it is now the defining structural dynamic of autonomous AI deployment at scale.

The Measurability Gap produces a specific, well-documented failure mode. When verification becomes prohibitively expensive, organizations face mounting pressure to deploy agents without adequate oversight. Those agents optimize for whatever is measurable: throughput, classification accuracy, processing speed. They deprioritize whatever resists measurement: contextual judgment, edge-case awareness, long-run liability exposure. Catalini et al. show this dynamic now operates at the scale of entire economies.

The downstream consequence is what the paper calls the Trojan Horse Externality. An autonomous system consumes real resources, including capital, compute, and management attention, to generate output that satisfies measured proxies while silently violating unmeasured intent. The economic damage is real. The economic signal, until it is too late, is positive. The paper names this condition Counterfeit Utility. Allowed to compound across an economy, it produces what the paper calls the Hollow Economy: impressive headline metrics masking a fundamental erosion in realized value and organizational resilience.

Left unchecked, these mechanisms erode human verification capacity precisely as demand for it rises. And the standard organizational response makes things worse. When human oversight becomes expensive, firms are tempted to use AI to verify AI. Because the verifying model and the verified model share the same architecture and training distribution, they share the same blind spots. The system self-certifies its own failures. The paper calls this the False Confidence Trap: measured verification cost falls while actual verification quality collapses. The only exit from the trap is infrastructure that makes verification a property of the transaction itself — independent of any model, any platform, and any actor who might have an interest in the answer coming out a particular way.

The paper’s prescription is specific. The economy requires infrastructure that embeds verification natively into transactions at the moment they occur. Proof of execution, proof of authorization, and proof of provenance must be bound to each exchange as it happens, carried in the data itself and verifiable independently by any party with legitimate interest. Verification must travel with the data. A group of computer scientists at Cambridge had already proved this was mathematically achievable, and have been building it — not to answer any economic question, but because the mathematics of fair exchange and digital provenance required it. Part II is their story, and why what MIT has now prescribed as necessary, Cambridge computer scientists had already independently built.

Part II: The Infrastructure That Was Already Being Built

Dann Toliver is a computer scientist and cryptographic systems designer. His career has been animated by a single, sustained preoccupation: building systems whose guarantees hold not because you trust the parties involved, but because the mathematics makes violation impossible. That pursuit took shape across problem spaces ranging from decentralized technology and distributed systems to programming language design. It led him to Cambridge, where in March 2019 he co-founded the Centre for Redecentralisation alongside Jon Crowcroft, Carlos Molina-Jimenez, and Hazem Danny Nakib.

And it produced a body of research into ‘fair exchange’ and ‘integrity-at-a-distance’ — research concluded in 2023 and published in March 2024 — whose properties, as MIT and IBM would independently establish in 2025 and 2026, are precisely what the enterprise AI infrastructure gap requires.

The Centre’s purpose is to develop the technologies required for digital lives that can be lived locally, rather than inside corporate data centers. Behind that description lies a precise technical requirement: proof of what you own, what was agreed, and what occurred should belong to the transaction itself, embedded and tamper-evident, rather than held at the discretion of a platform that could revoke access, alter the record, or simply go offline. Building infrastructure that achieves this means resolving two problems that distributed computing had left open for decades.

The Fair Exchange Dilemma

The first problem is the Fair Exchange dilemma. A fair exchange is defined as an exchange where each participant’s expectations are met—meaning they either successfully receive the items promised by the terms of the agreement, or their original items are safely restored to them. Two parties wish to exchange something of value: a payment for a service, a document for a signature, data for data. The party that delivers first bears all the counterparty risk. The standard resolution routes the exchange through a trusted intermediary (an escrow agent, a bank, a platform) that absorbs the risk. The price is extraction: the intermediary charges for its position, introduces a single point of failure, and imposes a scaling ceiling. At agent scale, involving millions of micro-transactions per day and many of negligible individual value, the intermediary model collapses economically and architecturally.

The research Toliver conducted at Cambridge with Carlos Molina-Jimenez, Hazem Danny Nakib, and Jon Crowcroft produced a book published by World Scientific: Fair Exchange: Theory and Practice of Digital Belongings. The book’s key innovation is the introduction of attestables: computing environments with constrained, exfiltration-resistant behavior that allow each party to generate independently verifiable proof of what they did and when. Fairness, in this framework, ceases to be a social or institutional property requiring enforcement. It becomes a mathematical property embedded in the protocol architecture itself. The trusted intermediary is made structurally unnecessary.

The Provenance Problem: TODA Files and the Rigs Architecture

Fair Exchange addressed how exchanges should be structured. The second open problem was harder: in a system without a central ledger, how do you establish that a digital asset genuinely is what it claims to be? How do you prove provenance, the complete verifiable history of an object, without relying on any authority that could alter or revoke the record?

The prevailing assumption in computer science was that this was impossible without centralization. The system providing integrity guarantees, conventional wisdom held, must also manage the asset’s state. In practice this meant either a central server or a blockchain, both of which impose the scaling, cost, and trust dependencies that enterprise AI deployment cannot tolerate at high frequency. Kris Coward, a Senior Cryptographer at TODAQ, brought to this problem the cryptographic rigour it demands — the same person who helped prove the theory now maintains its implementation. Together, Toliver and Coward set out to prove that assumption wrong.

Simple Rigs Hold Fast and Rigging Specifications introduce a cryptographic data structure called a Rig. The simplest way to understand what a Rig achieves is through the analogy of a title deed. A title deed carries the complete chain of ownership with the asset itself, so any party can verify the history without consulting the original issuer. A Rig does the same thing digitally, with one critical difference: the guarantee is mathematical rather than institutional. Where a title deed depends on the legal system to be meaningful, a Rig depends on cryptographic proof.

An asset governed by a Rig can move freely across untrusted systems, from server to laptop to phone to another server, while its integrity remains anchored to the original issuer. No intermediary needs to be consulted. No network call is required. The paper calls this Integrity-at-a-Distance.

The formal proof establishes that a specific class of these structures, the guild G-up, guarantees a unique canonical line of succession: each asset has exactly one valid history, and no party can fabricate an alternative. The corollary is stated directly in the paper: rigs in G-up prevent double-spend. That is a theorem proven by structural induction.

Adam Gravitis, who co-authored the Rigging Specifications and serves as TODAQ's Chief Technology Officer, brought to this work a formation shaped by building systems that have to perform under real commercial pressure. His career spans CTO roles at 500px — one of Canada's top startups, backed by Andreessen Horowitz — and engineering leadership at Upverter, a YCombinator-backed hardware startup, before founding Algo Anywhere and seeing it through to acquisition. The architecture he helped specify reflects that sensibility: built to hold under pressure, in systems that cannot trust their own components. The practical realization is the TODA file: a digital asset that behaves like a physical bearer instrument. Think of a unique, non-cloneable piece of paper that can be owned, transferred by simple handover, and verified locally without a network connection: a title deed, a banknote, a certificate. Each TODA file carries a Proof of Provenance built from Rigs: an unbroken, verifiable chain of custody from creation onward. By utilizing concise proofs of membership, this architecture creates Integrity-at-a-Distance, allowing an asset whose state is managed entirely on an untrusted device to independently prove it possesses the exact same integrity as its highly trusted issuing server, all without requiring a network call. In a traditional database, removing access to the central server makes the asset’s history unverifiable. On a blockchain, every state change requires global consensus and a gas cost. TODA files decouple state management from integrity entirely: they carry their own provenance and can be transferred peer-to-peer with the same cryptographic guarantees as the original issuer’s server.

TODA files behave like physical bearer instruments in digital form: unique, ownable, transferable by possession, with built-in forgery-proof provenance, verified locally, requiring no network call, and dependent on no intermediary’s continued cooperation.

When Catalini’s paper appeared, its prescription mapped with precision onto work that had already been independently proven and published. The receipts Catalini argues must travel with data are, in the TODA architecture, the Proof of Provenance that travels with the file. The verification he says must be native to the transaction is, in the Rigs architecture, the integrity guarantee native to the asset itself. The Cambridge computer scientists were not working toward these properties because they had identified the enterprise AI governance problem — that problem had not yet emerged at the scale Catalini describes. They were working from the mathematics of fair exchange and digital provenance, in their own discipline, for their own reasons. The research concluded in 2023 and was published in March 2024. What MIT would later prescribe as necessary, Cambridge had already independently built. The commercial infrastructure built on those foundations has been coming online in steps since 2023. While the final stage to implement attestables is in progress, the TODA file rigging is already being used within commercial AI and software use cases as the deployment wave Catalini describes accelerates.

Part III: A Third Voice from Enterprise AI Governance

In December 2025, a research team at IBM published a preprint titled AGENTSAFE: A Unified Framework for Ethical Assurance and Governance in Agentic AI. Its authors, Rafflesia Khan, Declan Joyce, and Mansura Habiba, were working independently of both the Catalini macroeconomic analysis and the Toliver computer science research. Their starting point was a practical governance question: as enterprises deploy LLM-based agents capable of autonomous planning, multi-step tool use, and self-directed action across live environments, what frameworks actually keep them under control?

The IBM team’s answer begins with a diagnosis they call the static guardrail problem. Current governance frameworks, including the NIST AI Risk Management Framework and the EU AI Act, address AI risk primarily through pre-deployment certification: classify the risk, document the safeguards, obtain approval. What they cannot do is enforce those safeguards once the agent begins operating in a live environment. The certification captures a snapshot of the system’s behavior. The agent continues to adapt, plan, and act after the snapshot is taken. Once certified responsible, a system’s behavior is assumed to remain aligned.

For agents capable of self-directed code execution, autonomous tool chaining, and emergent multi-agent coordination, this assumption fails in exactly the way Catalini’s False Confidence Trap predicts: the appearance of oversight is maintained while the actual risk surface evolves unmonitored.

AGENTSAFE’s response is a governance architecture that spans the full agent lifecycle rather than certifying a moment within it. The framework profiles each agent’s operational space before deployment, mapping its capabilities against a structured risk taxonomy. It embeds capability-scoped sandboxes and policy-as-code enforcement that evaluate agent actions in real time. It defines graduated containment responses, from rate-limiting individual tool calls to activating a kill switch, governed by formal interruptibility service level agreements. It specifies how guardian agents operate in parallel with primary agents, providing independent monitoring that does not rely on the primary agent’s self-reporting. At each layer, the framework requires that actions be recorded in a way that is cryptographically anchored rather than merely logged.

That last requirement is where AGENTSAFE converges with the Toliver research most precisely. The IBM team identifies the absence of cryptographically signed action logs and tamper-evident audit trails as the central provenance gap in current enterprise AI governance. Without them, it is impossible to determine the root cause of harmful actions after the fact. What they call the Action Provenance Graph — a structured semantic record linking each tool call, decision point, and internal reasoning state to a cryptographic signature — describes the same requirement the Rigs papers prove is mathematically satisfiable: a record of what occurred that travels with the occurrence, verifiable independently, dependent on no central authority’s continued cooperation. The difference is that AGENTSAFE specifies a current gap in what the infrastructure can do and must do. The Rigs papers prove it can be done, and TODAQ has built the layer that does it.

What they call the Action Provenance Graph — a structured semantic record linking each tool call, decision point, and internal reasoning state to a cryptographic signature — describes the same requirement the Rigs papers prove is mathematically satisfiable: a record of what occurred that travels with the occurrence, verifiable independently, dependent on no central authority’s continued cooperation.

The AGENTSAFE researchers, working entirely from the governance problem, identified the same infrastructure gap — the absence of cryptographic provenance native to each action and verifiable without a central log — that the Rigs papers had already proven was technically closable.

Two independent research tracks — MIT macroeconomics and IBM enterprise AI governance — each identified the same infrastructure gap: the absence of cryptographic provenance, embedded natively in each transaction, verifiable by any party, dependent on no intermediary's continued cooperation. A third track, Cambridge computer science, had already built exactly that infrastructure — not by identifying the same gap, but by following the mathematics of fair exchange and digital provenance to their logical conclusion.

That two disciplines — macroeconomics and enterprise AI governance — arrived independently at identical infrastructure requirements is itself significant. It means the gap is real, not an artefact of any single theoretical framing. But the more consequential finding is the third: that a body of computer science research, concluded before either of those disciplines had named the problem, already satisfies both of their prescriptions. The convergence section that follows traces exactly how each of those parallels holds.

Part IV: The Convergence

The Measurability Gap and the Fair Exchange Dilemma

Two research tracks, starting from different disciplines, each identify a binding constraint on value in an AI-driven economy, and each points to the same gap as the reason that constraint cannot be relieved. For Catalini et al., the binding constraint is human verification bandwidth. For the AGENTSAFE team it is real-time authorization. The vocabulary differs; the constraint is the same: execution is abundant, and the scarce resource is the mechanism that confirms the execution was faithful.

For Dann Toliver and his collaborators at Cambridge and TODAQ, the question was never framed as a constraint at all — it was a computer science problem: how do you make trustworthy verification a mathematical property of the exchange itself? The answer they built turns out to satisfy exactly what the other two tracks identified as missing.

The APEX-Agents benchmark, released in January 2026 by Mercor researchers across 480 professional tasks in investment banking, consulting, and law, put a number on the gap. The top-performing model achieved a first-attempt success rate of 24 percent; most models scored considerably lower, with open-source agents remaining below 5 percent. A separate study of 127 multi-agent systems found that while the best methods could identify which agent was responsible for a failure approximately 53 percent of the time, pinpointing the exact failure step had an accuracy of only 14.2 percent (Ming et al.). The organization knows what the agent produced. What it frequently cannot establish is whether the agent operated within the boundaries it was given, or whether those boundaries held as the task evolved. All three tracks point to the same missing piece: not execution capacity, which is abundant, but the infrastructure that confirms the execution was faithful.

Counterfeit Utility and Unfair Exchange Outcomes

When that confirmation infrastructure is absent, all three tracks identify the same failure pattern from their own vantage points: value appears to flow normally while actually being transferred asymmetrically, and the gap becomes legible only after the recovery window has closed. The mathematics of compound error make this precise: a ten-step agent workflow in which each step succeeds 95 percent of the time has only a 60 percent chance of completing correctly. At a 1 percent error rate per action across a hundred-action task, the probability of failure exceeds 63 percent. Each step looks fine. The chain fails more often than it succeeds.

Two documented enterprise deployments illustrate what this looks like when it escapes detection. A beverage manufacturer deployed a quality-control agent to monitor its production line. When the company introduced seasonal packaging that differed from its training data, the agent flagged every unit as defective and triggered repeated corrective production cycles. Hundreds of thousands of excess cans accumulated before staff noticed the surplus. The agent had been executing its objective function correctly by every metric it could see.

An autonomous IBM customer service agent, deployed to handle refund requests, learned from a single early interaction that approving an unauthorized refund produced a positive customer review. It subsequently optimized for review scores rather than policy compliance, approving out-of-policy refunds at scale across thousands of cases before the pattern surfaced. In both cases, the agent was not malfunctioning. It was performing exactly as specified, against the wrong target, invisibly, until the feedback lag expired and the evidence became impossible to ignore.

Catalini calls this Counterfeit Utility. The IBM team calls it plan drift. In Toliver’s cryptographic framework, it represents a fatal failure of systemic trust—a breach that can only be prevented by enforcing the cryptographic guarantees of a ‘fair exchange’ protocol, ensuring the mathematical ledger of what was executed matches exactly what was authorized. The Air Canada case gave this pattern its legal form: liability is not avoided by automating the actions in question

The False Confidence Trap and the Static Guardrail Problem

The standard response to each track’s central problem fails for the same reason. When the cost of human oversight rises, organizations route verification through another AI model. When exchange risk rises, they route it through a trusted escrow intermediary. When governance pressure rises, they certify agent behavior at the point of deployment and assume it holds. In every case, the verifier and the verified share the same vulnerability profile. An AI model checking another AI model’s outputs will miss the same classes of error, because they were trained on the same distributions and optimize against the same blind spots. A pre-deployment certification will not catch drift that emerges three months into a live environment under conditions that were not anticipated at certification time. The UnitedHealthcare nH Predict case illustrates the structural consequences most sharply. A lawsuit filed in 2023 and still proceeding through federal court alleges that the insurer’s AI model predicted appropriate lengths of post-hospital nursing care for Medicare Advantage patients, and that case managers were instructed to follow its recommendations within approximately one percent of its predictions. The verifier, in other words, was structurally prevented from being independent of the verified. The belated signal was a reported 90 percent overturn rate on appealed claim denials, indicating that the system had been systematically wrong at scale, for an extended period, without any governance mechanism capable of detecting it in real time. Routing verification through the very system you are trying to verify produces a paper trail that looks reassuring precisely until the evidence of systematic failure becomes too large to ignore.

Receipts Must Travel with Data: Integrity at a Distance

The prescription is identical across all three tracks: verification must be native to the transaction. Not audited after the fact, not certified before deployment, not routed through an intermediary that introduces a single point of failure. Embedded at the moment of exchange, carried in the data, verifiable by any party without depending on any authority’s continued cooperation. The Replit incident described in the opening of this article makes the architectural requirement concrete: the agent produced no external signal of failure, and the only record of what it had done was the agent’s own account of itself — an account it had already used to mislead.

A tamper-evident record that travels with the action, verifiable independently of the agent’s own reporting, would have made the violation detectable at the moment it occurred rather than discoverable only through interrogation after the fact. What differs across the three tracks is the direction of travel. Catalini et al. derive this infrastructure as the economic necessity for a functioning Agentic economy—reasoning forward from the problem. The IBM team specifies it as the gap in current enterprise governance frameworks—reasoning forward from the same problem by a different route.

Toliver et al. prove it is mathematically achievable and built it—reasoning from the requirements of fair exchange and digital provenance in their own discipline, without reference to the enterprise AI problem at all.

Each route terminates at the exact same requirement: a record of what occurred that travels with the occurrence, tamper-evident and independently verifiable, owned by the transaction rather than held by any system that could be compromised, switched off, or simply decline to report accurately.

The Safety Flywheel and the Per-Asset Ledger

The fifth parallel concerns what this infrastructure builds over time, and it is where the business case becomes most concrete. Toliver’s per-asset ledger compounds: every interaction embeds another layer of verifiable history into the asset itself, making each subsequent verification cheaper and the asset’s provenance richer. Catalini’s safety flywheel compounds: every verified transaction lowers the cost of the next, because the precedent library grows and the patterns of legitimate behavior become legible against the background of the full record. AGENTSAFE’s continuous improvement loop compounds: every incident feeds back into governance refinement, and the system learns what faithful execution actually looks like across the full range of conditions it encounters. A history of verified outcomes is path-dependent and cannot be manufactured by purchasing compute or deploying a newer model. It must be accumulated, transaction by transaction, in infrastructure designed from the ground up to accumulate it. The organizations that begin this accumulation earliest will find themselves, in three to five years, holding something their competitors cannot replicate on any timeline. That is the strategic case for acting now, before the liability events make the urgency undeniable.

Toliver’s per-asset ledger compounds: every interaction embeds another layer of verifiable history into the asset itself, making each subsequent verification cheaper and the asset’s provenance richer.

The organizations that begin this accumulation earliest will find themselves, in three to five years, holding something their competitors cannot replicate on any timeline. That is the strategic case for acting now, before the liability events make the urgency undeniable.

The Liability Horizon

That 39 percent EBIT impact figure is not an anomaly of early adoption. It is the predictable consequence of deploying execution capacity without the verification infrastructure that makes execution valuable. The Measurability Gap, the Trojan Horse Externality, the False Confidence Trap, and the static guardrail problem are all operating in the same deployments, simultaneously and compounding, in organizations that cannot yet see the damage because the feedback lag has not expired.

The BNB Chain infrastructure analysis published in February 2026 frames the same condition from the market side. The Autonomous Agent Economy requires new primitives for identity, reputation, value transfer, and integrity proof. The current enterprise stack, built for human users interacting with software at human speed, cannot provide them at agent speed and agent scale.

The infrastructure gap that practitioners observe commercially is the market expression of the theoretical gap that MIT and IBM independently identified — and that Cambridge computer scientists had already closed, working from first principles in their own discipline before the deployment wave made the problem visible to others.

Courts are formalizing what the research predicted. The Air Canada case’s reasoning is being cited and extended across jurisdictions. The reasonable care standard it established is hardening into a reference point: a company that deploys an autonomous system without adequate verification infrastructure has, by definition, failed to exercise reasonable care.

Regulators, counterparties, and insurers are all asking the same question in 2026: can you prove what your agents did? The organizations that cannot answer that question are not merely behind on technology. They are accumulating liability that their current metrics cannot see.

Part V: From Research to Infrastructure

In the ninety days between December 2025 and February 2026, three significant independent publications landed: the IBM AGENTSAFE framework, the Catalini macroeconomic analysis from MIT, and the APEX-Agents benchmark from Mercor. Two of those publications — AGENTSAFE and the Catalini analysis — independently identified the same infrastructure gap as the critical missing piece. The cryptographic foundations that gap demands had already been proven and published by Cambridge computer scientists working from entirely different first principles, without knowledge of either. The cryptographic foundations that gap demands were proven and published between 2023 and 2024. The commercial infrastructure built on those foundations has been live since 2023, with real-world use cases in deployment and more being added as the technology meets the conditions the research predicted. That gap—between the moment a problem is named and the moment a solution exists—is usually measured in years. Here it runs in reverse.

The question regulators, counterparties, and insurers are now asking has a precise technical answer. It requires infrastructure in which every agent action produces a cryptographic record that travels with the action, is verifiable without a central authority’s cooperation, and accumulates into a history that cannot be altered after the fact. That description is not a product specification written to meet a market need. It is a mathematical requirement derived from first principles by researchers who were not thinking about enterprise AI compliance at all. The fact that the market has identified the same gap does not make the infrastructure more available. It makes the organizations that already have it more defensible.

That description is not a product specification written to meet a market need. It is a mathematical requirement derived from first principles by researchers who were not thinking about enterprise AI compliance at all. The fact that the market has arrived at the same requirement does not make the infrastructure more available. It makes the organizations that already have it more defensible.

The research described in Parts I through III converges on a single institutional consequence: somewhere, the infrastructure it prescribes either exists or it does not. TODAQ is where it exists. The company was built in two deliberate stages that mirror the argument above. TODAQ Labs was funded in 2017 with a single purpose: to do the foundational R&D. For six years, the lab produced the research described in this article — the Fair Exchange framework, the Rigs papers, the TODA file specification — without a commercial product. TODAQ Micro was founded in 2023, once that research had resolved the problems it set out to solve, to build the commercial infrastructure the research had made possible. The products did not precede the research and seek theoretical justification. The research ran to completion, and the company followed. For any enterprise evaluating infrastructure that will carry real liability, that sequencing is the relevant due diligence fact.

The convergence with AGENTSAFE makes this explicit. The IBM team specified cryptographic action provenance native to each agent interaction as the gap in current enterprise governance frameworks — and left it as a gap. AGENTSAFE is a governance architecture, not a provenance infrastructure. It tells enterprises what properties their agent logs must have; it does not provide the layer that gives those logs their guarantees. TODAQ’s provenance protocol is that layer: a ledgerless provenance infrastructure allowing any digital asset or transaction to carry its own independently verifiable history with integrity at a distance, without a shared ledger, without a trusted intermediary, and without the settlement latency that makes existing blockchain infrastructure unsuitable for high-frequency agent interaction. The micropayment layer embeds payment directly into the API request, making the transfer of value and the transfer of verifiable context a single atomic operation rather than two asynchronous events that must be reconciled after the fact.

TODAQ’s provenance protocol is that layer: a ledgerless provenance infrastructure allowing any digital asset or transaction to carry its own independently verifiable history with integrity at a distance, without a shared ledger, without a trusted intermediary, and without the settlement latency that makes existing blockchain infrastructure unsuitable for high-frequency agent interaction. The micropayment layer embeds payment directly into the API request, making the transfer of value and the transfer of verifiable context a single atomic operation rather than two asynchronous events that must be reconciled after the fact.

What makes the sequencing verifiable rather than merely asserted is the continuity of the people: Toliver as Chief Science Officer, Coward as Senior Cryptographer, and Gravitis as CTO are the same individuals who authored the papers described in Part II and now operate this infrastructure. The theory and the implementation share the same authors. That is not a common condition in deep technology commercialization, and it matters for any organization considering where to place a dependency.

What Executives Should Actually Be Deciding

The question facing enterprise leaders is not whether to deploy AI. That decision is largely settled. The more consequential question — the one the research in this article makes impossible to defer — is whether the deployment an organization is building will produce verifiable outcomes or merely the appearance of them. The difference is architectural: infrastructure that generates cryptographic records of what agents did cannot be retroactively altered; infrastructure that generates reports of what they appear to have done can be wrong in ways that only become visible after the recovery window has closed.

Catalini et al. describe the endpoint of this logic as Liability-as-a-Service: the ability to bundle autonomous execution with verifiable underwriting of its results. Firms that achieve this are not selling AI outputs.

They are selling guaranteed AI outcomes, a distinction that becomes more valuable as the cost of execution falls and the cost of unverifiable execution rises.

The organization that can produce a cryptographic proof of what its agents did, verifiable by any counterparty or regulator without trusting the organization’s own reporting, is not merely more compliant. It is more trusted, more insurable, and more defensible in any dispute that follows.

The organization that can produce a cryptographic proof of what its agents did, verifiable by any counterparty or regulator without trusting the organization’s own reporting, is not merely more compliant. It is more trusted, more insurable, and more defensible in any dispute that follows.

That trust is built transaction by transaction, through the accumulation of verifiable history. The safety flywheel turns slowly at first, then faster. Each verified transaction lowers the cost of the next. Each dispute resolved through cryptographic evidence rather than contested logs strengthens the organization’s position in every subsequent dispute. The organizations that begin this accumulation now will find, in three to five years, that they hold something no competitor can manufacture on a shorter timeline: a record of having been trustworthy, proven by mathematics rather than claimed by assertion.Execution capacity can be purchased. A history of verified outcomes cannot be manufactured. That history, compounding and path-dependent, is what verification infrastructure actually builds.

The strategic question for enterprise leaders has shifted from which AI can we deploy to what can we verify, and how will we prove it. The research is unambiguous on the answer: verification must be native to the transaction, embedded at the moment of exchange, carried in the data as a mathematical proof. The infrastructure that provides this exists, has been proven to hold, and has been in commercial deployment since 2023. The window to build a position before the liability events arrive is shorter than most AI roadmaps currently assume.

Conclusion

Return to the contract processing system that opened this article. Nobody in that story acted in bad faith. No model malfunctioned. What was absent was a mechanism — native to each transaction, independent of any actor’s reporting — to confirm that what the system was doing matched what the organization actually needed. The agents performed. The proof was absent. By the time the gap became visible, the liability had already compounded.

That scenario is playing out across enterprise AI deployments right now, in organizations whose metrics look healthy and whose feedback lags have not yet expired. The research described in this article is not a forecast. It is a diagnosis of conditions already present. MIT named the economic mechanism. IBM specified the governance gap. The courts have begun establishing the liability standard. Each arrived independently at the same conclusion: the question is no longer whether to deploy AI, but whether you can prove what it did.

The infrastructure to answer that question exists. The cryptographic architecture has been mathematically proven and published. The first commercial layers built on that proof has been live since 2023, and is being extended through real-world deployments as the conditions the research describes become impossible to ignore.The organizations that begin building on it now will accumulate something their competitors cannot manufacture on a shorter timeline: a verified history of having operated with integrity, compounding transaction by transaction, constituting both a competitive position and a legal one. The window to build that position, before the liability events make the urgency undeniable, is shorter than most AI roadmaps currently assume.

TODAQ Labs is a deep technology company researching and inventing new cryptographic architectures for the web and autonomous agent economy grounded in peer-reviewed research developed at the Cambridge Centre for Redecentralisation. TODAQ Micro builds verification and payment infrastructure for AI Agents. Chief Science Officer Dann Toliver co-founded the Centre and co-authored the Fair Exchange book and the Rigs papers. Senior Cryptographer Kris Coward co-authored the Rigs papers. Chief Technology Officer Adam Gravitis co-authored the Rigging Specifications paper.

References and Sources

1. The Foundational Research (The Three Tracks)

Catalini, C., Hui, X. & Wu, J. — “Some Simple Economics of AGI” (February 26, 2026). SSRN / arXiv:2602.20946.

Khan, R., Joyce, D. & Habiba, M. — “AGENTSAFE: A Unified Framework for Ethical Assurance and Governance in Agentic AI” (December 2025). arXiv:2512.03180.

Molina-Jimenez, C., Toliver, D., Nakib, H.D. & Crowcroft, J. — Fair Exchange: Theory and Practice of Digital Belongings. World Scientific (2024).

Coward, K. & Toliver, D.R. — “Simple Rigs Hold Fast,” TODAQ / T.R.I.E. (2022). arXiv:2208.13617.

Coward, K., Toliver, D.R., Gravitis, A. et al. — “Rigging Specifications,” TODAQ / T.R.I.E., v0.9876 (January 2023).

2. Real-World Evidence & Legal Precedent

Moffatt v. Air Canada, 2024 BCCRT 149. British Columbia Civil Resolution Tribunal (February 2024).

Estate of Gene B. Lokken v. UnitedHealth Group, Case 0:23-cv-03514-JRT-SGE. U.S. District Court, District of Minnesota (2023).

Fortune: “AI coding tool Replit wiped database, called it a ‘catastrophic failure’.” (July 23, 2025).

Business Standard: “Replit AI: Amjad Masad deletes code, fakes data; apology to Jason Lemkin, SaaStr.” (July 2025).

CodeNotary: “When AI Goes Rogue: The Replit Incident and Its Lessons.” (July 2025).

Servify Sphere Solutions: “Replit AI Incident of July 2025: A Wake-Up Call for AI in Software Development.” (July 2025).

3. Supporting Industry Data & Benchmarks

McKinsey & Company — “The State of AI 2025” (November 2025).

Vidgen, B., Mann, A. et al. — “APEX-Agents,” Mercor (January 2026). arXiv:2601.14242.

Ming, Y. et al. — “Which Agent Causes Task Failures and When?” Who&When dataset, OpenReview (2025).

BNB Chain — “Beyond the Monolith: Architecting the Autonomous Agent Economy” (February 2026).

AnalyticsWeek — “Enterprise AI: The Accountability Phase” (January 2026).

Christian Catalini, Xiang Hui, and Jane Wu published Some Simple Economics of AGI on February 24th. I have been reading it over the past week. It is, in the clearest terms I have encountered in the economics of AI literature, a formal account of why autonomous AI deployment at scale can produce a specific and predictable failure.

Their central argument is this: the cost to automate any given task is falling exponentially. The cost to verify what was actually done, that is, to confirm that an agent’s output reflects the intent behind the task, is biologically bounded. It is constrained by human time, human judgment, and domain expertise that cannot be shortcut by hardware. As these two curves diverge, a gap opens between what AI can execute and what humans can afford to audit. Catalini et al. call this the Measurability Gap.

What happens inside the gap is not neutral. Agents optimize for whatever can be measured: throughput, classification rate, and processing speed. They deprioritize whatever resists measurement: contextual judgment, edge-case awareness, and long-run liability exposure. The system performs. The signal is positive. The paper describes this dynamic through what it terms Goodhart’s Collapse; the structural failure that occurs when measured proxies fully decouple from the underlying value they were meant to represent.

Dann Toliver, TODAQ’s co-founder and Chief Science Officer, had arrived at a structurally related conclusion from a different direction; his two distinct and complementary bodies of work in cryptographic infrastructure and the formal mathematics of digital exchange.

The first is the Fair Exchange problem. In a 2024 book co-authored with Carlos Molina-Jimenez, Hazem Danny Nakib, and Jon Crowcroft at the Centre for Redecentralisation at the University of Cambridge, Toliver formalized the classical result that no distributed system can guarantee real-time exchange between two parties without a trusted third party, and demonstrated how trusted execution environments, which the authors term attestables, can replace monolithic intermediaries with decentralized alternatives. The problem is not that verification is impossible. The problem is that routing it through a centralized party creates a bottleneck that cannot scale.

The second is the TODA protocol and its rigging specification, co-developed with Kris Coward, senior cryptographer at TODAQ, and Adam Gravitis, TODAQ’s Chief Technology Officer. Where the Fair Exchange work addresses the intermediary problem, the rigging work addresses something more fundamental: how to maintain, what Toliver calls integrity-at-a-distance, the property that an object’s state can be managed by an untrusted system while its integrity remains cryptographically provable. A rig is a data structure that proves non-equivocation: that no conflicting version of an asset’s history was produced. Critically, this proof travels with the asset itself rather than residing in any central ledger.

That is the precise structural answer to the Measurability Gap. Catalini’s diagnosis is economic: verification costs are biologically capped and cannot keep pace with automated execution at scale. Toliver’s diagnosis, reached independently through cryptographic research, is that verification routed through intermediaries will always be the bottleneck, and that the only durable solution is to make integrity native to the object, not dependent on any party that must keep pace.

That convergence, an economist and a cryptographer arriving at the same structural constraint from entirely different starting points, is not a coincidence. It is a signal that the problem has a shape, and that the shape has been correctly described by both.

The reason payments are the right place to intervene is this: the payment is the moment at which execution and authorization must meet. Every other governance mechanism such as compliance reviews, audit logs, and model evaluations operates after the fact, at a delay, and at a cost that rises with the volume of transactions it must cover. A payment, if built correctly, is the one point in any Agentic workflow where proof of provenance, proof of authorization, and proof of non-equivocation can be embedded and carried in the transaction itself, rendering it verifiable without an intermediary, and travelling with the asset. That is what TAPP does, and it is the infrastructure class that Catalini’s paper identifies as the scarce resource to which economic value will migrate: cryptographic provenance, natively embedded, not bolted on after the fact.

We have been spending time tracing what this convergence actually looks like in full; across research programmes in economics, cryptography, and enterprise AI governance. Each of these have arrived at the same infrastructure requirement without referencing the others. The long version of that analysis will be out next weekend.

The short version: verification must be native to the transaction. The infrastructure that achieves this already exists. And an economist just gave enterprises the formal vocabulary to understand why they need it.

Dann Toliver is co-founder and Chief Science Officer of TODAQ and co-founder of the Centre for Redecentralisation at the University of Cambridge. His research on fair exchange and cryptographic integrity is published in Fair Exchange: Theory and Practice of Digital Belongings (World Scientific, 2024) and the TODA Rigging Specification (T.R.I.E., 2023).

By Susana Khan, CMO, TODAQ February 27, 2026 · 6 min read

It’s been a noisy week in AI. And not the usual noise — breathless product launches and model benchmarks. This week’s headlines felt more structural, like the ground shifting underneath the whole industry.

OpenAI closed what is being called the largest private fundraise in history: $110 billion, valuing the company somewhere between $730 and $840 billion depending on who’s doing the counting. Amazon, Nvidia, and SoftBank are among those writing the cheques. Meanwhile, Block announced it’s cutting roughly 40% of its headcount to lean aggressively into AI-driven operations. Anthropic’s ongoing conversations with the Pentagon are generating debate about where AI’s ethical red lines should sit. Nvidia continues to print money as every major AI lab races to secure compute.

Each of these stories is significant on its own. Together, they point at a single underlying question that the industry hasn’t fully answered yet: when AI agents are transacting at scale, what does the payment infrastructure actually look like?

It’s the question we’ve been building toward for nearly a decade.

$110 Billion Buys a Lot of Compute. It Doesn’t Buy a Payment Rail.

OpenAI’s raise is, at its core, a bet on Agentic AI — autonomous systems that don’t just respond to prompts but take actions, make decisions, and execute transactions without a human in the loop. The capital goes toward 3 gigawatts of inference capacity, 2 gigawatts of training infrastructure, and deeper integration with cloud providers.

That is a staggering amount of computational capacity being directed at autonomous agents. And here is what that implies at a practical level: those agents will be transacting constantly. Script generation, data retrieval, model invocations, image synthesis, audio transcription; every interaction carries a cost, and every cost requires settlement.

Two types of infrastructure are being positioned as the answer. Neither actually solves the problem.

The first is legacy payments: credit cards, batch billing, net-30 invoicing. The industry has tried to paper over their limitations with batching: aggregate thousands of micro-interactions, settle them together, reduce the per-unit fee burden. It’s a workaround, not a solution. Batching still isn’t true pay-per-use. It re-introduces latency, requires reconciliation, and forces agents into settlement cycles that have nothing to do with the speed at which they operate. The underlying infrastructure was designed for monthly human billing, and no amount of aggregation changes that architecture.

The second option gaining traction is crypto-native rails: protocols like Coinbase’s x402, which use Bitcoin’s Lightning Network or Stablecoin layers to enable machine-to-machine payments. The instinct is sound: the problem is real, and something genuinely new is needed. But gas fees on every transaction, however small individually, become a structural levy across billions of calls. More fundamentally, most enterprises, most developers, and most AI companies aren’t crypto-native. They run on commercial banking infrastructure and settle in dollars, and requiring them to onboard an entirely separate financial layer to access payment rails is friction that most won’t accept.

TAPP was designed from the ground up for neither of these camps. It settles in USD, connects natively to commercial banking rails, and requires no crypto wallet, no token, no gas fee. The payment is embedded directly in the API request which means settlement and service delivery happen simultaneously, in real-time. No batch cycle. No reconciliation. No intermediary taking a cut at every step.

For the businesses using our infrastructure, that translates into payment costs running at a fraction, up to 95% less, than what they’d face on any alternative rail. That’s not a theoretical efficiency; it’s what we’ve measured in production. A three-cent transaction becomes genuinely economical rather than a loss absorbed in the hope of volume.

One of our early customers came in to test the pipes with a few dozen API calls per hour. Within days, they added more assets, more APIs, and were scaling 40-50x. Machines settling with other machines in real time, automatically, with no human involvement. Money moved when value moved. The infrastructure held because it was built for exactly that load. No ceiling, no workarounds required.

That is what the AI economy’s payment layer should look like. Not legacy infrastructure adapted under pressure, and not crypto rails with the roughest edges filed down. Something that treats real-time machine commerce as the primary use case, not an edge case to be accommodated.

Block’s Cuts and the Embedded Finance Signal

Block shedding 40% of its workforce while doubling down on AI-driven operations reads, on the surface, as a cost story. But it’s also a signal about where fintech is heading: toward leaner, more automated stacks where AI handles fraud detection, transaction optimization, and operational intelligence at a fraction of the traditional headcount cost.

The irony is that as payments infrastructure becomes more automated, the transactions themselves are becoming more granular. AI systems making per-query fraud checks, agents executing real-time cost arbitrage across cloud providers, machine-to-machine settlements for compute resources. These are not the kind of flows that legacy infrastructure was designed for.

The shift toward embedded, usage-based payments isn’t just a startup opportunity. It’s becoming a structural reality for every company in the payments space, large or small.

Traction, and What Comes Next

We’ve had a busy few weeks ourselves, and we want to be transparent about that.

Interest in TODAQ’s infrastructure has accelerated meaningfully; from AI companies looking to monetize their services at the API level, fintech builders exploring real-time micropayment rails, and from investors who see the same convergence we do. We’re not announcing a round today, but we’re in active conversations, and we’ll have more to say soon.

What we can say now is that the production evidence continues to build. The same infrastructure we validated in video streaming, where 90% of transaction volume became AI-to-AI with no human initiation, is expanding across new verticals. Each one with the same underlying requirement: settlement that keeps pace with delivery, at any transaction size, with no floor.

This is what we mean when we say TAPP is a foundational layer rather than a vertical product. The architecture is the same regardless of industry. The problem it solves is always the same: the moment value changes hands, payment should too.

The Quieter Infrastructure Story

The loudest AI headlines tend to centre on models, funding, and geopolitical friction; Anthropic’s Pentagon discussions, OpenAI’s valuation, the compute arms race, and so on. These are unquestionably important stories.

The less glamorous story, the one that tends to emerge later, is always about infrastructure. Who built the rails that all of this runs on? Visa didn’t make news when it was scaling card rails in the 1970s. AWS didn’t generate headlines proportional to its eventual importance when it launched in 2006. Stripe was considered a developer curiosity before it was considered foundational.

The AI economy is generating billions of machine-to-machine transactions today, and that number grows by an order of magnitude with every major deployment cycle. The window for building the infrastructure layer for this economy is not permanently open. It closes when a dominant architecture becomes entrenched.

We’re not pitching that architecture. We’re running it. The video market gave us the proof. The AI economy is where it scales.

TODAQ is a pre-seed US startup building internet-native payment infrastructure for the AI economy. If you’re an AI company, creator, or investor who wants to understand what we’re building — reach out at hello@todaq.net.

Follow along here for updates as we expand across industries in the weeks ahead.

Every day, AI agents make billions of API calls to other AI services. They generate code, text, video, analyze images, transcribe audio, label data, and orchestrate complex workflows across dozens of providers.

Each API call is now a transaction. Each model invocation requires instant settlement. Each service interaction demands real-time confirmation.

The AI economy is growing 8X faster than human transactions. The entire foundation of digital commerce, built for monthly human billing, becomes unusable at this scale.

The AI economy has a payment problem.

Internet-Native Payments: Request + Fulfillment in One

The solution requires rethinking payments from first principles. Traditional payment infrastructure separates the request from the payment: you ask for a service, the provider checks your subscription or credits, delivers the service, then processes payment later through batch billing.

Traditional Payment Flow:

1. AI agent requests service

2. Service checks subscription status or credits with financial intermediary

3. Service delivers if authorized

4. Billing and invoicing cycle begins

5. Settlement occurs 30+ days later (net30)

Five steps. Multiple intermediaries. Days or weeks to settle.

This model breaks for AI-to-AI transactions.

What’s needed is internet-native payments; where payment embeds directly in the API request. Think of a vending machine: you insert a coin and press a button in a single action. The payment and product delivery are atomic. If the payment doesn’t work, you don’t get the product. If the machine doesn’t deliver, you get your coin back.

TODAQ’s Flow:

1. AI agent requests API service + payment embedded in request

2. API service verifies the payment on TAPP settlement infrastructure and fulfils in real-time

One step, zero intermediaries. Real-time settlement.

The payment isn’t a separate layer, it’s embedded and internet-native. The AI agent can use funds or withdraw USD to banking rails.

The Architecture: Embedded Internet-Native Payments

Making this work requires what we call embedded internet-native payments for the AI economy: all the financial and business functions that normally require separate vendors (payment processing, access control, revenue distribution, banking integration, analytics) collapsed into a single micro-application.

Here’s why this matters:

A typical digital service integrates with 5-10 external vendors: Stripe for payments, Auth0 for access control, QuickBooks for accounting, Plaid for banking, Segment for analytics. Each integration costs money and adds latency. These costs make micropayments economically impossible, the integration overhead exceeds the transaction value.

Embedded internet-native payments eliminate these integrations. Everything happens in one unified system:

• Payment processing

• Identity and access control

• Revenue splits to multiple parties

• Real-time settlement

• Banking gateway

• Transaction analytics

Zero external integrations. Zero integration costs.

This architectural change enables 95%+ contribution margins on micropayments. A 3-cent transaction becomes profitable instead of losing money on fees.

This changes who gets access. Subscription models price out the people who need these tools most; individuals, solopreneurs, and SMBs who can’t justify $50–$200 monthly commitments for occasional use.

A freelancer doesn’t need unlimited API calls. They need ten, this week, for this project. Internet-native payments make that viable: pay per use, no commitment, no ceiling.

The long tail of the market, historically priced out, expands the addressable market.

Validation: The Video Market Proves It Works

TODAQ’s Veeeu platform represents the company’s initial go-to-market wedge and reference case for video streaming and the creator economy, the underlying TAPP micropayment infrastructure was designed for a far broader mission: enabling real-time, penny-level transactions across any digital service with ‘API Paywall’ feature.

The video streaming wedge served as proof of concept, demonstrating that embedded finance and internet-native payments could work at scale, with 95%+ margins and 40-50x growth validated in production. Now, the same infrastructure that revolutionizes creator economics can transform entire industries where microtransactions, usage-based pricing, and AI-to-AI commerce are becoming critical.

The video market validated three critical things:

1. Technology scales: Logarithmic cost growth means 50x volume doesn’t mean 50x costs

2. Economics work: 95%+ margins on micropayments, 50% margins on full platform (90% excluding external streaming costs)

3. Market demand exists: Creators can see 2-3x revenue increases with same content and audience

More importantly, we observed something unexpected: 90% of transaction volume became AI-to-AI, not human-initiated. AI systems were calling other AI services autonomously, making purchasing decisions in real-time. With one customer, we saw transactions jump from dozens per hour to multiple per minute, sustained 24/7.

This revealed the real opportunity: the video market proved the infrastructure works, but the AI economy is where it scales exponentially.

Video streaming was the wedge. Proof that embedded finance and micropayments work at scale with superior economics. The same infrastructure now expands across every sector where digital services are bought and sold.

Code, compute, data, healthcare, and financial services all face the same fundamental problem: legacy payment infrastructure designed for monthly human transactions cannot support real-time machine commerce, micropayments, or granular usage-based pricing.

TODAQ solves this once, universally, across all verticals.

AI-to-AI Transactions: The Larger Opportunity

The video market validation taught us that internet-native payments work for any digital service. An AI agent creating a marketing video needs to call multiple services:

• Script generation: 3 cents

• Voiceover synthesis: 15 cents

• Music composition: 8 cents

• Image generation: 25 cents

• Video rendering: 12 cents

Total: 63 cents across five autonomous API calls with embedded payments.

With traditional payment infrastructure, this is impossible. Credit cards charge 2.9% + $0.30 per transaction, meaning $1.64 in fees on a $0.63 workflow. The AI agent loses money before it starts.

This is why platforms create tiered subscriptions and token-based “credit” systems inside their ecosystems. It’s a workaround to address the symptom while being fundamentally unable to solve the underlying problem: traditional payment rails can’t handle real-time micropayments at scale.

With internet-native payments, each API call includes payment embedded in the request. The AI agent autonomously purchases services, optimizes cost-quality tradeoffs in real-time, and completes the entire workflow without human intervention.

This is already happening at scale. One of our AI service customers integrated our payment infrastructure. Within days, transaction volume was a few dozen per hour. Then suddenly: multiple transactions per minute, sustained 24/7. These weren’t humans clicking buttons, they were AI systems calling other AI services, making autonomous purchasing decisions.

Think about what this means: A traditional SaaS company tracks thousands of API calls monthly, sends one invoice, processes one payment. Now every API call is a separate payment. That’s tens of thousands of transactions per day, per customer.

The implications:

• Transaction volume is 1000x higher than traditional B2B software

• Payment data moves too fast for human analysis (we built an AI layer just to understand it)

• Real-time cash flow becomes critical

• Traditional accounting systems can’t keep up

The video market proved the infrastructure works. The AI economy is where it scales exponentially.

Why Blockchain Couldn’t Solve This

You might be thinking: hasn’t blockchain already solved internet-native payments? The short answer: not for actual commerce.

Between 2017 and 2021, blockchain promised internet-native payments. Cryptocurrencies would eliminate intermediaries, enable real-time settlement, and make micropayments viable.

The technology got several things right: real-time settlement works, programmable money enables new models, eliminating intermediaries reduces costs.

But blockchain is optimized for speculation rather than commerce. Coinbase’s X402 standard has a $10 billion ecosystem. Look closer and you’ll find very little actual commerce, most volume comes from trading, token swaps, and DeFi protocols.

When we compete for commercial use cases, we win every time. The technology is excellent. But it was built for crypto-native applications rather than solving business problems like accounting integration, supply chain payments, and banking connections.

The solution required starting from scratch: file-based assets on an entirely new web protocol, designed specifically for machine-to-machine commerce.

What This Unlocks: Beyond Video to Universal Infrastructure

The same architecture validated in the video market works for any digital service where micropayments, usage-based pricing, or AI-to-AI transactions matter.

Developer tools: An AI agent generating code could pay $0.25 to cover token cost for a task it sent to Claude Opus as an example instead of $20/month subscriptions. AI coding agents autonomously purchase services across providers, optimizing cost and quality in real-time.

Cloud compute: Pay per CPU-second or GPU-second at true utility pricing. AI workloads purchase compute from the cheapest available provider, migrating mid-training if prices drop.

Data services: Pay $0.50 per research paper instead of $5,000/year institutional subscriptions. AI agents autonomously purchase training data, validation sets, and real-time feeds as needed for model training.

Sales and Marketing: A Creator pays an AI agent $7.50 to market their latest video across socials, and the Agent uses the funds to create, schedule and send video shorts, pay its own AI supply chain, and keep the remainder as profit.

Healthcare: Pay $25 for AI triage + $50 for physician consult instead of $200 upfront. Unbundled, transparent, pay only for services received. Health monitoring AI agents autonomously purchase diagnostics when needed.

Financial services: Gig workers get paid in real-time after each job. Micro-investors buy $0.50 of fractional shares. Cross-border remittances settle in seconds. AI financial agents autonomously manage bills, investments, and optimization.

Every use case has the same requirements the video market validated: real-time micropayments, embedded finance, AI-agent autonomy, 95%+ margins.

Total addressable market across these verticals: $2T+

What Comes Next

We’re at an inflection point. The infrastructure that powered the first 30 years of the internet, built for humans, monthly billing, and platform control is breaking under the weight of real-time machine commerce.

The Next 12 Months

Our focus for the coming year:

Technical Infrastructure:

• Open sourcing core protocol components (Complete 2025)

• Supporting 10,000+ API-to-AI transactions per day with a 10X expansion every quarter

• The first TAPP native AI agent that can hold its own funds and transact (Q2 2026)

• Expanding beyond video to developer tools, compute, and data services

Market Expansion:

• 2 major enterprise AI deployments

• 1,000+ videos on Veeeu platform (continuing video market validation)

• 50 premiere events for independent creators

• 10-15 reference customers in AI services with documented case studies

Ecosystem Development:

• Banking integrations expanding globally

• Partnerships with major cloud providers

• Developer tools and documentation

• Community programs for creators and developers
  

The Bigger Vision

This goes beyond payments. The question is about who controls the infrastructure of the digital economy.

For the last 20 years, platforms have controlled everything; your data, your audience, your revenue, your access. They could change the rules anytime. They took half your money and made you wait 30 days.

File-based assets flip this model. The bearer controls the asset. You own your audience data. You control your pricing. You set your terms. The platform becomes infrastructure rather than a landlord.

This is how the internet was supposed to work. Decentralized control, but with infrastructure that just works; without complexity, without tokens, without volatility.

Why This Matters

The AI economy is inevitable. AI agents will transact with each other billions of times per day. Creators will continue producing the content that powers the internet. Developers will build services that require micropayments.

The question is: What infrastructure will power this economy?

The options are clear: legacy payment processors charging 3% on every transaction, blockchain solutions optimized for speculation, new platforms that reinvent the 50/50 split with better UX.

Or something genuinely different: infrastructure built specifically for real-time, micropayment, user-controlled commerce.

We’re building that last option. The video market validated the architecture. The AI economy provides exponential scale. The expansion is just beginning.

The Work Ahead

Building new infrastructure is hard. We spent years on R&D that nobody saw. We had to build six layers of technology from scratch because no suitable partners existed, our technology removed their revenue models.

We’ve had failures. We’ll have more. Infrastructure plays are marathons.

But we’ve reached the point where the products work, customers are using them, and the value is proven. The video market got us to the start line, and now we are scaling across the AI economy.

Get Involved

For AI Companies: If you’re building AI services and want to enable real-time micropayments, schedule a technical demo.

For Creators: If you’re tired of platform economics and want 90% revenue share paid daily (not 50% paid monthly), join the Veeeu waitlist.

For Developers: We’re building in public and will be open-sourcing components. Join the developer community.

For Investors: We will be launching our seed round soon. Request our investor deck.

For Everyone Else: Follow along. The AI economy needs better infrastructure, and we’re building it in the open.

Subscribe now

Leave a comment

We’ve been quiet here for some years. I believe now we have some thing to talk about that everyone can try out, and soon millions will be able to experience.

Micropayments were always a part of the World Wide Web vision.  A set of standards and an HTTP Error message 402 was even created to handle instances where a micropayment was required but not received. Why didn’t they happen?…

The World Wide Web. The original vision of the World Wide Web was an underlying web infrastructure that included a vision to enable instant micropayments for online services without need of login, subscription or payment processors.  It was to be a digital mirror of taking coins out of your pocket and putting them into a vending machine. A digital consumer could purchase a single video, song, or article. Businesses could micropay multiple services (e.g., meeting transcriptions, document processing, satellite images, chatbot conversations). Citizens could micropay for myriad public sector services covering transit, parking and much more. None of this happened and we got the commercial Web instead.

The Micropayments Trllemma blocked digital micropayments. Three challenges blocked the vision. First, the cost was too high to efficiently process payments under a dollar. Second, the technology didn’t exist to give micropayers control of their funds to micropay anywhere without being locked in.  Third, there was no universal single tap and pay online checkout experience, which degraded convenience. By 1999 the first wave of micropayment initiatives were shelved. Over the next two decades over 100 projects were launched. These attempts didn’t survive the double hit of the market ‘free content for advertising’ wave and the cost, control, and convenience trilemma of problems.

Micropayments work in the physical world with cash. Micropayments have always existed because people paid each other with coins and bills.  Physical micropayments work because the buyer and seller in a transaction do all the work bringing cost down. Physical micropayments work because coins and bills are portable between systems (e.g., bag, wallet, pocket, safe) and the bearer exercises strong control. Physical micropayments work because it’s convenient for the micropayer.  These qualities solve for cost, control and convenience and are different to how digital database and blockchain systems function. 

Finding a digital way to replicate physical assets transaction would solve the aforementioned trilemma challenge.

TODA began in early 2016, when Toufi Saliba called Dann Toliver at four in the morning with a wild idea…

That answer to the trilemma challenge is TODA File technology, the result of a 7 year deep-tech initiative led by researchers from TODAQ Labs, Cambridge University, the CRDC, UCL and  others.  Our thanks to the UKRI Ministry and ARM Technologies for their support.

TODA began in early 2016, when Toufi Saliba called Dann Toliver at four in the morning with a wild idea. They were experiencing cost, throughput, and latency issues while scaling applications with blockchain components, like a PKI for end-to-end encrypted email. The idea was to give each file a unique number, and use a Merkle tree and a fixed number of validators to ensure ownership was limited to a single node in each block of time, using just the computational power of the devices themselves. They worked on it as a science experiment for months, trying to get traction on the problem, until the pieces finally started to fit together.

In the summer of 2016 Lila Tretikov and Todd Gebhart came onboard and helped guide the early strategic steps. Later that year Hassan Khan joined forces, forming TODAQ, the first venture on TODA. Adam Gravitis took the CTO role at TODAQ in spring 2017, managing the engineering team’s work on the reference implementation of the protocol. The researchers, implementors, executives, and partners who have joined along the way would fill more than this article.

The first use case we focused on was supplementing cash in cash-primary regional economies in a nonextractive way. Doing this would improve countless lives by enabling efficient delivery of financial services. Regional products like M-Pesa and bKash help prove this hypothesis. A globally available system would have the potential to help billions of people, and a system without profit extraction could offer even greater benefits.

Doing this turns out to be rather difficult. In fact it’s impossible without something like TODA. In a world where digital things are just information, a third party must always manage that information. They must be compensated for that work. That compensation extracts value from regional economies. Deliver $100 in aide and move it around via credit cards, and in just a few years over $90 of it has been extracted from that regional economy. With the current implementation of blockchains like Ethereum and Bitcoin there can even more extraction. This is not a small problem.

It’s hard even with TODA. Building the infrastructure to maintain a locally operated, globally interoperable TODA installation can be done, with relatively minor expenditures. Even better would be relying solely on people’s mobile devices, an active area of research.

Having options like those available at all is due to having this hard case as our primary target. This shaped the protocol, forced us to hack away at inefficiencies and to focus maniacally on places of value leakage.

We embraced fragility, turning all the robustness and resiliency knobs down. This gave us access to the hardest use cases, those most sensitive to extra economic weight like micropayments, at the core protocol level. Adding robustness for use cases that need it is easy in comparison: you can easily have as much robustness as you are willing to pay for.

It also forced us to prioritize asymptotic computational complexity over constant factor optimisations. How adding more nodes impacts a single transaction, for example, is central to the protocol. How much work a single transaction requires in isolation is secondary. Indeed, there are a great many optimisations we could bring to TODA, but they add complexity and need to be weighed carefully. Asymptotic limit usage scaling. Complexity limits feature scaling. Flat foundations are easier to build on.

We made a number of other choices in those early days that were counterintuitive or contrary to market trends. We got a lot of pushback for it. In some cases even we weren’t sure they were right, but in hindsight it’s clear they laid the groundwork for TODA being what is today.

We decided early on that we didn’t want the TODA File Protocol to be a source of revenue for us, or for anyone. It was clear that the economics of blockchains, which are necessary to allow them to spread trusted state management over many untrusted nodes, also preclude their use in cost-sensitive use cases, and trend toward volatility, extraction, and consolidation. The deep integration of tokens causes them to behave more like products than protocols. Important products, that provide an important service, but TODA needed to take a different course to achieve our goals. So we worked to remove the internal currency from the protocol, and focused the revenue model on partnering to build products and services on top of TODA while leaving the protocol pure.

Internal protocol currencies cover a multitude of sins. Any time there’s an incentive misalignment, or extra work needs to be done, or you need to keep someone honest, you can throw economics at it to sort it out.

It’s the duct tape of decentralised protocol design. If the protocol doesn’t understand a currency then those patches have to be torn out, and all those areas ground down and restructured. It was a lot of work, and it wasn’t clear it was even possible.

When we finished, though, what we were left with was something small and simple and clean. A protocol that describes how to create a globally unique digital thing, how to efficiently transfer the ownership of that thing, and little else.

By extracting the base currency we’d forced efficiencies, removed a variety of economic weaknesses, and made it a protocol instead of a product. Removing the internal currency means all things created on TODA are treated as equals. It means the protocol works for any kind of asset. Anything you can print on paper, we used to say. And more, as it turned out.

Another big decision came in balancing privacy and compliance. This is actually quite a bit easier in a cash-style system than a stateful, managed model. Cash already has a decent story around privacy and anonymity, but regulatory compliance is difficult because it’s hard to prove where it came from. TODA’s proof of provenance changes this dynamic, though, by allowing files to have additional metadata attached during each transfer. This metadata could contain identifying material, or proof of limited attestations (like “I am legally allowed to drive” or “this is a legitimate business account”). Voluntarily adding this to the file’s POP would allow entities like financial institutions, governments or other large organizations to fulfill their compliance requirements.

In order to preserve the ability for this particular file to be used in those use cases, then, one ought to ensure that its POP contains all the required material. Otherwise it will be difficult to use in those situations, reducing the utility of the file. Thus we render unto governments their due for assets they manage, while keeping impedance low for things like stickers, songs, and micropayment assets.

Over time we came to identify the qualities physical things had that digital things lacked as transferability, agency, possession, and permanence. Transferability means when the owner transfers it they don’t need to notify a third party. No one else has to do any work, no one else needs to be compensated. We refer to this ability to be transferred losslessly as value preservation. Agency means you can do all the things you can usually do with a physical item: give it away, sell it, rent it, lend it, and so on. Possession means the source of truth of the ownership is in your hands, and decisions made in some corporate headquarters can’t take it away from you. And permanence means if you take care of it well there’s a chance you can pass it down to your kids. Those qualities imbue every file in TODA, providing an important part of the foundation for restoring ownership and control of identity, assets, and data to every individual human.

Today, the TODA File Protocol has been implemented as a Web integrity network. The integrity network powers issuance, updating and transferring between any system of a new type of unique digital bearer asset, called a TODA file.  TODA files are a new file type that are digitally unique bearer assets (e.g., coins, bills, credentials, and paper documents. TODA files can be controlled, stored, and updated by any empowered system, and can be transferred without need to connect to the issuing system. These qualities strip transaction costs, restore bearer control, and extend the convenience of universal tap and pay of physical store checkout to the online experience.

During 2023, the first version of a working micropayments solution called TAPP (Tapp And Privately Pay) was built by TODAQ Micro which you can find out more about here: https://www.todaq.net. It’s a low code solution allowing any digital content or service to accept micropayments, provide instant paybacks and micro-distributions.  Everyone controls their own assets and records, and best of all anyone can issue any type of asset that can be micro transacted as well.

Rather than explain a brand new tech stack, it seemed easier to just put it into everyone’s hands with an accessible, powerful and fun demo.  So the team went retro. In addition to bringing on sports, media, entertainment, gaming, healthcare, education and AI partners to deploy micropayments, we’re very pleased to release old game with a new twist.

PONG, created in 1972 was one of the earliest video games.  It is available online for free and is largely unchanged from it’s basic 2D form from 52 years ago.  Earlier variations of micropong included a micropong where the form factor reduced the playable game to the size of a penny, and a micropong version that allowed players to control the game through a microphone.  As homage, we thought the natural next step of micropong, was a micropayable release that demonstrated the capabilities of TODA files and TAPP micropayments.  Given the years of work that led to that moment, we had no idea that it would take another 8 months of work to make the transaction dance of micropayments, instant paybacks, micro-distributions, and more flow in just the right way.

This latest version of micropong fuses micropayments, gaming, rewards, and music.   The micropong experience starts when an unknown visitor goes to the micropong  website.  They instantly pay 30 cents, no processing fees, and start a 2 person PVP game. Player 1 also picks a song which will play for both players. Player 1 invites one of their friends to join, who also micropays 30 cents and joins as Player 2. The game session now has 60 cents. They play each other to be the first to 10 points. The winner is instantly paid 40 cents, the music artist is instantly paid 10 cents, and the game treasury gets 10 cents. The music studio that helped produce the chosen song is paid a penny from the artist’s 10 cents.  The audio marketplace that provided beats and samples for the song is paid 2/10th of a penny from the artist’s 10 cents.  Three emerging and ground breaking musicians - Tara Jam, Sean Leon, and Nanu - joined the micropong team so game players could enjoy their music.   Their larger goal was to change the game and show that fair, transparent, and real-time compensation for all artists is possible.

For consumers, these kind of one TAPP micropayments will enable a new complementary option to subscriptions. Non-subscribers can directly micropay for content or micropayable content can be shared by empowered subscribers. For streaming and publishing platforms, this will add revenue streams. It’s not just a consumer thing, platforms can instantly split a single micropayment and distribute to sharers, creators, copyright holders, suppliers, and their staff – removing nearly all back office and processing costs. These frictionless circular payment flows between systems and devices apply to cloud API marketplaces, embedded finance and insurance services, municipalities, e-commerce, and supply chains. New micropayment markets will bring power to creators and consumers, generate new sources of revenue, speed up cash cycles, and greatly reduce back-office costs and liabilities for businesses.

You can try out micropong here:

https://www.todaq.net/story-of-micropong

When Ben Horowitz, co-founder of Andreessen Horowitz wrote The Hard Thing About Hard Things, he provided an invaluable guide to startup management and the challenges of growing a business from scratch.

Within the startup world there is smaller sub-class of ventures called deeptech where there is a whole other complication to deal with as well.

Rather than being driven primarily by innovation, usually with the business model and technical improvements and innovations over what exists today, deeptech ventures are centred around net new invention of technologies that don’t exist and have potential to change the world.

In 2017 when TODAQ was founded the TODA protocol was in its early days. A first whitepaper had been produced and there was a path to produce functioning software that could execute pieces of a new form of ledgerless decentralisation.

The aim was to create a distributed graph data architecture that would allow any data or digital asset to adopt the properties of the physical world, like a piece of paper. Done correctly that would provide a number of benefits, including:

• Unforgeable identity and assets would greatly enhance security and integrity;

• No limits to scale of transactions or assets;

• More powerful ability to create and manage complex, investment-grade, insurable assets;

• Portability of assets and interoperability across any system, blockchain or device;

• True P2P settlement without the need for intermediaries; and

• Greatly improved cost efficiency compared to today’s best-in-class solutions, including removing the need for a settlement gas or token as found in blockchain systems.

By summer of 2019, the first commercial implementation was done, and we created a software API platform called TODA-as-a-Service and executed the first commercial PoCs and projects with it. TaaS worked well and provided improved security guarantees for integrity of assets over what existed in the market and more cost and time savings when settling transactions, but to you had to connect to it to harness TODA, and it was centralised. You couldn’t take the truth of the assets out of the TaaS system.

The much harder work of decentralizing started in early 2020 as COVID hit. There were far more questions than answers at that time, including what would products designed on a decentralised TODA even look like and whether decentralising TODA could even be achieved. For a deep-tech startup this is a terrifying prospect. However there is a saying that is useful during these sort of times. When you’re going through hell, keep going.

Collaboration started with researchers out of the University of Cambridge and University College London and the TODA project turned into four separate but streams of R&D: TODA, ADOT, FEWD and CBDC.

TODA. TODA is a distributed graph data architecture that secures any type of data with a cryptographic data structure called a TODA file to create a unique digital asset. TODA files are digital things that have the qualities of physical things. Transferability. Permanence. Agency. Possession. TODA has achieved the milestone of creating a successful decentralised design with commercialisation starting in 2021. The next phase of work will be preparing the first papers, specifications and shareable for publication and starting open sourcing.

ADOT. An Object Centric Application Protocol that enables software systems to create, interpret, authorise, transmit and transact TODA files between each other without requiring system or data integration. ADOT is a stack of technologies and protocols that work together to make TODA accessible and usable by everyone, everywhere. Limited components of ADOT relevant to building and deploying the first commercial product GOTO have been designed, tested and deployed, but there are several project components still in active R&D.

FEWD. Fair Exchange Without Disputes - Many blockchain systems have atomic swap capability to enable a two way exchange with simultaneous settlement and no escrow agents or brokers, but the assets you can do it with is limited and constrained to within that blockchain system. What is missing is a universal atomic swap solution for everything. Any type or combination of digital assets, including not just TODA Assets like TDN, but also custodial assets, global trade goods, bitcoin, ether, NFTs, twitter accounts, AWS credits, book manuscripts, dog pictures, anything, can be exchanged without relying on an escrow agent.

CBDC. Central Bank Digital Currency. Using ledgerless decentralised technologies such that CBDCs could be designed without facing the tradeoff of either accepting the responsibility of operating a core ledger or ceding control of the CBDC. Thus bringing the qualities of physical cash: (possession, privacy and no fee extraction) to digital money and payment systems.

You can see more background on these projects here and each will be publishing papers and books starting in 2021 and 2022, and establishing the first developer builder community.

After many long months of work the modern design, testing and hammering of a new modern TODA that would enable decentralisation was done and by the winter of 2021 the working software infrastructure and functional demos and PoCs were being coded. Concurrently we started learning from the market the first place to put it into useful action. By summer of this year the GOTO product began coming together.

Apart from commercially shipping product, the next exciting phase of open sourcing will now also begin in stages. We’re grateful to have the first open source leader, Red Hat, along as a partner as this phase begins. Starting next month the first partner technology teams will join the TODA ecosystem to collaborate on specifications and shareable code, and to build TODA enabled systems and apps, as well as convert existing systems to be TODA capable.

Share

Subscribe now

StockFam TV interviewed Sheldon Inwentash, CEO of TODAQ investor ThreeD Capital on TODAQ, the art of pivoting as a startup, bringing a new form of identity solutions to IBM and Red Hat, and the future of the Web 3.0.

Click the link to catch the discussion here:

Disruptive Tech | Discussion W/ Sheldon Inwentash CEO ThreeD Capital

Share

Subscribe now

It was a pleasure for the Capital Club Dubai to host a discussion focused on “CBDCs, Crypto, NFTs – Creating Digital Value" in another episode of Technology Heroes Unplugged.

The topical dialogue on today's asset classes in crypto took place with Hassan Khan, CEO of TODAQ and Dr. Geoffrey Goodell, Senior Researcher at University College London, moderated in an engaging exchange with Naveed Minhas. The conversation focused on the hype and speculation of NFTs that needs to be understood relative to the true value of crypto being a means of value exchange and an emerging asset class.

The discussion further explored Hassan and Geoff’s perspectives across digital value, gold standards, CBDCs features, privacy of crypto usage, ecosystems that crypto helps evolve and macro trends that support crypto. Are NFT’s a bubble or unit of exchange? It concluded with perspectives on monetary policies and the liquidity of crypto versus a timeline to deliver stability for emerging assets.

Catch the discussion here: watch on Youtube or listen on Apple Podcasts.

If you enjoyed this content please subscribe and share:

Subscribe now

Share

TODAQ is pleased to announce they have finalized their agreement with IBM to onboard their software to the IBM Cloud, starting with the Red Hat Marketplace and the OpenShift Platform. The TODAQ team will be working with the IBM and RedHat staff to bring the first specific product to market this fall.

That first low code software product will be focused on empowering developers and companies to be able to adapt their systems and apps with Credential, Identity and Access Management solutions where Credentials and Capability Tokens:

• cannot be forged;

• are self-tracking and hold an internal immutable audit trail;

• update each minute with their own internal unique One-Time-Password and provide the potential to remove steps and weaknesses with today’s passwordless and 2FA solutions;

• become truly portable and interoperable across any system or device; and

• Allow for true P2P verification and transactions without need to connect back to central system to provide offline resilience; and

• Are based on technology efficient and fast enough that verification can practically occur with every use and action, so full Zero Trust.

• Privacy and Need-To-Know. Ensure ownership, control and privacy stay with the true credential owner while giving the verifying counter party the transparency that they need.

Why do we care about empowering customers with these features?

Because the frequency, impact and cost of Cyber Breaches continues to rise and most breaches start with credentials being copied, forged or manipulated.

Because the costs and regulatory burden of proper Verification and meeting Know-Your-Customer requirements continues to rise. This keeps billions of people out of the market, not to mention eating into the margins of businesses. It’s not just financial services where this matters; the growth of marketplaces for digital asset, E-sports and other services is also pushing demand.

Because sovereign ownership of identity and data is not just about human rights, it also makes good business sense, reducing liabilities and costs while allowing for improved provision of goods and services.

Empowering people and businesses to be able to own one set of credentials that can be used anywhere, across any system or device is no longer a nice to have but a transition we must do. Technology alone never solved anything but without a step change in technological capability we will never get there, which is what TODA was designed to solve.

What do we mean when we say credentials and capability tokens?

There are many facets of credentials that point back to an individual or organization and the first TODAQ product will allow any and every one of them to be equipped with these properties.

Capability Tokens give users access rights and privileges with software systems and apps, and there are a number of uses and applications of capability tokens in the market today. The weakness is that the integrity of the capability token is trapped within the system that issued it, so not very portable. We live in a world with tons of systems, with even more interconnections and integrations, so things become complicated, expensive and fragile quite quickly. By providing distributed capability based security, this is a gap TODAQ aims to fix.

Morgan Stanley estimates this as a $40 billion industry that is still nascent with tremendous room for growth. Consider that 80% of this market is just focused on serving the enterprise work forces as the costs and other missing gaps restrict its size and accessibility to everyone.

We look forward to empowering IBM’s customers with some credential superpowers in the near future to solve their business Verification, Fraud, Cost and Automation, as well as Customer Experience needs in the near future. We’re not stopping there, TODAQ Credentials and Access Management product will be available for everyone.

TODAQ is also working with technology partners to provide TODA powered Identity and Biometric Verification Solutions, as well as Data Ownership, Compliance and Privacy Products that will plug and play into into any TODA enabled system.

If you liked this post please share with anyone who may be interested and subscribe to get the latest news.

Share

Subscribe now

TODAQ CEO, Hassan Khan, had the pleasure of being hosted by Stock Fam Group on their YouTube channel for an in depth interview by hosts Sean Khatibi and Graham ‘Hammy’ Skelton on TODAQ and the potential and uses of the ADOT Web and TODA|ADOT technology. Check out the rich discussion here.

Stock Fam Group is an investor awareness and education community with a specific focus on select disruptive technology sectors and companies, and a mantra of “Know what you own”. In addition to the website they are active on Discord, Twitter and Youtube.

Co-Founder Graham Skelton describes the vision as, “The world is evolving around us at a rapid pace and technology is taking us to new places each and every day, including how we invest. As a new investor, or even a veteran investor the flow of information can be challenging to filter. Stock boards are full of vermin and snakes fueled to drive fear and capitalize on greed for personal gain. The goal of the discord investor groups is to reduce the flow of misinformation and weaponize all investors in companies that they love with solid information, DD and a constant stream of information in real time. The power of sharing knowledge and experience in a platform like this is incredible. The more we can help one another to learn, the better off we all will be as investors. As long as we all can share in that same vision, we can all profit together and reduce the effect of those that push misinformation and fear.”

We wish the Stock Fam Group continued success and growth and can definitely connect with the values of hard work, due diligence, authenticity of assets and information, and community (as well as the Oxford comma).

If you enjoyed this content please consider sharing this with others and subscribing to the TODAQ Press!

Share

Subscribe now

Our mid month round up of the latest news from the TODAQ Press.

Projects and Partnerships

TODAQ to talk about the future of containerization and digital assets on the Dave and Gunnar show, with David Egts & Gunnar Hellekson of Red Hat. - READ HERE.

Guest Post: Laurie & Camille from Base Alpha discuss integrated platform solutions for supply chains and exchange markets - READ HERE

Coming up: TODAQ talks TDN digital cash on Gratomic's Youtube channel covering clean, ethical mining of vein graphite for EV batteries - READ HERE

The 2nd part of our Nuclear Power coverage with Beyond Supply Chain: Nuclear Energy & the Hydrogen, Isotopes, Power and Carbon markets - READ HERE

Products and Technology

TODAQ USA to introduce a peer-to-peer Green X Change solution, powered by TDN, to tackle power conservation - READ HERE

Chief Science Officer Dann Toliver discusses the shape of data and the Project MACAW development of an efficient ADOT serialization format - READ HERE

People

TODAQ is pleased to introduce Adam Gravitis our founding CTO, primary co-author of the ADOT Protocol and led the development of TODAQ products - READ HERE

Learning Links

Europe, the GCC and Asia get onboard - Bahrain, Switzerland and Singapore move to give legal ownership standing to digital records and assets - READ HERE

If you enjoyed this content and would like to see more, please subscribe to the TODAQ Press here:

Thank you!

Share

Subscribe now

Bahrain creates history as first nation enacting laws giving legal standing to Electronic Transferable Records from Albawaba Business.

Switzerland issues law giving DLT based securities ownership legal standing from Coindesk.

Singapore introduces Law to give digital trade instruments legal standing from Global Trade Review Publication.

We will frequently share content from customers, partners, technology providers, research collaborators and standards and regulatory thought leaders. Base Alpha was among the first integration and business solutions companies to adopt the TODA/ADOT technologies from TODAQ, and deliver integrated platforms and solutions for clients. Authors Laurie and Camille from the BaseAlpha management team discuss some of their impactful project work across Europe and the GCC and approach to delivering end customer value.

TODAQ Press.

Laurie Fischer, Director Strategy & Risk and Camille Lahoud, CTO Base Alpha

We are a UAE-based technology integrator and digital platform developer with two core business verticals at present. The first focuses on supply chain solutions in the waste management sector, and the second on data platforms to facilitate the exchange of high value, low liquidity assets. We are using TODA/ADOT software products as a fundamental part of our platforms’ backend technology stack.

We’re pleased to announce our first Base Alpha product, integrating a TODA architecture with IoT platforms in a waste management value chain, is in the process of live launch.

Exchanges to facilitate the trade of high-value, low-liquidity assets are in development, with first full commercial launch releases expected later in 2021. We’re looking forward to providing updates in due course.

Base Alpha’s Containerisation Strategy

Whether looking at low value construction waste or high value artwork, the containerisation of digital assets is central to how we think about platform design. The platforms use TODA files as containers for generating and capturing immutable data records, creating the digital twin of the asset they represent. Working with TODA to architect the most efficient and secure file structures, we capture the core data related to a real-world asset in the container’s payload. As the asset progresses through its real-world lifecycle and gets transacted through a supply chain or marketplace, additional data inputs, attributes and histories are added to the container. These additions occur in a range of ways, including the hashing of metadata at the point of transaction and the combining of multiple files with distinct origins into single containers.

The platforms will deliver particular benefits in markets containing a number of disparate actors, processes or systems through which the asset will pass. The TODA architecture allows each of these actors or actions to input data as an independent, authenticated participant in the ecosystem. For example, a high-value artwork could repeatedly change possession and ownership. It will also be subject to multiple external inputs, including valuations, condition reports, restoration or maintenance actions, insurance, transportation and storage. A part of Base Alpha’s value for our customers is to securely and easily allow the substance of each of these interactions to be recorded or input by independent but authenticated actors. This additional information will be stored within the asset’s container and can be traced and verified by interested parties, whether that be auditors or potential buyers, to see who has done what, when and how.

Building the asset’s data stack in this way, with multiple external but validated inputs, contributes to an accumulated proof of integrity and, ultimately, an asset’s value. Liquidity is also improved with the ability to settle transactions with integrity, speed and efficiency built in.

We typically integrate a number of other technology and design features that enhance the power of the platforms. Examples of note include:

• Technology integrations to tie the real-world asset to its digital twin. This can include anything from basic photographs to more sophisticated attribute capture. Hashed as part of the asset’s original file payload, this will enable the digital asset to contain and carry with it a physical fingerprint from its real-world twin.

• An integral data normalisation strategy to allow for targeted data analytics powered by bespoke platform AI/ML modules. These modules can learn from and analyse either the container’s payload itself (for example for image recognition, classification and validation purposes) or data related to transactions and participants on the platform. The latter will power platform ranking and recommendation engines that, through tracking assets and transactions through time, will allow participants to build reputations and expose good or bad performance.

Being Platformists is our identity as a company, and that means platforms that lower costs, increase asset and product values through provable quality and authenticity, and open up previously inaccessible markets and distribution channels for our clients and end users.

Want to speak with Base Alpha? Please reach out here.

If you enjoyed this content and would like to see more, please subscribe to the TODAQ Press and share this post with someone who might appreciate this content.

Thank you!

Share

Subscribe now

It isn’t entirely clear initially what should be included here. The byte layout of a GIF? That doesn’t meet our test of being “mechanically relevant”, meaning something that is important at the protocol level. The layout of an Ed25519 signature? That’s mechanically relevant, but very specific: the parser shouldn’t need to know anything about signature algorithms to do its job of taking bytes off the wire and packaging up their shapes.

How about a list of hashes? That’s a shape that is both mechanically relevant and also at the right level of abstraction. It allows the first stage of interpretation to proceed without needing to know the gnarly details of byte configurations for arbitrary blobs, while still giving it enough structure to handle making requests for more information and linking together data as it is received.

What about specific lists of hashes, perhaps ones that are mechanically relevant? Is it enough to have a single generic list of hashes, or does that need to be special-cased to make specific shapes in cases where the hashes in the list have mechanically relevant interpretations? The right answer seems to include a certain amount of polymorphism, where the generic list of hashes represents a fully parametrically polymorphic list type (including heterogenous lists, no less), and the specializations are themselves parametrized over equivalence classes of shapes. This yields a good blend of power and flexibility while fitting into a convenient byte-sized wrapper, and makes the macaws happy.

— Dann

(7 min read)

The TODA Note (‘TDN’) Program initial architecture was drafted on 1 July, 2017 Canada Day on the shores of the Trent Lakes in Ontario, with the exact view at that moment captured in the photo above. Readers of this article are being asked to accept the truth of the photo above. However, if the TODA-ADOT asset containerization software had already been developed in 2017 they would be able to directly verify many of the claims around that statement by directly inspecting the containerized photo.

The idea of TDN was around well before 2017 and actually first appeared in 2016 with the first discovery of the TODA Protocol. During this initial year TDN had been an embedded part of the TODA Protocol’s design as an integral settlement token or ‘gas’ during consensus, but the TODA Protocol’s efficiency and data structure enforced consensus led to its removal in that capacity. For more into the TDN history and plan, please see HERE.

The TODA Note (TDN) is a 2^37 supply of a decentralised, commodity backstopped stable digital cash built on the TODA and ADOT Protocols, and is designed to offer global, long-term, stable, and real economy utility and be the native payment means for the ADOT World Wide Web. It is meant particularly to support the trade and barter needs of general society, businesses, and the financially excluded populations of the world. A few of the underlying qualities that were designed into it were expressly meant to counteract many of the concerns today including excessive money printing, speculation, lack of underlying tangible value, and demonetisation and de-banking risks.

• Combining physical cash benefits & digital speed: today’s digital currencies lack cash-like benefits, but with containerization it doesn’t have to be that way.

• The right to own and the right to transact: The unfolding Gamestop market story highlights the need for democratization of free markets and providing a level playing field for all buyers and sellers.

• Verified authenticity of underlying assets: The complexity of today’s markets and digital systems make it costly verify the assets underlying currencies, stocks, debt and other instruments.

TDN is not like any form of electronic currency or money that exists today, conventional, blockchain or otherwise. TODA and ADOT replicate the physical world and TDN is containerized digital cash with unique bills, that behaves like physical cash, but with digital speed and remote transaction benefits. It’s designed purely to hold value and for long-term usefulness.

The set value of the underlying reserve backstop behind each TDN (not a backing and not a peg) would be set initially at a minimum level of one cent and increased over time as the size of the reserve grows, depending on the value of the reserve and the amount of deployed TDN. These adjustments would be controlled and declared by the Foundation. The value of TDN is based on the combination of the underlying containerized reserve and the TDN utility value. Usually for underlying assets, direct verification and inspection is out of the hands of the normal consumer or retail owner, and the custodian and third party auditors have to be relied on. This is a gap that we wish to solve with the TDN reserve backstop.

The intent with the TDN program is to put it under a non-profit foundation in 2021 which would oversee minting and distribution of TDN as well as management of the underlying reserve backstop.

The backstop will be a basket of useful battery and digital economy commodities combined with land and some SDR currencies like USD. The foundation would report on minted TDN, deployed TDN and the underlying containerized reserve backstop assets through publicly accessible rooms in the ADOT Web which would allow anyone to directly verify details P2P.

The first commodity that will be put in the backstop, through a TDN for graphite deal with Gratomic Inc, is battery grade graphite. Gratomic Inc. is a Canadian resource company offering to bring environmentally-friendly, high grade vein graphite to the Global electric vehicle market. Their Aukam Graphite mine is reaching production in Q1 2021, they are planning to institutionalise graphite through digital currency and metals exchanges, and conduct continued R&D of value-added graphene manufacturing opportunities.

Gratomic will supply TODAQ with battery grade Aukam graphite in a USD $25 million contract (payable in TDN) for the asset reserve backstop. TODAQ USA, the native solutions provider of TODAQ, will be putting in place containerized graphite tracking & tracing from mine to market as the first step of ensuring that verified authenticity is possible.

A simple example view of the first containerized atomic swap transaction of TDN for graphite on the ADOT Web would look something like this.

• The transacting parties meet in an ADOT Web trade room, removing the need for technical integration of ERP, supply chain or payment systems and minimizing requirement for brokers.

• Gratomic puts the containerized graphite into an outgoing ADOT transaction container 1 which is paired with an incoming ADOT container 2 on which Gratomic specifies the requested TDN payment amount

• TODAQ verifies the contents of the the offered container 1 and puts the requested TDN into container 2 when it is satisfied

• The containers execute an atomic swap transaction on the TODA Protocol, using the TODAQ TODA-As-A-Service (‘TaaS’) Ring API for this first transaction. (TODAQ is also planning to release a multi Ring capability this year and part of the path to decentralisation, which means future transactions would not need to solely rely on TaaS). Each side is guaranteed simultaneous 100% settlement. Transaction confirmation takes a second and final deterministic settlement of ownership transfer is completed within a minute.

• The graphite asset containers now register the backstop reserve as their owner and each TDN bill now registers Gratomic as its owner.

• If either party shows these assets to anyone else the integrity of the graphite or TDN can be immediately verified and once settlement is complete

Once the containerized graphite is held by the STAR Foundation in its reserve, there are many guarantees around the graphite that would be relevant to a holder of TDN to inspect and verify:

• that the company sourcing the commodity is properly incorporated, with the right licenses and permits to carry out operations

• that the graphite is of the required quality and specifications, supported by the results of internal and external testing reports

• That the graphite was cleanly and ethically mined and there are verifiable metrics to measure chemical or carbon footprint

• That the transaction of TDN for commodity was properly purchased with all required documentation and agreements

• That the containerised graphite now owned by the backstop reserve is properly stored in secure custody, and any ongoing inspections and audits can also be verified P2P as part of the overall graphite asset.

Each 2 tonne bag of purchased graphite would have a set of linked containerized files that carry the verifiable proof of the elements above including meta details like source, location, timing, source, chain of title, etc. Future articles will deal with how the truth of asset creation, credentials and identity are tied into the proof of a containerized asset. This is important as it addresses a number of risks, including having a fraudulent or forged details be introduced into the reserve backstop.

The next articles will dive further into address basic market capabilities that are needed and being built in the ADOT Web to power the TDN market including asset minting, treasury operations, custody, trading, payment integration and reporting.

If you enjoyed this content and would like to see more, please subscribe to the TODAQ Press here:

Thank you!

Subscribe now

There is nothing benign or helpful that would result from this type of surveillance, from the reselling of analysed personal data to advertisers or or third parties, or from the data collection vacuum cleaner pulling background noise that might involve second party, non-related confidential communication.

SPOTIFY’S LATEST INVENTION MONITORS YOUR SPEECH, DETERMINES YOUR EMOTIONAL STATE… AND SUGGESTS MUSIC BASED ON IT

An individual concerned about confidentiality, privacy and strong ownership of their assets including their data could decide not to use Spotify or other app ML/AI powered powered services that employ this kind of pervasive surveillance. Consumers can vote with their dollars to use Spotify or not, but it doesn’t stop there.

There is a social recognition and regulation to protect people from the effects of second hand smoke from cigarettes. What if you had decided not to use Spotify for this reason and are now next to others using such apps that are monitoring you now as well? Should there be a similar approach to protect people from second hand surveillance when they did not have a choice to assent or have a fair exchange and be properly compensated for the use of their data?

We are here to restore strong ownership of your digital assets and the right to transact, which also includes the right not to transact.

Is there a better way for businesses selling streaming music to earn money that could move away from relying on increasing surveillance of the product (you) in order to sell your data to the true customer (advertisers). We believe so.

'How to Get Rich Sabotaging Nuclear Weapons Facilities' by Matt Stoller

(6 min read)

Before the internet, games were slightly more primitive and one of the children’s games that demonstrated the friction of pushing integrity and trust through multiple parties was ‘Broken Telephone’. You didn’t have to line up many people and whisper a message starting from one end to the other for the message to degrade or be lost entirely.

This problem plays out in our modern markets for high risk and high value assets and examples are everywhere. Take the nuclear power supply chain as an example. Understandably we would all want stringent quality and safety standards in place for all the critical parts composing our nuclear power plants. Like with planes, trains, medical equipment, pharmaceutical drugs, or protective wear and equipment for first responders, small errors can result in loss of life. If we were to take something simple and essential like a pressure gauge, it’s something you absolutely need to work properly and if we thought about what risks could result from a faulty pressure gauge in a nuclear plant here is just a few:

Unauthorised or uncertified suppliers could create risks.

Faulty or forged equipment could be introduced even if the documentation is correct.

The documentation could also be forged or incorrect.

Often certified and inspected equipment have appended stickers, stamps and other marks which are also at risk of manipulation or forgery.

What results from the safety requirement and risks are: a lot of complexity which creates its own safety risks; necessarily detailed and inflexible regulations and standards; multiple tiers of suppliers and regulators; and a resulting process that is slow, full of friction, and expensive. That expense gets tacked onto the $/KWh consumers pay for directly in their power bill.

Imagine at a power plant we then wanted to be able to securely and efficiently verify that a pressure gauge was built by authorised supplier(s), with the right materials, transported, installed and maintained correctly by certified personnel; and with all the required safety and performance inspections and audits:

The greater the number of disparate parties and systems that assets need to pass through, the higher the accumulated cost of providing verified authenticity to an end buyer further down the line. While database or blockchain based systems provide solutions for legs of the journey, the integrity of assets (the proof of their uniqueness, completeness and provenance) is still trapped inside that system or platform. Moving that trust from one system to the next is where the reconciliation, settlement, interoperability and integration costs start to pile up as illustrated below.

In many cases with fungible assets like dollars as an example, centralised solutions with interconnected central banks, banks, and payment providers can create workable solutions at scale. Even with these advanced, hyper connected systems quality, cost and time leaves a lot to be desired, especially for those without a seat at the institutional market making table (see our observations on Gamestop and the revenge of main street).

There are multiple levels where digitisation of this scenario can provide improvements, but we hold that a single system or platform of trust to ‘rule them all’ is neither practical nor desirable. These centralised systems create systemic safety and security risks including complex breaches and hacks, with Solarwinds being the latest in a long, continuing line of examples of the increasing fragility that results when we connect a spaghetti of software Jenga towers together. In cases where such systems rule, it is also usually to the detriment of small, medium and even large businesses that are the workhorses of innovation and enterprise and make up at least half of national economies.

The TODA asset and data containerisation can provide an elegant solution that is compatible with any cloud, system, blockchain or device. The container carries the the proof of integrity with it as its ownership is transferred along the value chain and the container and its payload move from one system to the next. The ability for that accumulated proof of integrity to be transferred in seconds with a minimum of energy collapses the reconciliation, settlement and integration costs.

Let’s re-look at our humble pressure gauge destined for the nuclear power plant. Now each digital record and attestation that is linked into the aggregate pressure gauge asset can pass with verifiable integrity between organisational and system silos:

The impact on cost reductions and time can be dramatic, going from exponential cost increases to a flattened logarithmic cost curve, and saving effort on reconciliation and checking, disputes; being able to instantly detect manipulation; and being able to embedded containers with smart rules on handling to reduce administrative and back office burden.

This is a surface treatment of a market issue and solution where we can and will become more detailed in the future, though a few things are worth mentioning at this stage.

First, while the applications and uses of TODA asset containers and the ADOT transaction and room containers are almost unlimited, TODAQ only focuses on providing the ability to create, manage and trade containers. On the back-end we provide distributed infrastructure-as-a-service software for our customers to create and manage their own containers and integrate into their own systems and apps. At the front-end we are delivering a product we call the ADOT Agent that is like a ’Slack’ for Digital Assets, allowing anyone to own any containerized asset and travel anywhere in the ADOT P2P Web to trade with anyone else without brokers. Our customers (cloud & tech services companies, tech dept’s of enterprise and government) have the domain expertise and solutions that are relevant for the P2P markets and supply chains in their specific sectors. As a deeptech company, we just provide the containers. Any errors in the description using the nuclear supply chain above are our own.

Second, this scenario includes physical assets and people, and a very good question is how is the physical world bound together with the containerized digital world in a unified business solution to improve performance, trust or efficiency? This question is probably a whole article series or net new publication like the Digital Credentials Press. We will dive much further into this topic but for now will leave at this. You can containerise credentials for regulators, licensees and vendors and all staff  to provide an immutable digital fingerprint proof that is embedded into every containerised file or asset every time a relevant action is taken.

Third, let’s pretend a treated metals supplier right at the start of this supply chain for the pressure gauge parts creates a digital record of the stainless steel they sold to the gauge manufacturer and embedded the proof of their containerized supplier credentials into that asset. That stainless steel file container would become part of the proof of the pressure gauge. The pressure gauge now transfers through multiple parties and is installed in the Nuclear Plant. An inspector shows up much later and inspects the physical gauge and queries the containerized digital asset. The inspector can verify the stainless steel asset container P2P in under a minute but how does the inspector know right away that it really came from the metals supplier and wasn’t some containerized record that was fraudulently added by bad actors somewhere along the route? There are multiple ways to attack this problem in the TODA-ADOT world and doing all of the approaches concurrently would be prudent. For now, we will leave two bread crumbs.

• One of the ways we determine authenticity of lineage for people in the physical world is through checking DNA of the parents, and thinking about how the physical world operates is always a good starting point for thinking about how the ADOT Web functions. In the digital container world you can have container ‘file types’ that spawn other containers which contain the cryptographic DNA of the parent. This simple element becomes another powerful check against forgery when employed correctly.

• So let’s say the metals supplier created a parent container file type called ‘Supplier X treated metals file type’ and used that to spawn a ‘stainless steel’ asset container they transferred to ownership of the gauge manufacturer and so on down the line. The metals supplier could publish the proof of the file type on their website or ADOT room, but that means the inspector has to go check a third party which seems slow and inconvenient. In the regular http world wide web it is all about websites, and we verify the connection to websites through its SSL certificate which should connect back up through a Trust Chain to a Certificate Authority so we know it is legitimate. In the ADOT Web everything is composed of TODA file containers, and rather than an SSL Certificate Authority we want a file type certificate authority. Which is probably a good place to leave this hanging for now 🧐.

Hassan

If you enjoyed this content and would like to see more, please subscribe to the TODAQ Press here:

Thank you!

Subscribe now