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3K followers
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Max Christoff shared thisI had a chance to sit down with Raconteur to discuss the use of AI in high-stakes legal work at Everlaw. Used well, AI can provably outperform humans on a variety of legal document review tasks. But the lessons and basic approaches on how to do this responsibly and effectively are by no means unique to litigation or the legal industry. Over time I’ve had the opportunity to work on a popular web browser, on payment systems, and a variety of financial services applications. These domains are all nuanced and critical in different ways. Copy-pasting AI into any of them is not going to end well. What this piece argues is that: - Context matters. Spend more time here, understanding exactly what ‘context’ means for your industry and how your model is going to get it. This is a better use of time than obsessing about the choice of which model is currently performing better on which benchmark. - To achieve context, you need systems and platforms that are connected. Insist that your systems, whether developed in-house or purchased from vendors, interoperate. Not just that they’ve checked the MCP box, but that the MCP connection actually provides access to the critical content that matters. Of course even if you have context and connectivity, there’s still the challenge of what will fit within a context window. That’s where additional techniques (RAG, memory/compaction, etc.) will come into play. https://lnkd.in/gv_vPCbr Enjoy!
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Max Christoff shared thisAsk @AJ Shankar and me anything! We’re joining the r/legaltech community now to answer your questions about Everlaw, how I think about building software in the AI era, or even how studying ancient Greek led me to computer programming. I’m here for it. Getting started here: https://lnkd.in/ggTfDHmG
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Max Christoff shared thisAsk me your toughest questions about Everlaw! Next Thursday September 24th, AJ Shankar and I are spending 90 minutes answering questions on Reddit r/legaltech, hosted by Alex Denne and Melia Robinson. We're eager to hear from users, prospects, and anyone else in the broader legal tech community about Everlaw, how we build, how we think about the future, and how we're evolving in the AI era. We'll be on next Thursday 9/26, 10:30am Pacific / 1:30pm Eastern / 6:30pm UK, for 90 minutes. The question thread is pinned on r/legaltech.
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Max Christoff shared this#Astra is a game-changing model, and we're delighted to partner with OpenAI to bring the evidence layer to legal AI. If you work in litigation or investigations, you already know why this matters. The Everlaw plugin for ChatGPT Enterprise is available today.Max Christoff shared this🚨 Just announced: Everlaw’s plugin for ChatGPT Enterprise is available today. We’re excited to join OpenAI's Astra for Law launch. Legal teams can search and analyze case materials from ChatGPT Enterprise without exporting or duplicating files, while existing Everlaw permissions remain intact. Our new plugin connects frontier AI with the governed evidence at the center of litigation and investigations. It’s another step in our Everlaw Everywhere strategy – helping legal teams work directly with their evidence in Everlaw, from whichever platform they choose. Learn more here: https://lnkd.in/edJgpQq9
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Max Christoff shared thisLooking for some real numbers on token usage and ROI? VentureBeat interviewed me recently about how I’m approaching this at Everlaw. https://lnkd.in/gfNF3ddv I’ve typically seen two approaches, both unsatisfactory: Either it’s a free-for-all (or tokenmaxxing-crazed environment), and a great way to spend $$$ with little to show for it, or there’s a hard cap per engineer per month. But what if it’s a company priority to undertake a large-scale legacy system rewrite? One that might have taken multiple engineers chipping away for years? Or a new product line that will bring in 7+ figures of revenue and might have otherwise taken years to build? At some point there’s a real business case to be made: spending $50k or $250k is perfectly justified (and may be a bargain) for that level of impact. My framework is simple: 1. Every engineer has a standard monthly token budget. This is sufficient ~80% of the time. We monitor and adjust this number regularly. 2. If they hit their cap, the engineer can easily get 2x budget by dropping a one-line request to the tools team, cc’ing their manager. There’s no elaborate approval flow or justification. This step is simply meant to provide basic visibility and to encourage thoughtfulness. There might be a follow-up conversation about what models they’ve tried and what kind of results they’re seeing, but they’re immediately unblocked from doing their work. ~10-15% of the engineers fall into this category. #1 and #2 above account for the vast majority of work. We give our engineers latitude to make sensible decisions as to what model works best for them, as long as it's within the menu of options that pass security muster and with which we have an enterprise agreement. One notable exception is that we’ve disabled “fast mode” at an org level. Most models have a fast mode that produces exactly the same quality of results at greater speed – and at double or triple the cost. For building software, I just haven’t found any justification of why waiting 3 minutes for a coding task differs materially from 7 minutes. Either way, the engineer is going to get a cup of coffee or check on another task while waiting. 3. For a larger project–say $5k for a big refactor or $250k for a building a new product we can bring to market– a business case is written: - What's the actual proposal? - What’s the expected token cost? What would the human time be to build with / without AI? - What’s the expected return on investment? Over what time period? -What are the specific AI models we’re planning to use for each step (spec, design, coding, review, QA, etc.), and what harnesses? Have we considered cheaper models for some parts of the work? - What experiments have we done to give us some confidence in the estimated costs and results? If most of these questions look old-fashioned, they are! It always comes back to the ROI.Enterprise AI spending: the ROI is unproven | VentureBeatEnterprise AI spending: the ROI is unproven | VentureBeat
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Max Christoff shared thisMy 7th-grader interviewed me this week about 9/11 for class. 25 years later, this was still harder than I expected. In 2001, I was early in my technology career at Morgan Stanley. Writing code, learning what traders actually did, and generally trying to be useful. The company had merged with Dean Witter Discover in 1997 to become the conglomerate mouthful "Morgan Stanley Dean Witter & Discover", but the real integration was finally starting now. I was diving into the engineering work to connect our identity and communications systems. I was on the Morgan Stanley side, based in midtown Manhattan at 750 7th Ave; my Dean Witter technical counterparts were based in 2 World Trade Center. I was living in Princeton at the time and normally commuted into the office via NJTransit. But on September 11th, my schedule was different. I had a planning session with the Dean Witter side at 9am, and since this would be my first time meeting them, I was going to attend in-person at 2 WTC. Blame my poor planning that morning, but I failed to catch the early express train from Princeton. Not wanting to arrive late, I made a game-time decision to continue on my normal route into the Midtown office rather than transfer to the PATH train downtown. I would take the meeting over video instead. The meeting never happened. I looked up from my SPARCstation to see colleagues gathered at the window. The first tower had just been hit. Even 40 blocks north in the light of a hazy morning, the flames were clear, and smoke was filling the downtown skyline. No one knew whether this was a freak accident or something deliberate. TVs on the floor were turned up to max volume to catch the news. I knew the people I worked with were in the other building, but wondered if they were ok. At 9:03am, nose pressed against the glass and watching the first tower burn, I watched a plane fly into their building. Time became a blur, and my memory hazy. I walked to Penn Station and joined throngs of office workers trying to get out of the city, not knowing what might come next. On the ride home, I remember staring at people’s shoes, mentally tallying how many were covered in ashes and soot. I learned only later of the heroism of Rick Rescorla, Morgan Stanley’s security chief in 2WTC. If you don’t know his story, look it up. He ignored “everything is fine” orders from the building, pulling reluctant brokers away from their desks and herding them down the emergency stairs. 2,700 Morgan Stanley employees survived that day. Traveling back up the stairs to look for stragglers, he was one of the 13 who didn’t make it. Two years later, on the anniversary of the attacks, I took this photo from my Manhattan apartment. Beams of light replaced the void where the buildings had stood. My story is by no means exceptional, and could have easily turned out differently. It brought me close enough to the brush of tragedy to be grateful for the time we have together. And now, it’s becoming a history paper!
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Max Christoff shared thisA few reflections from #ILTACon: If you’re in #legaltech you’ve already heard about the buzzing scene at this year’s ILTACon conference. The sheer scale of the event reflected not only the pace of innovation in this industry, but also the level of interest in AI and technology from legal professionals – many of whom have leapfrogged from cautious observers and beta testers to enthusiastic adopters who are already realizing value today. The conversation this year moved on from “should we be using AI?” to “how do we get the most value from AI?”. The answer is context. In legal work, “context” concretely means access to statutory and case law, access to institutional knowledge, and access to evidence (that’s where we come in at Everlaw). Not surprisingly, MCP was on everyone’s lips as a way for AI-powered systems to connect with each other to obtain that context. I’ve always been a strong proponent of open platforms instead of walled gardens, and treating APIs as a core part of the product rather than as an afterthought. As a company, we came to the conclusion a while ago that building robust interconnections with other systems would be the logical evolution for our platform. To that end, I’m super proud of the 4 (!) announcements that we made last week to enable Thomson Reuters CoCounsel Legal, Microsoft Copilot for Legal, Google Gemini Enterprise for Legal, and Harvey to connect to Everlaw. This is in addition to the integrations we announced previously for Anthropic Claude Cowork for Legal and Legora. These integrations not only provide the evidentiary context for other legal AI tools, but it’s a strong affirmation of our conviction that legal professionals will be using a variety of tools, and we should meet them wherever they’re doing their work. All in all, it was a jam-packed week. Beyond the launch announcements, I spent a lot of time talking with customers and partners. Not just to get validation on our direction, but also to dig into what problems they’re trying to solve. I came away more inspired than ever. Hats off to the organizers at International Legal Technology Association (ILTA) for putting on an amazing event. I’ll also say it was both surreal and sad to be in Nashville at the time of Dolly Parton’s passing, but I’m glad I was there to hear and see the many tributes to her legacy reverberating throughout the city. 🎶 ❤️
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Max Christoff shared this@Everlaw engineering is growing! If you're in your final year of university or are just starting out now in the workplace, this is for you. When I think back to what I would have wanted for myself early in my own career, I would have listed: 1) real mentorship 2) people willing to take a bet on me 3) tackling ambitious problems 4) a mission that resonated 5) a great culture Everlaw is where you get all 5. Come join us!Max Christoff shared thisHiring early career software engineers! Engineering is in our DNA. Founded by a computer scientist, Everlaw gives engineers real ownership, strong coaching and mentorship, and maintains a high bar for quality with a long term view of our roadmap. While many tech companies are pulling back on early-career engineer hiring, Everlaw is investing in the next generation of great engineers. Join our cohort of rising talent to solve complex technical problems that help legal teams uncover the truth. Roles are set to start in the spring of 2027 and are open to students graduating in 2027 (or sooner). Our Engineering team is led by Max Christoff, who previously led the team that built Chrome at Google. If the amazing engineering culture, mission, mentorship and great snacks aren’t enough, we’re also a certified Great Place to Work. Ready to build and grow with us? Apply here by October 1 for this round of hiring: https://lnkd.in/gdT86AfJ
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Max Christoff shared thisToday at Everlaw we announced key partnerships and integrations with Thomson Reuters CoCounsel Legal, Harvey, Google Cloud’s Gemini Enterprise for Legal and Microsoft Copilot, expanding how legal teams can use their chosen AI tools with Everlaw’s trusted evidence foundation. In litigation, you need both the law and the facts. This announcement extends Everlaw’s reach in the legal software ecosystem, and reflects our broader approach to AI interoperability: legal teams should be able to work with the AI tools and agents they prefer without moving sensitive evidence into disconnected systems, or compromising the controls that legal work demands. The new integrations add to our previously announced work with Claude for Legal and our strategic partnership with Legora. And they're core to how I've always approached building products: they demonstrate the power of open platforms that interoperate (safely) and meet the customer wherever they're working. Read more below about our vision to use the power of Everlaw everywhere legal work gets done, with the partnerships announced today: https://lnkd.in/gzBvtYNrMax Christoff shared this🚨 Breaking News: Today, Everlaw announces partnerships with four leading AI platforms to bring Everlaw everywhere legal teams work. We’re announcing new partnerships and integrations with Thomson Reuters CoCounsel Legal, Harvey, Google Cloud’s Google Gemini Enterprise for Legal, and Microsoft 365 Copilot. AI is only as useful as the documents, skills, and context it has access to. These integrations will allow legal teams to securely search their Everlaw data and use Everlaw’s powerful analytical tools from the AI platforms they choose. Building on the rave reviews of our Claude integration, we’re giving customers more choice as legal teams adopt a range of AI tools, bringing their case-critical evidence wherever they choose to work. Read the news: https://lnkd.in/gYz-Jfj6 #LegalTech #ILTACON2026 #GeminiEnterprise #MCP
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Max Christoff reacted on thisMax Christoff reacted on thisMy heart is full after SOLID New York. To our speakers, sponsors, faculty, attendees, and the team who made SOLID New York possible: thank you. Thank you for your time, your generosity, and the ideas and experience you shared. Thank you for showing up with curiosity and contributing so much to the people around you. Looking around the room yesterday, I kept thinking how lucky we are to be in the right place, at the right time, with the right community. There is so much to appreciate about the people who make SOLID what it is. Today, I just want you to know how grateful I am. Carpe Momentum! David Cowen #SOLIDNYC2026 #SOLIDNYC26 #ThinkOutLoudTogether #SOLIDSUMMIT
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Max Christoff reacted on thisBlog from our CEO Josh Bloom on a meaningful announcement today from arXiv - tapping brakes, and limiting the number of submissions per submitter: (2) per calendar month, with no more than (3) active submissions at any one time. As he thoughtfully points out at end of blog... "The end of an era always means the beginning of another. This is not to say that arXiv will not remain highly relevant and central to the future of science. It’s just that today’s announcement appears to be an acknowledgment that, indeed, as times are changing, new ways of furthering the progress of science need to emerge."Max Christoff reacted on thisarxiv is tapping the brakes. Announced today, the new cap of two submissions a month looks like a small policy change. For many of us in science, it's a sea change.
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Max Christoff liked thisMax Christoff liked thisAI is only as reliable as its evidence. In this piece in Raconteur, our CTO, Max Christoff, explains why trusted evidence, strong governance and connected tools are essential to reliable AI in legal and other high-stakes environments. Read the article: https://lnkd.in/gKwAph-A
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Max Christoff liked thisMax Christoff liked thisSkipping Sam Altman’s keynote at #openai devday to join our RAG CLE goldcast 😂. Answering any questions on #generativeai in the chat (For the rules lawyers out there, this counts as “presenting” at DevDay. 😉)
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Max Christoff liked thisMax Christoff liked thisI’m thrilled to announce that I’ve joined the Career Development Office at UC Berkeley Law as Associate Director, Private Sector Counseling and Programs. I’ll be working with law students and alum to help them with understanding the private legal sector, job searches, and the decisions and details that come with both of those. After 21 years in private practice, this is a big change! Thank you to everyone I’ve worked with along the way and especially to those who I’ve had the privilege of managing or mentoring. Thank you also to the UC Berkeley Law CDO team for this opportunity. And, as always, Go Bears!! 🐻 ⚖️
Experience & Education
Patents
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Confirming physical possession of plastic NFC cards with a mobile digital wallet application
Issued US 10685349
See patentA user accesses a merchant system website via a user computing device, selects items for purchase, and selects an option to checkout using a digital wallet account. The user selects payment information associated with a payment card device for use in an online transaction. The merchant system transmits an unpredictable number to the user computing device. The user taps the payment card device to the user computing device to establish a wireless communication channel over which the payment card…
A user accesses a merchant system website via a user computing device, selects items for purchase, and selects an option to checkout using a digital wallet account. The user selects payment information associated with a payment card device for use in an online transaction. The merchant system transmits an unpredictable number to the user computing device. The user taps the payment card device to the user computing device to establish a wireless communication channel over which the payment card device receives the unpredictable number. The payment card device transmits payment card information and a check sum calculated from the unpredictable number and by the payment card device to the merchant system via the user computing device. The merchant system transmits the check sum and payment card information in a transaction authorization request to the issuer system, which verifies the check sum using the shared secret and the unpredictable number.
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Rule-based electronic message processing
Issued US 8621007
See patentIn various embodiments, a computer-assisted method for processing an electronic message includes the steps of reviewing the recipients of the electronic message, evaluating whether any of the recipients of the electronic message are potentially incorrect, and prompting the sender of the electronic message to evaluate whether the potentially incorrect recipients are correct. This computer-assisted method, as it evaluates whether a recipient may be potentially incorrect, may provide an effective…
In various embodiments, a computer-assisted method for processing an electronic message includes the steps of reviewing the recipients of the electronic message, evaluating whether any of the recipients of the electronic message are potentially incorrect, and prompting the sender of the electronic message to evaluate whether the potentially incorrect recipients are correct. This computer-assisted method, as it evaluates whether a recipient may be potentially incorrect, may provide an effective warning to the sender of the electronic message as opposed to previous systems which automatically, and often, spuriously, warned the sender that a recipient may be incorrect without ever evaluating the recipients.
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Processing encumbered electronic communications
Issued US 7912909
See patentThe invention includes, in various embodiments, a method for archiving an electronic message which includes the steps of scanning the electronic message to determine whether at least a portion of the message is encumbered, disencumbering the encumbered portion of the message if it is encumbered, storing a copy of the disencumbered electronic message, and transmitting the electronic message to a recipient. Once stored, the disencumbered message can be accessed and reviewed by a supervisor, for…
The invention includes, in various embodiments, a method for archiving an electronic message which includes the steps of scanning the electronic message to determine whether at least a portion of the message is encumbered, disencumbering the encumbered portion of the message if it is encumbered, storing a copy of the disencumbered electronic message, and transmitting the electronic message to a recipient. Once stored, the disencumbered message can be accessed and reviewed by a supervisor, for example, without having to request a password from the sender or recipient. Encumbered messages include messages, or portions of messages, which are password protected, encrypted, and/or otherwise structured to prohibit unauthorized access thereto.
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AI drafting assistant for a web browser
Filed US-20250117573-A1
See patentDynamically engineer/enhance prompts based on the context of the
web page, facilitating more accurate and relevant
responses from the generative language model. -
System and method for importing electronic credentials with a third-party application
Filed 20210133727A1
See patentPlatform oriented approach for 3rd parties to store electronic credentials (e.g. credit cards, loyalty cards, gift cards, boarding passes, event tickets) in a digital wallet
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German
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French
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Greek
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Greek, Ancient (to 1453)
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Latin
Professional working proficiency
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Spanish
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Norwegian
Elementary proficiency
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English
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