Two questions haunting lawyers about AI — and why the industry still moves slowly

I walked into a packed legal-conference ballroom expecting a tech pep talk. Instead I left wondering the same thing the Business Insider reporter did after 17 hours of panels: how many lawyers are actually using the tools? That core question — how many lawyers are actually using the tools? — sits at the center of billions of dollars of investment, a handful of discipline-worthy courtroom errors, and a simmering debate about the future of legal work.

The mood in the room was equal parts excitement and anxiety. Vendors promised speed and margin; partners worried about billing models; regulators and bar leaders warned about responsibility and hallucinations. Those conversations reduced to two persistent questions that every panelist, judge, and GC seemed to be circling back to.

The first question: Is the AI good enough — and safe enough — to use on client matters?

This is about accuracy, explainability, and risk. Lawyers aren’t just writing marketing copy — they’re giving advice that can cost clients millions or expose them to sanctions. So a model that hallucinates a case citation or invents a legal doctrine isn’t a novelty; it’s malpractice risk.

Recent reporting shows this tension plainly: firms have faced real sanctions when attorneys relied on generative models that produced fake cases, and vendors are racing to add hallucination checks and provenance features. That high-stakes context means many lawyers treat AI like an unclassified chemical: promising in the lab, suspect in the courtroom. (archive.ph)

But accuracy isn’t the only technical worry. Lawyers also ask whether tools reliably surface the whole legal universe they need — not just the most convenient answer — and whether outputs can be audited for conflicts, privilege, and source provenance. Firms longing for “copilot” productivity also need guardrails that turn AI from a black box into a supervised assistant. Studies testing legal copilots suggest progress but underscore important limits. (fortune.com)

The second question: Who pays when AI makes lawyers faster?

This is the business question that keeps partners awake. The legal economy is structured around the billable hour, and AI changes that math. If a task that used to take an associate 10 hours now takes 90 minutes with AI plus 30 minutes of review, how do firms price their services? Do they lower rates, keep rates and increase margin, or move toward value-based fees?

The answer matters because it determines incentives for adoption. If partners believe AI will hollow out revenue, they’ll stall investment and restrict use. If clients demand lower-priced, faster results, firms will be forced to pivot — but that pivot still faces cultural and billing inertia. The industry’s confusion shows in surveys: personal experimentation with generative tools often outpaces firm-level policies and billing strategies. (americanbar.org)

Transitioning from those two questions brings us to the real adoption dilemma: enthusiasm vs. institutional readiness.

So how many lawyers are actually using the tools?

Short answer: it depends which survey you read and which “use” you count. Personal, informal use of ChatGPT or other assistants is widespread; firm-sanctioned, regular use for client work is far less uniform.

• Large, tech-forward firms and in-house legal teams report higher adoption rates and dedicated copilots, while many solos and small firms lag. (americanbar.org)
• Some surveys show a modest minority using generative AI daily (roughly 20–30% in certain snapshots), while others report broader “some use” figures (30–60% depending on methodology). (news.bloomberglaw.com)

Put another way: a lot of lawyers have tried the tools, but fewer have woven them into audited, firm-wide workflows that handle privilege, provenance, and billing. That gap — between curiosity and trusted operational use — is where most of the money and friction live.

What’s holding the profession back?

Several practical and cultural brakes show up repeatedly at conferences.

• Ethical and regulatory uncertainty. Bars and courts still debate disclosure, competence, and supervision rules for AI-assisted work. That uncertainty chills firm-wide rollouts. (americanbar.org)
• Risk of hallucinations and errors. High-profile sanctions stories make partners risk-averse. The lesson: AI needs human checks, and those checks cost time. (archive.ph)
• Billing and business-model friction. The billable-hour legacy makes firms ask whether to profit from AI efficiency or pass savings to clients — and that debate slows adoption. (lawyerist.com)
• Data hygiene and integration. Many firms’ document ecosystems are messy; effective AI needs clean, well-governed data, which requires investment. (sbo.consulting)

These are solvable problems — but they require governance, training, and leadership decisions that many firms haven’t fully made.

Where investors and vendors fit in

Venture capital and vendors see a huge runway: legal AI deals and product launches have attracted billions. Investors are betting that once the ethical and billing knots are untied, adoption will accelerate and generate substantial efficiency gains across litigation, corporate work, and compliance. That’s why conferences feel equal parts product demo and sales pitch. (allaboutai.com)

But vendor enthusiasm must pair with sober legal risk management. The winning products will be those that embed verifiable sources, offer audit trails, and mesh with law firms’ billing and records systems — not just flashy drafting demos.

My take

AI in law is already real, but it’s not yet ubiquitous in the professional, accountable sense that matters for clients and courts. The two questions haunting lawyers — “Is it safe?” and “Who benefits financially?” — are practical, not philosophical. Answer those, and the rest follows.

We should expect uneven adoption for a few more years: rapid uptake among in-house teams and large firms that can invest in governance; slower movement among smaller shops where the billing model and compliance risk cut differently. The real measure of success won’t be how many firms claim to “use AI,” but how many can show audited, client-safe workflows that improve outcomes without inviting sanctions.

Final thoughts

When billions of dollars are riding on lawyers moving faster with AI, the overriding challenge isn’t the models themselves — it’s the profession’s risk calculus and business incentives. Conferences are useful because they surface those debates, but the practical work happens back at the firm: cleaning data, writing policies, training people, and rethinking pricing.

If the industry solves the two questions — safety and billing alignment — adoption will accelerate. Until then, expect a lot of pilots, a few headline failures, and steady, incremental progress.

Sources

• Business Insider — “How Can Lawyers Stop AI's Hallucinations? More AI, of Course.”
  https://archive.ph/2025.12.12-155351/https://www.businessinsider.com/lawyers-legal-tech-companies-fight-ai-chatgpt-hallucinations-2025-12
• Bloomberg Law — “Legal AI Revolution Moves Ahead at Measured Pace, Survey Says.”
  https://news.bloomberglaw.com/artificial-intelligence/legal-ai-revolution-moves-ahead-at-measured-pace-survey-says
• American Bar Association — “2024 Artificial Intelligence TechReport.”
  https://www.americanbar.org/groups/law_practice/resources/tech-report/2024/2024-artificial-intelligence-techreport/
• Stanford / HAI reporting summarized in Fortune — “What a Stanford study of AI copilots for lawyers says about AI for everyone else.”
  https://fortune.com/2024/06/04/stanford-hai-legal-ai-copilot-study-rag-llms-future-of-ai/
• Rev (Legal Tech Survey summary) — “The 2025 Legal Tech Survey.”
  https://www.rev.com/blog/legal-tech-survey

Listening to a Planet: When Technology Lets the Earth Speak

The first time you slow down to listen to a forest or stand beside the ocean at night, you get a sense that the world is making music you didn't write. New technology enables us to perceive sounds beyond human hearing range, and that simple fact is changing how we think about our place on the planet. These tools—underwater hydrophones, infrasound arrays, dense acoustic sensors and machine listening—are widening our ears and nudging us toward a humbler, more relational way of living on Earth.

For centuries humans treated sound as something primarily for human use: conversation, music, warning cries. But the planet has been talking long before us—seismic groans, whale songs, ice creaks, insect choruses—most of it outside our audible range. Today’s listening technologies translate those vibrations into forms we can perceive and analyze. The effect is partly scientific (new data about ecosystems) and partly existential (a different story about who “speaks” on Earth).

Why it matters: a new sensory perspective

When we translate low-frequency infrasound, ultrasonic clicks, or the spectral richness of an underwater soundscape into audible forms, we gain a vantage point not only for research but for empathy. Scientists use these signals to track whale migrations, detect earthquakes, monitor volcanic unrest, and even infer the health of coral reefs and forests. But beyond practical uses, these translations let people experience how nonhuman life and large-scale Earth processes occupy time and space.

That matters because our policy debates and moral imaginations are shaped by perception. If decision-makers and the public can hear the slow rumble of glaciers or the layered chorus of a healthy reef, those phenomena stop being abstract data points and become visceral realities. Sound becomes a bridge between scientific knowledge and public feeling.

New technology enables us to perceive sounds beyond human hearing range

• Hydrophones brought whale song and ocean noise into public consciousness decades ago, but modern networks and better microphones make continuous, high-fidelity listening possible.
• Infrasound arrays and seismic-acoustic coupling reveal events too low for our ears but crucial for understanding storms, volcanic eruptions, and human-made disturbances.
• Machine listening and AI let researchers parse hours of recordings, classify species by call, and detect subtle changes in the acoustic ecology that would be invisible otherwise.

Together, these technologies form a new kind of sensory infrastructure: distributed, data-rich, and persistent. They don’t just capture rare moments; they map long-term patterns.

Where this is already showing value

• Conservation: Passive acoustic monitoring identifies species presence and behavior without intrusive observation. For whales and other cryptic animals, sound is often the best real-time indicator.
• Disaster detection: Infrasound and low-frequency monitoring can provide early signals for volcanic explosions, glacier calving, or landslides—events that move faster than visual monitoring networks sometimes can.
• Urban planning and quiet protection: Acoustic maps reveal the loss of quiet spaces and the invasion of human-made noise into previously silent habitats. That helps prioritize conservation and design quieter infrastructure.
• Cultural and artistic engagement: Sound artists and educators use translated Earth sounds to build empathy and curiosity—turning scientific signals into narratives that people can feel.

These use cases show both pragmatic benefits and cultural shifts: listening becomes a policy tool, a research method, and an aesthetic practice.

Challenges and caveats

• Interpretation is hard. A recorded sound doesn’t automatically tell you intent or ecological significance. Contextual data (location, time, complementary sensors) remain essential.
• Bias and access: Most monitoring happens where researchers have funding. That risks concentrating "listening power" on certain regions while leaving others under-monitored.
• Privacy and ethics: Acoustic networks in human-dominated landscapes raise surveillance concerns. Distinguishing human voices from other sounds and ensuring appropriate use of recordings must be part of deployment plans.
• Data overload: Continuous listening generates huge datasets. Machine learning helps, but training models requires careful curation and transparency.

A responsible listening practice pairs technological capability with ethical frameworks and equitable deployment.

The cultural ripple: what listening does to us

Listening to translated Earth sounds has an unusual effect: it slows us. Hearing a glacier calve in slow, low frequencies or the layered rush of a rainforest at dawn changes temporal scale—sudden human events sit differently against geologic and ecological durations. That re-scaling is political: it can shift debates from short-term convenience to long-term stewardship.

It also challenges human exceptionalism. When seas, wind, and soil are legible as “voices,” policy conversations must reckon with a more-than-human chorus. That doesn’t give animals or landscapes literal legal speaking rights by itself, but it makes it harder to treat ecosystems as silent resources.

Common questions, briefly

• Will this replace other ecological methods? No. Acoustic data complements visual surveys, satellite imagery, and community knowledge. Each method offers distinct strengths.
• Are these sounds reliable evidence? They’re robust signals when combined with careful analysis and corroborative data. Sound is a sensor, not a verdict.
• Who owns acoustic data? This is evolving. Open-data approaches promise broad scientific gains, but stewardship, consent (for recordings near communities), and clear governance are essential.

My take

Listening is more than a technical upgrade; it is a change in attention. New technology enables us to perceive sounds beyond human hearing range, and with that perception comes a new responsibility. The planet’s signals can guide safer infrastructure, better conservation, and richer cultural experiences—but only if we pair technical ingenuity with ethical governance and a willingness to let nonhuman voices reshape our priorities.

If we move from extraction to attention—if policy-makers, scientists, artists, and communities adopt listening as a shared practice—we may find more humane and sustainable ways to inhabit this noisy, speaking planet.

Sources

• "Decoding The Hidden World Of Nonhuman Sound, With New Biotech" — Science Friday.
  https://www.sciencefriday.com/articles/sounds-life-excerpt/

• "Tuning in to the world of nonhuman sound: Q&A with author Karen Bakker" — Mongabay.
  https://news.mongabay.com/2022/10/tuning-in-to-the-world-of-nonhuman-sound-qa-with-author-karen-bakker/

• "Listening To The Creatures Of The World" — Noema Magazine.
  https://www.noemamag.com/a-parliament-of-earthlings/