Why Your Lawyer Must Actually Understand Technology (and What TRAIGA Gets Wrong)

AI statutes and model‑specific rules are not abstract exercises for policy wonks — they land on top of running products, data pipelines, and engineering roadmaps. When a law like TRAIGA defines an “AI system” in broadly textual terms, the consequence isn't just academic: it becomes a practical constraint on architecture, product features, vendor contracts, and sales conversations.

The core risk is simple and concrete. Lawyers who treat technology as a black box will draft and negotiate text that is untethered to how systems behave. That produces definitions and obligations that are overbroad, impossible to operationalize, or that force expensive, pointless rework: blanket documentation demands, inapplicable disclosure regimes, and governance theater that slows product velocity without reducing real risk.

This piece is written for founders, product leaders, engineering managers, and in‑house lawyers responsible for building or deploying software and AI features. Label up front: this is an opinion/perspective essay with a clear thesis — in today’s AI‑saturated market, counsel who do not understand technology are a liability. TRAIGA’s overbroad definition of “AI system” is a timely case study showing how technical ignorance in drafting turns into legal and commercial harm.

What follows will be practical, not rhetorical. I’ll (1) show what TRAIGA reveals about the gap between law and tech, (2) explain why that gap is uniquely dangerous for modern software products, (3) describe how tech‑literate lawyers materially change outcomes for startups and product teams, and (4) give you ways to evaluate whether your counsel is up to the task. If you want background on how lawyers operationalize tech‑centered advice, see our practitioner posts on bridging law and code, the lawyer‑in‑the‑loop pattern, and our AI governance playbook, which the later sections assume you’ve at least skimmed.

TRAIGA’s AI Definition: A Case Study in How Law Goes Sideways Without Tech Literacy

TRAIGA — a recent AI‑focused legislative draft — tries to define what counts as an “AI system.” Its operative formulation reads, essentially, that an "AI system" is “any machine‑based system that, for any explicit or implicit objective, infers from the inputs the system receives how to generate outputs, including content, decisions, predictions, or recommendations, that can influence physical or virtual environments.”

Read slowly, that sentence contains four moving parts:

• Machine‑based system — any software/hardware combination running on compute;
• Explicit or implicit objective — goals the system serves, even if not formally documented;
• Infers from inputs to generate outputs — ambiguous “infers” language that can cover both statistical learning and simple conditional logic;
• Can influence environments — virtually any non‑trivial program affects a virtual or physical environment.

To a developer this reads less like a targeted definition and more like a description of ordinary, non‑trivial software. Concretely, the text would plausibly sweep in:

• a rules‑based pricing engine that computes price from inventory and rule sets;
• a spam filter using heuristics or lightweight ML to classify mail;
• a recommendation widget ranking items from click and conversion signals;
• a scoring function that prioritizes customer support tickets or loan applications.

Legislative intent — that is, the sponsor’s stated goal of targeting opaque, high‑risk learning systems — does not cure an overbroad textual hook. Once language is enacted, courts, regulators, customers, and counterparties start from the text. That means agencies and commercial parties can reasonably interpret TRAIGA’s plain wording to capture many systems the drafters never intended to regulate.

This is the predictable result of drafting without technical grounding. When policymakers and generalist counsel treat technology as a black box, they default to inclusive inputs→outputs language because it sounds comprehensive. In contrast, in industries like healthcare or energy, lawyers routinely bake operational nuance into doctrine and contracts.

Imagine a SaaS founder with a simple automated prioritization rule. Under TRAIGA’s wording the feature could be treated as an "AI system" — triggering registration, documentation, DPIA‑style reviews, and audit demands. Engineering is diverted to instrumentation, product experiments stop, customers demand warranties or carve‑outs, and the company incurs compliance costs that don’t meaningfully reduce the real risks. A tech‑literate lawyer would spot that overreach and press for functional thresholds or carve‑outs tied to learning, autonomy, or material risk.

For practical approaches to mapping legal obligations to actual system components, see our AI governance playbook.

How Overbroad AI Definitions Create Real Risk for Startups and Product Teams

When statutory language is broad enough to capture ordinary software, the consequences are practical, immediate, and expensive. An overbroad “AI” hook doesn’t just create theoretical exposure — it imposes real obligations that small teams must staff, implement, and justify to regulators, customers, and auditors.

Regulatory and compliance uncertainty

If your product can be read as an “AI system,” you may suddenly face registration requirements, mandatory impact assessments, formal documentation (model cards or DPIA‑style reports), regular monitoring, and third‑party audit demands. The uncertainty alone causes chilling effects: engineering and product teams pause releases, remove or hide automation features, or divert scarce resources into compliance theater instead of building product.

Contract and negotiation consequences

Statutory definitions travel. Vendors, enterprise customers, and partners routinely copy legislative language into MSAs, DPAs, and procurement terms. An overbroad definition can make your entire stack qualify as an “AI system” under a contract — triggering audit rights, enhanced liability, warranty obligations about datasets and model behaviour, and onerous change‑control regimes. Negotiations slow, and startups often give away protections just to close deals.

Litigation and enforcement exposure

Vague text is a litigation magnet. Plaintiffs’ counsel and enforcement agencies can read broad definitions expansively to claim failures of disclosure, inadequate oversight, or unlawful automated decision‑making. Imagine a customer arguing that a ranking algorithm is an “AI system” under TRAIGA and seeking rescission or damages for supposed failure to register or disclose — the dispute drives discovery, reputational risk, and defence costs irrespective of the underlying technical seriousness.

Operational friction and engineering burden

Legal ambiguity translates into engineering work: added logging, versioning, retraining controls, audit trails, and human‑review workflows. Teams build brittle workarounds (feature flags, strict segmentation) to avoid classification instead of designing responsibly. A tech‑savvy lawyer avoids the worst of this by helping teams segment components, draft narrow contract definitions, and build a defensible mapping of systems to risk tiers — see our AI governance playbook and the lawyer‑in‑the‑loop pattern for practical, implementable approaches.

These are not abstract harms. They are the predictable, avoidable effects of drafting law and contracts without technical literacy — which is why you need counsel who can translate code, architecture, and product behavior into precise legal hooks and operational obligations.

Why Technology Literacy Is No Longer Optional for Lawyers

Other industries already expect domain‑savvy counsel

In mature regulated sectors, domain fluency is a selling point. Entertainment lawyers know distribution windows, chain‑of‑title issues, and how a licensing term translates into monetization schedules; a film financing lawyer will negotiate around theatrical, streaming, and broadcaster windows because those operational distinctions matter to value. Oil & gas counsel understand lease language, royalty mechanics, and field practices that directly shape commercial risk. Healthcare lawyers live in HIPAA, FDA classifications, and clinical workflows — they must know how device software is used in care to advise on liability and compliance.

By contrast, many tech and AI lawyers still claim the law is “tech‑neutral” and treat architecture as a secondary detail. That double standard is untenable: in AI and software, technical distinctions are legal distinctions. If your drafter doesn’t grasp where decisions happen in the stack, contracts and statutes will misfire.

Why AI and software are uniquely sensitive to misunderstanding

Modern AI‑enabled products are modular: data pipelines ingest and transform data, models (or rule engines) produce inferences, orchestration/workflow tools (e.g., n8n) connect services, and front‑end integrations present results. The legal status of a product often hinges on how you draw boundaries between those pieces — which component performs the decision, whether it adapts over time, and where data is stored or shared.

Legal hooks attach to technical concepts: "training" vs "inference" maps to lifecycle obligations; "rules" vs "learning" maps to whether explainability or monitoring is meaningful; deterministic vs probabilistic outputs map to when human review is necessary. TRAIGA’s phrase “infers from the inputs” is a classic example — without technical nuance it can be read to include both simple conditional logic and complex adaptive models.

Hypothetical: a linear scoring model that ranks support tickets is misclassified as a high‑risk ML system. The company then faces disproportionate documentation, human‑review requirements, and audits — costs that don’t align with the model’s actual risk.

Rebutting the common excuses

“Tech changes too fast — lawyers can’t keep up.” The point isn’t to master every library or framework; it’s to internalize core mental models (data flows, model lifecycle, APIs, where state is kept) and to know when to probe deeper with engineers.

“We just need to understand the business, not the code.” In AI/software the business risk is implemented in code. Ignoring architecture leaves lawyers unable to assess what actually affects rights, safety, or economics.

“We’ll just rely on experts.” External experts are essential, but counsel must translate their findings into precise statutory and contractual language. That translation requires baseline technical literacy to evaluate expert conclusions and to draft implementable obligations.

Reframe: technology literacy is table stakes. Lawyers who engage with architecture, data, and models produce practicable law and contracts; those who don’t create avoidable compliance costs, negotiation friction, and regulatory exposure. For practical steps to build this fluency, see our bridging law and code essay and the AI governance playbook, which show how lawyers operationalize technical understanding and embed it into product cycles.

In practice, a tech‑fluent lawyer treats a definition like TRAIGA as an engineering requirement: start by listing the behaviors you truly mean to regulate, then iterate until the definition excludes ordinary, non‑learning automation. For example, rather than a single catch‑all “AI” term, a defensible approach is to require (1) data‑driven adaptation, (2) an explicitly enumerated class of model families or training methods, and (3) clear exclusions for deterministic business logic. That structure makes downstream obligations actionable for engineers and auditable for regulators. When counseling clients facing overbroad laws, counsel can use definitions and scoping clauses to align contract obligations to the client’s real risk profile. Practical clause patterns include: (a) scoping “AI system” to named components or services; (b) excluding systems that do not change parameters after deployment; and (c) tying obligations to observable metrics (e.g., retraining events or drift thresholds). Embedding these points into contracting templates avoids unreasonable interpretations while remaining enforceable in operational settings. For implementation guidance, see the AI governance playbook available at https://blog.promise.legal/ai-governance-playbook, which shows how to translate legal definitions into inventories and controls, and review the case study on adopting AI controls in law firms at https://blog.promise.legal/ai-in-legal-firms for a concrete example of narrowing obligations to fit real systems.

How to Tell If Your Lawyer Actually Understands Technology

Questions founders and GCs should ask

• Can you explain, in your own words, the difference between training and inference and give an example using our product?
• How would you determine whether our product meets TRAIGA’s “infers from inputs” threshold?
• What parts of our architecture would you want to see before drafting risk disclosures or a DPA (e.g., data flows, model endpoints, vendor contracts)?
• Which operational artifacts would you ask for to support a compliance position (model cards, retraining logs, test harnesses, access controls)?
• If a customer demands TRAIGA‑style language in an MSA, what concrete edits would you propose to narrow scope?
• How would you tier risk across our features — what characteristics push a feature into a high‑risk category?
• What controls would you require before we ship (monitoring, rollback plan, human‑in‑the‑loop) and how would they be measured?

Follow‑ups if the answer is hand‑wavy: ask for a specific checklist, a short timeline for review, or an example clause they would include to limit scope. If they can’t name artifacts to review or can’t sketch a one‑page plan, that’s a problem.

Red flags that your counsel is out of their depth

• Relies on vague clichés (“AI is a black box,” “we’re tech‑neutral”) instead of concrete steps.
• Insists that “all AI is the same” and offers one‑size‑fits‑all advice.
• Copy‑pastes statutory or regulatory definitions into contracts without tailoring.
• Refuses to review architecture diagrams, logs, or vendor agreements.
• Only recommends prohibitions (“don’t use that tool”) rather than workable mitigations.

Example: a founder shows an architecture diagram; the lawyer waves it off and issues blanket warnings. Result: unnecessary product changes, stalled negotiations, and wasted engineering cycles.

Positive signals of a tech‑literate legal partner

• They ask for diagrams, sandboxes, and specific artifacts and can accurately describe your system back to you.
• They propose narrow, functional definitions and carve‑outs rather than sweeping labels.
• They map legal obligations to engineering actions (e.g., “require retraining logs if model updates occur more than X times/month”).
• They offer sample contract edits and operational checklists you can implement without stopping product work.

At Promise Legal we operate at this interface: reading code‑adjacent docs, translating technical facts into precise legal hooks, and designing governance that matches how systems actually behave. If you want templates and practical workflows, see our AI governance playbook, or learn more about embedding counsel in product cycles. If you’d like, schedule a short intake to test your exposure under TRAIGA‑style definitions and get concrete edits you can use in negotiations.

Beyond TRAIGA: The Broader Stakes of Law + Tech Misalignment

TRAIGA’s drafting error is illustrative, not unique. When lawyers and policymakers miss technical nuance, the fallout shows up across other legal domains — and the costs compound as companies scale and stitch more automation into their products.

Other domains where ignorance creates real problems

• Data localization & cloud architecture — a law that requires ‘‘data storage in X’’ can be interpreted to mean every service, cache, or analytics pipeline. Re‑architecting multi‑region deployments or refactoring vendor integrations is expensive and often unnecessary if counsel fails to scope which data must be local.
• Security obligations vs threat models — contracts that prescribe generic ‘‘pen testing’’ or ‘‘industry‑standard security’’ without mapping to your architecture (multi‑tenant SaaS vs on‑prem) can demand the wrong controls or leave real gaps unaddressed.
• Overreaction to generative tools — blanket bans on LLMs or “no AI” policies often drive shadow usage and lost productivity. Practical governance (allowlists, prompt templates, logging) reduces risk far more effectively than prohibition.

How misalignments compound

Each mismatch multiplies downstream: vague regulator language becomes copied into customer contracts, which become procurement clauses with audit rights, which demand engineering changes — and the company accrues both technical debt and legal obligations. By the time you raise funding, onboard enterprise customers, or undergo M&A diligence, inconsistent interpretations across contracts and regions are a major liability.

Recursive — formerly lawyer‑in‑the‑loop — as a durable pattern

The durable solution is process, not panic: embed legal review into product design and release cycles so legal requirements are translated into implementable engineering checkpoints. A Recursive approach conducts architecture walkthroughs, maps components to regulatory triggers, and helps design tiered controls (inventory → risk tiering → approval gates → monitoring). For practical frameworks and templates, see our AI governance playbook (AI governance playbook) and an explainer on the lawyer‑in‑the‑loop pattern (What is Lawyer in the Loop?).

Not a one‑off problem

TRAIGA is a warning sign. As AI and software regulation proliferates, the gap between tech‑literate and tech‑naive lawyering will determine which companies can scale without constant rework. Start with an inventory, embed counsel in design conversations, and standardize mapping artifacts so legal obligations follow architecture — not the other way around.

So What? / Where This Leaves Us

In an AI‑saturated economy, a lawyer who does not understand technology is not neutral — they are a liability. TRAIGA’s overbroad “AI system” language is the emblematic warning: drafting that treats technology as a black box will routinely capture ordinary software, impose unimplementable obligations, and force expensive operational changes that don’t reduce real risk.

That outcome is avoidable. Tech‑literate counsel translate architecture into legally meaningful boundaries, draft functional thresholds instead of sweeping labels, and design governance that engineers can actually implement. The difference is practical: fewer slowdowns, cheaper compliance, clearer contract positions, and defensible narratives for regulators and customers.

What you should do differently

Founders & product leaders: don’t accept “issue‑spotting” as an excuse. Audit your counsel against the diagnostic questions and red flags in this article, insist they talk to your engineers, and involve legal early in product design and vendor selection.

In‑house counsel: invest in technical basics (data flows, model lifecycle, integration points) and push back on overbroad internal policies and copied statutory language. Use legal review as a design partner, not a post‑hoc blocker.

Concrete next steps

• Run a quick inventory: list systems that might be swept in by TRAIGA‑style definitions and flag ambiguous components.
• Ask your counsel the diagnostic questions from this piece; evaluate answers against the red flags.
• Document architecture and data flows for one critical system — use this as the template for legal/engineering mapping.
• Adopt a risk‑tiering approach and apply narrower, behavior‑based definitions in contracts and policies.
• Use Promise Legal’s practical resources — start with our AI governance playbook and the lawyer‑in‑the‑loop pattern — to operationalize controls.
• Schedule a short workshop to stress‑test contract language and your model inventory before a customer or regulator forces the conversation.

TRAIGA is not an isolated drafting mistake — it’s a signal of a broader mismatch between how law is written and how systems behave. If you want to stop legal text from dictating architecture, start with better questions, better artifacts, and counsel who can sit with your engineers. When you’re ready, Promise Legal will review your definitions, contracts, and governance to surface and fix the exact issues TRAIGA exposes.