The New Legal Code: Hiring in the Age of Artificial Intelligence
AI is now infrastructure. Copilots live inside office suites, enterprise assistants sit in every tool stack, and compute is the new oil. NVIDIA's rise mirrors the arms race for capacity, and the stock market treats AI spend like oxygen.
The real question for in-house legal: does hiring follow the hype? To find out, we pulled insights from Garrett Rosen, senior vice president of legal recruiting at Larson Maddox, who spends his days placing product counsel, privacy, IP, and compliance talent across the biggest tech hubs.
The market: steady, busy, and selective
The pendulum swung hard. After the 2020-2022 run-up came a cooldown and layoffs. Over the last year, demand has stabilized and improved, but it's not a free-for-all.
There's more candidate activity than there are great roles. Companies are picky. Job security matters again, and hiring managers scrutinize fit, impact, and tenure.
What companies are actually hiring for
- AI product counsel: the intersection of product development, privacy, consumer protection, and regulatory risk.
- AI governance and policy: often "privacy-plus" roles that bolt AI oversight onto existing privacy/commercial remits.
- Explicit "AI" titles: more common in larger, mature organizations with material regulatory exposure and complex product lines.
Earlier-stage teams often fold AI accountability into existing product, privacy, or commercial roles until scale demands a dedicated function.
Upskill vs. proven experience
Both paths exist, but the bias has shifted. Many employers now want candidates who have already partnered with product and engineering on AI-related work and can move fast without handholding.
Trying to pivot in? Make the translation obvious. Show the projects you've touched, the risks you scoped, and the frameworks you implemented. Curiosity is not enough-show receipts.
The profile that gets shortlisted
- Core training: reputable firm background; experience in privacy, product counseling, consumer protection, or regulatory work.
- Cross-functional depth: tight working rhythm with product, engineering, data science, marketing, and trust & safety.
- Signals of commitment: privacy certifications (e.g., CIPP), and structured AI coursework to formalize knowledge.
If certification helps your story, look at programs like the CIPP from IAPP here. For broader learning paths spanning AI, privacy, and product, explore curated tracks here.
What "technical fluency" actually looks like
You don't need to write code. You do need to speak the language well enough to ask smart questions and spot risk early.
- Data flows: what data is collected, how it moves, retention, access, and vendor touchpoints.
- Models: high-level training and deployment basics, fine-tuning vs. RAG, evaluation, and monitoring.
- User journey: consent, disclosures, opt-outs, fairness and safety guardrails, and redress.
- Narratives with outcomes: "Here's the product, the AI use case, the risks we saw, and the guardrails we shipped-and how that affected the business."
Job-hopping is out of style
Movement has cooled. Employers are wary of frequent jumps, and candidates are more cautious about leaving stable roles. Interest in AI-heavy positions is high, so the bar rises-especially for fully loaded comp and scope.
Remote work widened the field-and the competition
Remote and hybrid options let companies source talent nationally. They also pit you against candidates from every major tech hub.
- Hub expectations: many teams still want a presence in New York, San Francisco, Seattle, or similar for part of the week.
- Fully remote: exists, but demand is intense for top-tier AI/privacy roles.
- Your call: relocate for access, or compete hard for the best remote seats.
Action plan for law students and early-career lawyers
- Lock the fundamentals: writing, analysis, judgment, and client service still carry you.
- Get proximity: staff onto privacy, data, product counseling, trust & safety, or AI-adjacent matters.
- Build proof: take relevant courses, pursue a privacy or AI-related certification, and document your learning.
- Show thought leadership: publish short memos, internal briefs, or talks that map risks to product decisions.
- Volunteer internally: help draft AI usage guidelines, evaluation checklists, or vendor due diligence templates.
What's next: growth, then consolidation
Expect continued hiring, with bumps along the way as regulation tightens and business models mature. Before any real "bubble pop," anticipate consolidation-bigger players acquiring tech and teams.
For legal, that means steady demand in AI, privacy, consumer protection, safety, and regulatory strategy. 2026-2027 should be active across M&A and enforcement, which usually sustains need for strong in-house counsel who can partner with product and ship guardrails without slowing the business.
Bottom line
AI work in legal isn't a fad. It's becoming part of how companies build, launch, and govern products.
If you can bridge legal risk and product velocity-and tell precise stories about doing it-you'll keep getting calls.
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