Emerging-technology founders are building faster than the legal categories around them can settle. AI is becoming the decisioning and automation layer; blockchain is becoming the verification and recordkeeping layer. The two are increasingly converging inside single products that touch payments, custody, securities, identity, and personal data. 

A common assumption is that a token standard, or an on-chain restriction is the same thing as a compliance program. It isn't. A control can help satisfy an obligation but it cannot define one, own one, or answer for one when it fails. The fundamental question founders must answer is: have we mapped what the law actually requires, what the technology can realistically enforce, and what still has to be handled through governance, contracts, disclosures, policies, and human review?

To work through this, we spoke with Monique Tronchin (Mo), founder and principal of Interconnect Legal, PLLC, a fractional commercial counsel practice serving early-to-mid-stage technology companies across Web3, fintech, AI governance, privacy, and SaaS. Mo brings a distinctive dual background: a foundation in computer science and economics that lets her engage directly with product and engineering teams rather than reviewing only at the policy layer, paired with deep AML/BSA and financial-crimes experience from earlier roles across global banking and investigations. Admitted in New York, she works at the regulatory frontier where AI governance and digital-asset compliance intersect.

In this Q&A, Mo explains why "compliance by code" is not the same as a compliance program, how AI and Web3 are converging around identity, accountability, monitoring, and proof, and why she addresses regulatory characterization before anything else. Her guidance offers founders a practical way to convert ambiguity into a decision framework so legal work becomes part of product strategy rather than a last-minute obstacle.

1. What’s the biggest legal gap you see when Web3 startups try to scale their compliance infrastructure?

The biggest legal gap is the assumption that “compliance by code” is the same thing as a compliance program.

A smart contract can be a useful compliance control. It can restrict transfers, automate permissions, apply wallet-level rules, manage whitelisting, create attestations, or produce a reliable record of certain activity. But compliance is not something a smart contract possesses. It is a set of obligations owed by a company, protocol, issuer, platform, intermediary, or other legally responsible party.

That distinction matters.

Before a founder can know whether a smart contract control is sufficient, they need to know what legal obligation the control is supposed to satisfy. Is the product implicating securities laws, commodities regulation, payments, money transmission, custody, AML, sanctions, consumer protection, privacy, data security, lending, marketplace rules, or some combination of these? Who is the customer? What assets, data, or funds are being touched? What jurisdictions matter? Who is accountable if the automated control fails or produces an unintended outcome?

The gap is usually not that founders are ignoring compliance altogether. Many are thinking about it early. The problem is that they often build a compliance feature before they build the compliance map.

A token standard, smart contract function, or on-chain restriction may solve part of the problem. It rarely solves the whole problem. Some obligations can be supported on-chain. Others require off-chain procedures, legal documentation, disclosures, vendor oversight, board-level governance, KYC/AML review, sanctions screening, privacy controls, cybersecurity processes, complaint handling, or human escalation.

So the legal gap is really a mapping gap. I usually advise founders to create an obligation-to-control map. For each material obligation, the company should identify: what rule or standard applies, what control is intended to satisfy it, whether that control is on-chain or off-chain, who owns it internally, how it is tested, and what happens when it fails.

The smart contract is where the analysis begins, not where it ends.

That is the difference between a product that has sophisticated technical controls and a company that has sophisticated controls that can withstand investor diligence, enterprise customer review, and regulatory scrutiny.

2. How are you seeing AI and Web3 converge from a regulatory standpoint — and what should founders be doing now?

I see AI and Web3 converging around identity, accountability, monitoring, and proof.

AI is increasingly becoming the decisioning and automation layer. It can interpret information, recommend actions, generate content, detect anomalies, calibrate risk, and act through autonomous or semi-autonomous agents. Blockchain can function as a verification and recordkeeping layer. It can record permissions, provenance, transaction history, asset ownership, attestations, and execution events.

The combination is powerful, but it can also create a false sense of completeness. A perfect record is not the same as a complete account of responsibility.

Regulators, investors, customers, and counterparties will not only ask what happened on-chain. They will ask why it happened, who authorized it, what data informed it, whether the action involved a regulated activity, and who is responsible if the result causes harm.

That becomes especially important as AI agents begin interacting with wallets, smart contracts, decentralized marketplaces, tokenized assets, payment systems, and data marketplaces. If an AI agent recommends a transaction, executes a swap, prices risk, triggers a smart contract, screens a counterparty, or acts on behalf of a user, the legal analysis becomes more complex.

The key tension is that AI and blockchain operate differently. AI systems can be probabilistic, adaptive, and difficult to explain. Blockchain systems are designed to be deterministic, stateful, and record-based. Putting those systems together does not automatically make the combined system trustworthy. Blockchain may prove that a transaction occurred, but it does not necessarily prove that the underlying data was accurate, that the AI output was appropriate, or that the transaction was lawful.

That is where governance becomes critical. Founders should not treat AI governance, smart contract governance, privacy, and financial compliance as separate workstreams if the product operates as one integrated system.

At a minimum, founders should map five things.

First, they should map the AI use case. Is the AI system merely assisting a user, or is it making decisions, ranking options, initiating transactions, allocating assets, approving access, or triggering smart contract execution?

Second, they should map the regulated activity. Does the product touch payments, custody, securities, commodities, lending, stablecoins, brokerage, financial advice, identity verification, AML, sanctions, or consumer transactions?

Third, they should map the data flows. What data trains the model? What data enters the AI system? What data is written on-chain? What stays off-chain? What can be deleted, corrected, restricted, or explained?

Fourth, they should define human accountability. Autonomous does not mean ownerless. Someone still needs to own model governance, smart contract governance, escalation rights, exception handling, user disclosures, vendor diligence, and incident response.

Fifth, they should preserve evidence. If the company wants to rely on AI and blockchain as trust infrastructure, it needs audit logs, permission records, versioning, monitoring, and clear records of when humans can pause, override, or review the system.

The founders who handle this well will be the ones who can explain what the system does, why it does it, what legal obligations apply, and where accountability sits when something goes wrong.

3. As a fractional counsel serving early-stage companies, what’s the one legal priority you always address first?

The first legal priority is regulatory characterization tied to the actual product and operating model.

I do not like to start with a generic checklist or a template document. I start with a more basic question: what is the company actually doing?

That sounds simple, but in emerging technology companies, the answer is often more complicated than the pitch deck suggests. A company may describe itself as a software platform, but the product may involve custody, payments, token issuance, automated decisioning, data sharing, financial recommendations, marketplace activity, cross-border transfers, or third-party developer activity. Each of those facts can change the legal analysis.

So the first step is to build a product-and-risk map. Who are the users? What assets, funds, or data move through the system? What decisions are automated? What role does the company play? Where are users located? What third parties are essential to the product? What happens if the technology fails, a user is harmed, or a regulator asks questions?

Once that map exists, the legal roadmap becomes much more practical. The company can sequence what matters first: entity structure, IP ownership, customer terms, privacy, data rights, vendor contracts, token analysis, AML/sanctions, licensing, fundraising risk, employment/IP assignments, or commercial contracting.

For early-stage companies, sequencing matters. Founders do not need to spend legal budget analyzing every theoretical issue at the same level of depth. They do need to identify the issues that could block launch, affect fundraising, slow enterprise sales, create regulatory exposure, or force a product redesign later.

Characterization is inexpensive early and expensive late. Resolved before launch or a raise, it is a conversation. Discovered afterward, it can become a restructuring.

That is where fractional counsel can be especially useful. The role is not to slow the company down or turn every issue into a legal memo. The role is to help founders convert ambiguity into a decision framework, so legal work becomes part of product strategy rather than a last-minute obstacle.