Akshat Singhal @asinghal_7

Introducing Codex Redline! Upload a contract and your playbook, and get a fully marked-up Word doc back in seconds. Until now, you open the document and your playbook side by side, then go clause by clause, comparing and marking up edits by hand. It is slow, and it is easy to miss one. That is what Codex Redline fixes. It does the comparison and the markup for you, as real Track Changes in a Word document. Here is how it works: 1. Upload the contract and your rules file into Codex 2. Ask it to review the contract against the rules and redline it 3. It flags the non-compliant clauses and applies tracked edits 4. Download a .docx with every change ready to accept or reject in Word What I really like about this is that for every change, you see the original text, the new text, and the exact rule that triggered it. So nothing is a black box. In legal, a redline you cannot explain is a redline you cannot defend. This one shows its reasoning on every line, which is what makes it something a lawyer can trust. If you run an enterprise legal team and want to see how Codex Redline fits your workflow, happy to show you.

Legal AI is improving fastest at the hardest parts of the job. That is the real story in this chart. There is a LegalBench chart comparing 2024 models to 2026 ones on legal reasoning tasks. Many models now score 80 to 100% on work where they used to be in the 60s and 70s. And the biggest gains are on the hard, judgment-heavy tasks, the kind of reasoning that used to need a senior lawyer. This is why teams that were only piloting a year ago are now deploying for real. The accuracy finally caught up to the stakes. That said, the score you can rely on is always per task, not an average. Knowing which tasks you can lean on fully, and which still want a human check, is how you turn this leap into safe, real adoption. If your team is putting legal AI into real workflows, I would like to hear where it is landing for you.

The higher you go up the AI stack, the more capable it gets, and the harder it gets to trust in legal. This "layers of AI" chart runs from Classical AI at the bottom up to Agentic AI at the top. A quick tour of what each layer is for: 1. Classical AI: hard-coded rules and logic. Predictable and easy to audit. 2. Machine Learning: learns patterns from data instead of following fixed rules. 3. Neural Networks and Deep Learning: handle messy inputs like language and images. 4. Generative AI: creates new text, drafts, and summaries. 5. Agentic AI: plans, uses tools, and acts on its own with less human input. Each step up adds power and adds risk. An agent that decides and acts for itself is thrilling in most fields and nerve-wracking in legal, where someone has to answer for every action it takes. The model layers are becoming a commodity. What actually decides whether AI works for a legal team is your own data, your workflow, and your verification, and that matters more than any layer on the chart. A legal team needs a contract reviewed or a deadline caught, done safely. The right layer is whatever solves the problem without outrunning your ability to trust it. If you are working through where AI fits in your legal work, I would like to hear how you are approaching it.

The best hires I have made looked average on paper. What made them great is invisible on a resume. Brian Armstrong said something similar. Some of his best hires would not have passed a resume screen, and they shared the same qualities every time: high agency, smart, mission-aligned, got things done. I have seen the exact same thing. By agency, I mean the instinct to act without being told to. But it is hard to find, because the agency barely shows up even in a polished interview. So how do you find it? A resume lists the work someone was assigned. Agency shows up in the rest, like a tool they built because it was annoying not to, or a problem they fixed that was never their job. That is why I stopped asking candidates about their responsibilities and started asking what they did that no one told them to do. The answer tells you more than any credential. When AI can produce polished output for anyone, the rare person is the one who knows what is worth doing at all. That is agency, and it is the one thing the tool will never hand you. That is exactly who we hire at Legistify. We have a few roles open right now, and if this sounds like how you work, come build with us. Apply to open roles at Legistify: legistify.com/careers

An Oxford paper explains why AI is great at settled law and not good at the argument nobody has made. It is called "Theory Is All You Need," and the core idea is that AI works by prediction from existing data, while human thinking works from theory and causal reasoning. The researchers call it the intervention gap. Humans do not just process what already exists. We act in the world, test ideas, and generate new data. AI can only work from what is already there. Legal work splits along exactly that line. Settled law is strong ground for AI. Standard clauses, routine research, prior rulings: it has seen thousands of examples. The hardest legal work is the opposite. A novel argument. A first-impression case. A deal structure nobody has tried. There is nothing to train on, because the whole point is that no one has done it before. So AI can summarize every precedent and still not originate one. Making that new move is the lawyer's job, and it was always the most valuable part of the work. That is why I have been saying AI will empower lawyers and massively improve their efficiency. If you are thinking about where AI fits in legal work and where it does not, I would like to hear your take.

We just shipped Monitor Agent, a standing research assistant that watches the topics your team cannot afford to miss. Now, it is the most powerful feature of Legistify. You tell it what to track, and it comes to you the moment something changes, so you are not running the same search over and over. The developments that hurt you land unpredictably. A vendor added to a sanctions list. A new ruling. A filing deadline buried in a docket. Miss one, and the cost is real. Yet most teams still track this manually, checking sites and newsletters with gaps in between. Here is how it works: 1. Describe what to track in plain language, like a sanctions list or new RBI circulars that affect NBFCs 2. Pick how you want it, a live stream of each change or a periodic snapshot 3. Set the cadence: hourly, daily, or weekly Personally, what I really love about this feature is what each update carries. A one-line summary, why it matters, a confidence level, and the sources behind it, so your team can trust the alert and check it in seconds. Any AI tool can share alerts with you, but the real work is cutting through the noise to what matters, with the reasoning and a reference to the original source. That is why it is highly trustworthy and saves time. If you run an enterprise legal team and want to see how Monitor Agent fits your workflow, happy to show you.

If AI does the work that used to train junior lawyers, where do the senior ones come from? The old law firm pyramid ran on juniors doing high-volume work at billable rates. Document review, first-pass research, diligence. As per a Thomson Reuters report, the person reading a 5,000-page legal doc is usually the least experienced lawyer in the organization. So why trust them over a machine? It is hard to argue. Diligence that once took 15 to 20 hours is already down to about 2 hours. Also, reading a thousand contracts was how a young lawyer learned which clause matters and which one is noise. Take it away, and you hit a chicken-and-egg problem, as one legal exec put it. How does a junior learn to judge AI's output when they skipped the work that builds judgment in the first place? So the junior lawyer does not disappear. The path that turned them into a senior one does. This is something we think about a lot at Legistify. The best legal AI shows its work, the clause, the source, and the reasoning behind every answer, so a junior can actually learn from it while they work. If you are wrestling with this on your team, I would like to hear how you are approaching it.

This a16z chart shows cyber risk going parabolic. What it does not show is why that is so dangerous now. The chart tracks critical and high-severity vulnerabilities, and the line goes vertical this year. The more interesting insight is that the time from a flaw being disclosed to a cyber attack hitting it has collapsed to about a day, and it keeps shrinking. Now put that next to how enterprises actually buy security. Evaluation, security review, budget approval. That runs a quarter, easily. So the cyber attack lands in a day, and the defence you bought to stop it shows up a quarter later. When things move this fast, waiting to evaluate a tool next quarter is the exposure itself. The response has to live in the systems you already run, with controls and audit trails ready before the incident rather than ordered after it. This is the thinking we build into Legistify, keeping compliance and audit trails ready inside the workflow rather than bolted on later. If your legal or compliance team is working through this, happy to share what we have learned.

Everyone treats legal AI adoption as a tech problem. Being in this industry for 10+ years, I can tell you it is an economic problem. The technology mostly works now, so when adoption stalls, the reason is rarely the model. It is that AI pushes on the two things a firm is built on. 1. Billable hours The firm sells time, and AI saves time. Every hour it removes is an hour that used to be revenue, so the tool that should help quietly threatens the business model underneath it. 2. Liability A partner is personally accountable for the work. Handing that judgment to a machine is not a step most will take, no matter how good the output looks. And a better model does not ease this. It only sharpens the accountability question, forcing it into the open faster. The firms that break through do one thing differently. They stop asking AI to do the same work with fewer hours and start using the freed capacity to take on more, serve clients deeper, and move upmarket. A big part of what we do at Legistify is making that liability question easier to answer, with grounded, traceable output a partner can actually stand behind. If your team is working through this, happy to share what we have learned.

We are hiring for tech roles and this chart aligns with how we hire at Legistify. We look for people who can figure it out. An a16z chart shows tech job postings asking for more years of experience and fewer specific skills since early 2025. What companies are really after is engineers who can understand and design tech architecture. With how fast the AI space is developing, the tools and the approach for designing and scaling applications keep changing. So we hire someone who can walk onto shifting ground and work it out. That is what we screen for on the tech team. People who stay steady on unfamiliar ground and adapt fast. We have a couple of tech roles open right now. Apply now, the link is in the first comment. Our open tech roles at Legistify: legistify.com/careers

A McKinsey survey just showed which skills are rising and falling. It is like a map of what AI took and what it left behind. They asked about 1,300 HR leaders across 10 countries which skills will matter most. Skills like software development, data analytics and AI, and digital learning are falling. Meanwhile, skills like problem-solving, digital literacy, reasoning, and creativity rose. I see this every week when I take hiring interviews. Candidates who can generate polished output are everywhere now. The one who can reason through a problem and tell me why the answer holds is rare, and that is exactly who is rising in value. If you are hiring or building a team right now, I would like to hear what you are screening for.

Claude holds all 6 top spots on the AI leaderboard. Design your architecture so that you can switch models anytime. But if you run legal or compliance inside a large company, you read it differently than a consumer would. Six of six from one provider means the best models, and everything that depends on them, sit with one company. Pricing, access, policy, and uptime all live there too. This dependency on one company can get tricky, and a little dangerous too. That is why you want the model to be a swappable part. Build it so that when the lead changes hands, you flip a switch and get the upgrade, with nothing to rebuild. We learned this early on selling into regulated enterprises. Never let one vendor become a single point of failure, no matter how good they are right now. If you are setting up AI inside a regulated team and thinking through vendor risk, I would like to hear how you are approaching it.

AI made a weak performer much harder to spot. Actually, AI is an amplifier. We are shipping features in days that used to take weeks. But sometimes, the same speed makes long & messy code much harder to review. There is an old idea called Brandolini's law. Cleaning up bad work takes way more effort than making it. AI made the making almost free, but the cleaning up is just as slow as ever. Look at what that does to a team. 2 years ago, a weak developer or lawyer was easy to catch. The work was thin, or it plainly did not hold up. Now, that same person can generate a polished, confident draft in minutes. It looks fine on a first read, then falls apart the moment you dig in. You can no longer judge the work by how it looks on the surface. AI multiplies output for everyone, strong and weak alike. What it does not multiply is the judgment that separates good work from work that only looks good. If you are leading a team and feeling this, I would like to hear how you are handling it.

We’re happy to partner with FirstCry! 🤝 With 56 users, multiple POCs, approval workflows, DSC signing, and 50 templates, FirstCry is using Legistify to make contract management more structured and efficient. Thank you to the FirstCry team for your trust! #FirstCry #LegalTech

Our contract AI now shows its work, every value traced to the clause and page it came from. You know how it goes if you review contracts. AI reads an executed document, flags where the stored details differ from the signed version, and suggests updates. Useful, but you are still left asking how sure the AI is, and where it actually found that value. That is the part we just fixed. Verification now shows the evidence behind every result. Here is how it works: 1. Open an executed contract and run Verify Agent 2. See the Current value and the Found value side by side 3. Each detected field shows a confidence score 4. The source clause points to the exact section and page, with the supporting text 5. Accept a single change, or Accept All where it makes sense Traceability is the part I care about most. In legal, a value you cannot trace is a value you cannot trust. Now every suggestion comes with how confident the AI agent is and the clause it came from, so your team validates in seconds instead of digging through the document. And if Auto Verify runs it overnight, the same confidence score and source stay attached to the result.

Legal AI adoption grew 108x this year. Since February, legal has grown its use of AI agents 108x, ahead of sales, recruiting, even engineering. Look at this chart shared by a16z, and that purple line is showing exponential growth. Legal started from almost nothing in February. Teams are adopting it to improve their efficiency. But the real win is turning it into work that genuinely gets better and stays better, and legal is clearly ready for that. If you are on a legal team putting AI to work, I would love to hear how it is going.

Gartner says 80% of enterprise software will be multimodal by 2030. Legal work was multimodal all along. That is a big jump, from under 10% of software in 2024 to 80% by 2030. Multimodal means one model that works across text, images, tables, audio, and video, rather than text alone. For legal, it is catching up to how the work always happens. A single matter is a scanned contract, an email chain, a court order as a PDF, a spreadsheet of dates, sometimes a recording. Multimodal by itself does not get you there. Gartner's own analysts tie the value to domain-specific models. A model that reads a stamped court order, pulls the hearing date, and links it to the right case is one that earns its place on a legal team. That last part is the real work in legal AI. Most legal documents are messy, full of scans, stamps, tables, and handwriting, and teaching a model to read them the way a lawyer does is where the value sits. If your team is working on this, I would like to hear how you are approaching it.

Contract signing in Legistify just got a lot less manual on both ends. You know how it goes if you run contracts. You send one out, then chase the signer for their title and company, and pause to work out who in which department is even supposed to sign. Small steps, but they add up. That is the part we just fixed. We shipped two upgrades to contract signing. Here is how they work: 1. Add Name, Email, Company, and Title fields right onto the PDF in Virtual Sign 2. The signer fills them in before signing, and the details get stamped onto the final document 3. Set default signatories by department, so a Marketing contract routes to the right person on its own 4. Assign one signatory or several, and add more conditions when you need them The department routing is the part I like most. The system picks the right signatory the moment a contract meets the condition, so nobody has to remember who signs what. Fewer manual steps at signing, cleaner records, and the right person on every contract without the back and forth. If you run an enterprise legal team and want to see how it fits your workflow, happy to show you.

Proud moment for Legistify! Our Co-founder Pratik Mohapatra joined the AI & Emerging Technologies panel at the Legal Era India Conclave 2026 – Mumbai Edition. Anoushka Mehta & Parth Lalai met legal professionals and exchanged ideas on legal tech. 📍 Mumbai | 📅 21 Aug

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Every enterprise has the same AI models now. The top 10% still pull away and this chart shows why. OpenAI just published data on how enterprises actually use AI. The leading companies now produce 8.3x as much AI output per person as typical firms, up from 2.6x in January. Everyone has the same access, so the difference comes down to what they do with it. Two habits stand out: 1. Frontier firms use plugins twice as often 2. They use skills six times as often A skill is just one person's good prompt saved so the whole team can run it. That 6x gap is a firm turning its best thinking into a shared standard, while everyone else keeps it stuck in a few people's hands. Since February, legal was the fastest-growing function for agents, up 108 times, ahead of every other department. If you are working on closing this gap inside your own team, I would like to hear how you are approaching it.