Thomas Martin, CEO and Founder of LawDroid and an adjunct professor at Suffolk University Law School, offered a clear reframe of the profession’s anxiety: law is confusing tasks with purpose. Drafting and first-pass review may be automated, but the lawyer’s value endures, solving client problems and spotting new ones early, a point he likened to Nvidia CEO Jensen Huang’s view that tools change, but outcomes matter.

That shift matters for junior training. If juniors are trained only as “task-doers”, they’ll feel replaceable. If they’re trained as “problem-solvers”, AI becomes a lever rather than a threat.

What’s Changing for Junior Lawyers?

They challenge the optimistic view that AI “accelerates apprenticeship” by pointing out the old reality: juniors historically learned through high-volume, sometimes menial work that built judgement often by reading everything. Martin agreed AI can make juniors “one step removed” from source material when it summarises, but argued the trade is worth it if used correctly: AI should triage and flag, while humans deep-dive where it matters.

Instead of spending hours on inconsequential documents, juniors can spend more time on the small set of issues that identify risk, value, and negotiation outcomes provided firms teach them the key skills of the future.

New Apprenticeship Pathways

Martin offered concrete examples of emerging roles that can sit naturally with early-career development:

• Knowledge Operations Curator: maintaining the firm’s “internal source of truth” for AI updating clause libraries, harmonising templates with rules, flagging suspect precedents, and owning data quality.
• “Vibe coding” / workflow prototyping: translating legal workflows into lightweight prototypes or agentic processes. Juniors can become disproportionately valuable quickly by building practical tooling while also learning the substance and the real workflow constraints.

His point: these roles aren’t a detour from legal training; they can be a faster route into meaningful work because juniors must understand context to build anything useful.

Law Schools and Professional Training aren’t Moving Fast Enough

Martin was blunt: most legal education still isn’t leaning into AI at the pace the market is moving. His classroom experience suggests student familiarity with AI is accelerating year-on-year, and institutions risk becoming detached from real practice if they treat AI as peripheral.

The group also discussed how quickly mainstream platforms are pushing into “legal-shaped” workflows (Martin referenced new agent “skills” and legal workflows being shipped by general AI providers). That pace increases the pressure on education providers and firms to stop treating AI literacy as optional.

The Money Question: Who Pays for Training?

Ron Given argued that firms can no longer assume clients will bankroll junior learning through billable hours; firms may need to invest more directly to develop associates quickly.

Martin pushed back with a different lens: if firms keep measuring revenue using the old ruler, they’ll conclude training becomes unaffordable. But AI may expand the pie by enabling deeper integration with client operations e.g., “always-on” monitoring and early warning systems that prevent issues and create new service lines.

Both views converged on a practical truth: firms will need to behave more deliberately like businesses investing, designing pathways, and proving value rather than relying on tradition.

The Premium on Human Skills (and “Human-sounding” work)

Rachel Guan and Judith Raphaely highlighted the skills that won’t commoditise neatly: understanding client objectives, anticipating risk, relationship-building, and trust. Judith added a sharp operational observation: clients can often tell when communications are AI-generated, particularly in emails and short advice notes areas where authenticity and judgement are the product.

Martin agreed the “freed up for strategy” line is often vague; what actually matters is concrete human value: trust, collaboration, authority, confidence, and insight, applied to real client outcomes.

Essential Takeaways for Firms and Junior Lawyers

• Train juniors to validate AI outputs, not just consume them: use AI for triage; require source-checking for anything consequential.
• Redesign apprenticeship around ownership: give juniors responsibility for curated knowledge, workflow playbooks, and repeatable processes (with supervision).
• Treat AI fluency as baseline: assume juniors are already using AI; training should focus on professional-grade use (risk, ethics, verification, confidentiality, and client communication).
• Invest in client understanding as a core skill: outcome-focused practice and proactive issue-spotting become the differentiator as tasks commoditise.
• Build defensibility beyond software: education + service + change management is where lasting value sits, not tooling alone.