
What Solo Lawyers Should Actually Look for in Legal AI
Legal AI for solo lawyers isn’t the same as enterprise tools. Here’s what actually matters when choosing a legal AI platform.
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NEWS & BLOGS
Research, analysis, and perspectives from the team building the future of legal work.
From our Head of AI
Writing on reasoning systems, AI infrastructure, and the engineering decisions behind Irys — read by thousands of practitioners.
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Legal AI insights, product updates, and research — delivered to your inbox.
38 results

Legal AI for solo lawyers isn’t the same as enterprise tools. Here’s what actually matters when choosing a legal AI platform.

A primary-source investigation for developers and security researchers who want the real story about what the Data says about Mythos

Legal AI is scaling fast—but not always for small firms. Here’s what BigLaw platforms mean for AI for lawyers.

Every case handoff loses context. Here’s why legal work breaks across transitions and how a legal AI platform fixes it.

AI Market Report March 2026: Google’s price war, America’s shadow-banking energy play, and why the whole AI stack just fractured into four industries — each with different economics.

Most law firm AI tools fail before the work begins. Here’s how legal AI actually handles real case records.

Saleforce TDX, Google Cloud Next and more..

Lawyers work in Word. Starting today, the full Irys sidebar lives directly inside Microsoft Word.

Featuring insights from Andrew Ng, Terence Tao, Yann LeCunn, and many other top researchers on how they use AI for better gains

Legal AI just changed: courts now care how your AI handles data—not what it produces.

Document-level legal AI helps with drafting — but breaks at deal complexity. Why AI for lawyers must move beyond single-document workflows.

Most legal AI tools rely on chat. But legal reasoning depends on continuity — and continuity depends on matter context. Here’s why chat-based tools fail without it.

From Societal Bottleneck to High-Yield Infrastructure

What Apple, GitHub Copilot, and legal AI reveal about how incumbents lose paradigm shifts
![How a Leading Venture Capitalist uses AI Agents [Guest]](/_next/image?url=https%3A%2F%2Fsubstackcdn.com%2Fimage%2Ffetch%2F%24s_!-M8X!%2Cf_auto%2Cq_auto%3Agood%2Cfl_progressive%3Asteep%2Fhttps%253A%252F%252Fsubstack-post-media.s3.amazonaws.com%252Fpublic%252Fimages%252F2c03f2eb-10cb-4fa0-94b3-e79c07f6c9c1_256x256.png&w=3840&q=75)
A skeptic’s field guide to building personal agents that are powerful, practical, and safe

How Liquid AI built the Best Edge AI Model in the World. And what this reveals about the next wave of model design.

Understanding why Reasoning Models are Different from Normal LLMs and How to Prompt them.

A practical look at how debugging workflows, metrics, and automated runbooks are used to investigate slowdowns and failures in large-scale model training.
![Earth Scientist Tells You How Your Land Is Lying to You [Livestreams]](/_next/image?url=%2Fimages%2Fblog-11.png&w=3840&q=75&dpl=dpl_HVEGDNLmG8yKsjb4vx29PDZKQipS)
How AI turns weeks of GIS work into 10 seconds of queries

A clear guide to the new architectures battling the transformer’s memory and inference bottlenecks.

How labs differentiate when models converge, what AI coding tools actually change, and why the backlash against AI became a market force

How Mass AI Surveillance and AI Weapons Systems Can Threaten Our Freedom
![Most Important AI Updates of the week. Feb 16th 2026-Feb 22 2026 [Livestreams]](/_next/image?url=https%3A%2F%2Fsubstackcdn.com%2Fimage%2Ffetch%2F%24s_!qCU8!%2Cw_1456%2Cc_limit%2Cf_auto%2Cq_auto%3Agood%2Cfl_progressive%3Asteep%2Fhttps%253A%252F%252Fsubstack-post-media.s3.amazonaws.com%252Fpublic%252Fimages%252F2352a158-b110-4fd2-95a2-0dd5c06ad763_2208x2208.jpeg&w=3840&q=75)
why AI benchmarks are failing and more

From FLOPs to GPUs to KV Cache — What Every Token Actually Costs

How to teach yourself AI- for technical folk

A deep dive into hierarchy and why present AI systems are missing it.
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Meet Philipp Wehn, the a16z Founder using AI to improve Heavy Industries.
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A candid conversation on tradeoffs, scale, and what actually matters.

How verification costs, retention curves, and stateful systems are reshaping where value accumulates

Databricks, Sakana AI, Radix, and why Investors are rushing to invest in Open Source AI

A deepdive into the Metaverse, Open Source, and how Meta is tackling their biggest challenges.
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What are Kernels and Why writing them better can improve GPU usage by 5x.

Today we are announcing the general availability of Irys Prime — the first version of our platform that delivers what we believe is the correct standard for legal AI: matter-aware, jurisdiction-grounded, and auditable at every step.

Most legal AI tools conflate finding relevant cases with knowing what to do with them. Irys draws a deliberate architectural boundary between the two — and it changes everything about how lawyers can trust AI in practice.

The legal AI market is full of tools that do impressive things in controlled settings and disappointing things in actual practice. Here is why the gap exists, what causes it, and what doing it right actually requires.

Chat interfaces gave us a taste of what AI could do for legal work. But a conversation is not a matter. The next generation of legal AI has to be organized around how lawyers actually work — and that means building around the matter, not the message.

The deployment of AI in legal practice raises profound questions about professional responsibility, client confidentiality, and the nature of legal judgment itself. How Irys approaches these questions — and why getting them right is non-negotiable.

The legal AI market has bifurcated into thin wrappers on general models and raw API access for sophisticated teams. Neither is sufficient. Law needs a purpose-built infrastructure layer — and the firms that understand this earliest will have a durable advantage.