Small business loans are available from a large number of traditional and alternative lenders. A small business loan can help your business grow, fund new research and development, help you expand into new territories, enhance sales and marketing efforts, allow you to hire new people, and much more. With interest rates and lending criteria constantly evolving, it's more important than ever to approach the loan process in an informed and organized way.This article explains the key steps to follow
Small business loans are available from a large number of traditional and alternative lenders. A small business loan can help your business grow, fund new research and development, help you expand into new territories, enhance sales and marketing efforts, allow you to hire new people, and much more. With interest rates and lending criteria constantly evolving, it's more important than ever to approach the loan process in an informed and organized way.
This article explains the key steps to follow to get a small business loan, along with practical advice and insight on the lending process.
1. Understand the Different Types of Small Business Loans Available
There are multiple types of small business loans available. The options vary depending on your business needs, the length of the loan, and the specific terms. Here are the main small business loan choices:
Small business line of credit. A line of credit allows your business to access funds from a lender as needed, up to a set limit (e.g., $100,000). It is useful for managing cash flow and unexpected expenses. You are not charged interest until you actually draw down the funds. Interest is typically paid monthly, and most lines of credit require annual renewal.
Accounts receivable (AR) financing. An AR line of credit is a credit facility secured by the company's accounts receivable. It allows you to access cash quickly based on outstanding invoices, and the line is paid down as customers pay their bills. This is an especially useful tool for businesses with long payment cycles.
Working capital loans. A working capital loan is used to finance a company's daily operations and manage fluctuations in revenues and expenses. These loans are typically short-term (30 days to one year) and range from $5,000 to $100,000. Companies with little or no credit history may need to pledge collateral or provide a personal guarantee.
Small business term loans. Term loans are for a set dollar amount (e.g., $250,000) and are used for business operations, capital expenditures, or expansion. The principal is typically repayable within six months to three years, and the loans can be secured or unsecured with fixed or variable interest rates.
SBA small business loans. Some banks offer low-interest-rate loans for small businesses backed by the U.S. Small Business Administration (SBA). Because of the SBA guarantee, the interest rates and repayment terms are more favorable than most conventional loans. Loan amounts range from $30,000 to $5 million, though the application process can be time-consuming and involves strict eligibility requirements.
Equipment loans. Small businesses can finance the purchase of equipment, vehicles, and software through an equipment loan, typically requiring a 20% down payment. Loan amounts normally range from $5,000 to $500,000. Equipment loans can also sometimes be structured as equipment leases.
Merchant cash advances. A merchant cash advance (MCA) provides a lump sum of capital in exchange for a percentage of future credit card sales or daily bank deposits. MCAs are fast to obtain but typically carry high effective interest rates and should be used carefully.
Small business credit cards. Business credit cards can serve as short-term small business financing. Many offer promotional 0% introductory rates, cash-back programs, and rewards. Issuers include American Express, Capital One, Bank of America, Chase, and Brex, among others.
2. Research Available Lenders
There are more lenders than ever willing to lend to small businesses. Here are the main categories of lenders to consider:
●Direct online lenders. A growing number of fintech companies make small business loans through a fast, mostly digital process. Loan amounts can range from $5,000 to $500,000, and funding can often be received within days of approval.
●Large commercial banks. Traditional lenders such as Wells Fargo, JPMorgan Chase, and Bank of America serve the small business market. The loan approval process tends to be slower due to more rigorous underwriting standards, but rates are often competitive for well-qualified borrowers.
●Local community banks and credit unions. Many community banks and credit unions have a strong desire to make small business loans to local businesses and may offer more personalized service and flexible terms.
●Alternative lending platforms. Platforms such as Funding Circle act as intermediaries between institutional lenders and small business borrowers, often with faster decision timelines.
●SBA-backed bank lenders. A number of bank lenders issue loans guaranteed by the SBA, allowing them to offer more attractive rates and terms than conventional loans. The SBA's website maintains a list of the 100 most active SBA lenders.
●CDFI lenders. Community Development Financial Institutions (CDFIs) are mission-driven lenders that focus on underserved markets and small businesses that may not qualify for traditional bank financing.
3. Anticipate How Lenders Will View Your Credit and Risk Profile
Lenders ultimately make a judgment call on whether to approve a small business loan based on the borrower's credit and risk profile. Review the following factors carefully and consider taking remedial action where needed before applying:
●Credit score and credit report. Lenders will review your personal and business credit reports, scores, and payment history. Review your credit reports in advance and dispute or resolve any errors.
●Outstanding loans and cash flow. Lenders will review your existing debt to assess whether your cash flow is sufficient to service both existing and new obligations.
●Assets in the business. Lenders will look at business assets—especially current assets like cash and accounts receivable—to evaluate what could be recovered in the event of a default.
●Time in business. Most lenders prefer businesses that have been operating for at least one to two years, though some online lenders will work with newer businesses.
●Annual revenue. Many lenders have minimum annual revenue requirements. Demonstrating consistent and growing revenue significantly strengthens your application.
●Investors in the company. Lenders view the company more favorably if it has professional venture capital, strategic, or prominent angel investors involved.
●Financial statements. Lenders will scrutinize your balance sheet, income statements, and cash flow statements.
4. Make Sure Your Financial Statements Are in Order
Depending on the size of your loan, your financial statements and accounting records will be reviewed carefully. Make sure they are complete, accurate, and thorough—including balance sheets, income statements, and cash flow statements. The lender will analyze cash flow, gross margin, debt-to-equity ratio, accounts payable, accounts receivable, and EBITDA, so be prepared to answer questions on those topics. Consider having your accountant review your financial statements before applying to anticipate issues a lender may raise.
Lenders prefer financial statements that have been audited by a certified public accountant (CPA), but many small businesses find a full audit cost-prohibitive. One alternative is to have the statements reviewed by a CPA—a less expensive process that still carries some professional credibility. Some lenders, particularly online lenders, may not require audited or reviewed statements and may instead rely more heavily on bank statements and tax returns.
5. Gather Detailed Information for Your Loan Application
To be successful in obtaining a small business loan, you must be prepared to provide detailed information and documentation about your business. Being organized and proactive about gathering this information will make the process faster and improve your chances of approval. The following documents/information are typically required:
●Name of business (including any DBAs) and Federal Tax ID
●Legal structure of the business (LLC, S corporation, C corporation, etc.) and state filings such as certificate of incorporation and good standing certificates
●List of executive officers and their backgrounds
●Financial statements for the past two to three years, plus year-to-date financials for the current year
●Projected financial statements for the next one to two years
●Business and personal tax returns for the past two to three years
●Business bank statements for the past three to twelve months
●Business credit report (e.g., from Dun & Bradstreet or Experian Business)
●Amount of loan requested and detailed use of proceeds
●Description of potential collateral available for the loan
●Personal financial statements of the principal owner if a personal guarantee may be required
●Business plan or executive summary describing the business, market opportunity, and business model
6. Be Prepared to Specify How Much You Want to Borrow and the Use of Proceeds
The lender will want to know exactly how much funding you are seeking and how the loan proceeds will be used. Common uses of proceeds include equipment purchases, capital expenditures, business expansion, hiring, increased inventory, enhanced marketing efforts, technology development, or entry into new facilities or markets. Be specific—lenders are more comfortable when borrowers have a clear, well-reasoned plan for the funds.
It may be wise to borrow a modest buffer above your minimum needs to avoid a short-term cash crunch in the months after closing. At the same time, do not over-borrow—requesting an amount significantly above what your financials support will raise red flags for underwriters. Run conservative cash flow projections to find the right loan amount for your situation.
7. Determine What Security or Guarantee Can Be Provided
A lender is primarily concerned about the ability of the borrower to repay the loan. Offering a security interest in company assets—such as equipment, real property, accounts receivable, or inventory—can significantly improve your chances of getting approved and obtaining favorable terms. Some lenders may insist on the personal guarantee of the principal owner of the business, which means your personal assets could be at risk if the business defaults.
A personal guarantee should be avoided if at all possible, particularly for early-stage businesses where the personal and business assets of the owner may be intertwined. If a personal guarantee is unavoidable, try to negotiate for it to be limited in scope or to burn off over time as the loan is repaid. Always consult with legal counsel before signing any guarantee agreement.
8. Analyze the Key Terms of the Proposed Business Loan
To make sure the proposed loan makes sense for your business, analyze the key terms carefully and compare them with those available from competing lenders. Key terms to review include:
●What is the interest rate, and is it fixed or variable? Many variable-rate loans are tied to the prime rate or SOFR (the Secured Overnight Financing Rate, which has replaced LIBOR as the standard benchmark).
●How often is interest payable, and when is principal due or amortized over the life of the loan?
●What are the loan origination fees, underwriting fees, administration fees, and other closing costs?
●What operating covenants are imposed on the business (such as a minimum cash balance or a maximum debt-to-equity ratio)?
●Under what circumstances can the lender call a default?
●Is there collateral or a personal guarantee required?
●What periodic financial reports are you required to provide to the lender?
●Are there restrictions on how loan proceeds can be used?
●Can the loan be prepaid early without a penalty? If there is a penalty, is it reasonable?
9. Review Your Online Profile and Digital Presence
A small business lender will conduct due diligence that may include reviewing information publicly available online about your business and its principal owners. Do a proactive review of your digital footprint before applying, anticipating what a lender may find:
●Review your company's website: Is it current, professional, and reflective of an active business?
●Review your presence on LinkedIn, Facebook, X, Instagram, and other social media platforms.
●Check Yelp, Google Reviews, and other review sites for customer feedback—both positive and negative—that a lender might see.
●Review the principal owner's personal LinkedIn profile and public social media presence.
●Search your business name and the owner's name on Google to see what third-party content appears.
Address any outdated information, negative reviews, or unprofessional content before submitting your application. A polished and consistent digital presence reinforces the credibility and stability of your business.
10. Key Small Business Lenders
Finding the right lender is as important as finding the right loan. Below are some reputable small business lenders across a range of loan types, sizes, and borrower profiles:
●U.S. Small Business Administration (SBA). The SBA offers multiple government-backed loan programs—including the flagship 7(a) loan, CDC/504 loans, and microloans—through an extensive network of approved bank and non-bank lenders nationwide. Because of the federal guarantee, SBA loans offer some of the most competitive rates and terms available to small businesses.
●Wells Fargo Small Business. One of the largest small business lenders in the United States, Wells Fargo offers term loans, lines of credit, SBA loans, equipment financing, and business credit cards. With a nationwide branch network and an extensive digital platform, Wells Fargo is a go-to option for established small businesses seeking competitive rates.
●JPMorgan Chase Business Banking. Chase Business Banking provides a broad range of lending products—including business term loans, lines of credit, SBA loans, and commercial real estate financing—backed by the resources and branch network of the nation's largest bank. Chase is well-suited for creditworthy small businesses with at least two years of operating history.
●Bank of America Small Business. Bank of America offers a comprehensive suite of small business financial products, including secured and unsecured term loans, business lines of credit, SBA loans, and commercial real estate loans. Existing Bank of America business customers may qualify for preferred pricing and streamlined application processes.
●OnDeck. OnDeck is a leading online lender that offers short-term business loans and lines of credit to small businesses, with funding decisions often available within hours and funding as fast as the same day. OnDeck is an option for businesses that need fast access to capital and have at least one year in business and $100,000 in annual revenue.
●Funding Circle. Funding Circle is a leading small business lending platform that offers SBA loans and term loans with competitive interest rates, transparent fee structures, and loan amounts up to $500,000. Funding Circle is suited for established small businesses looking for a faster and more transparent alternative to traditional bank loans.
●Bluevine. Bluevine specializes in business lines of credit and business checking accounts designed specifically for small businesses and entrepreneurs. Lines of credit up to $250,000 are available with a streamlined online application. Bluevine is known for fast funding and flexible repayment structures tied to business cash flow.
●Lendio. Lendio is a small business loan marketplace that matches borrowers with over 75 lenders through a single application, covering SBA loans, term loans, lines of credit, equipment financing, merchant cash advances, and more. Lendio is useful for business owners who want to compare multiple loan offers quickly and efficiently.
● Fundbox. Fundbox is a financial tech company that provides fast working capital to small and medium-sized businesses. It specializes in revolving lines of credit, allowing business owners to bridge cash flow gaps, pay expenses, or manage inventory. Funds can be received as soon as the next business day after approval. Fundbox is known for being more accessible to startups and smaller businesses than traditional banks. There is no application, origination, or inactivity fees; costs are based on weekly fees when funds are drawn.
Conclusion on Small Business Loans
Small business loans are available from many different lenders, with a wide variety of choices tailored to the financial situation, stage, and needs of your business. Whether you seek a traditional bank term loan, an SBA-backed loan with favorable government guarantees, or a fast online line of credit from a fintech lender, understanding the landscape and preparing thoroughly will make a significant difference in your outcome. By anticipating what lenders will review and require, building a strong credit and documentation profile, and selecting the right type of financing for your specific purpose, you greatly increase your chances of obtaining a beneficial small business loan on favorable terms.
The small business lending environment continues to evolve, with new fintech platforms, government programs, and alternative lenders expanding access to capital for businesses of all sizes and stages. Stay informed about changes in interest rates, SBA program updates, and new lending products as you plan your financing strategy. A qualified financial advisor, accountant, or business attorney can be an invaluable partner as you navigate the loan process and negotiate the terms of your financing.
The legal technology industry is undergoing a period of rapid transformation, driven by advances in artificial intelligence and increasing demand for more efficient, accessible, and cost-effective legal services. What began as a market focused on practice management software and basic document automation has evolved into a sophisticated ecosystem of AI-powered tools capable of assisting with legal research, contract drafting and analysis, litigation support, and complex workflow automation.Much
The legal technology industry is undergoing a period of rapid transformation, driven by advances in artificial intelligence and increasing demand for more efficient, accessible, and cost-effective legal services. What began as a market focused on practice management software and basic document automation has evolved into a sophisticated ecosystem of AI-powered tools capable of assisting with legal research, contract drafting and analysis, litigation support, and complex workflow automation.
Much of the current momentum stems from the emergence of large language models, including ChatGPT in late 2022. These systems demonstrated the ability to generate coherent legal text and assist with analytical tasks, while also highlighting important limitations—particularly around hallucinated citations and the need for human oversight.
Today, both established legal information providers and a new generation of AI-native startups are investing heavily in the space. The result is a rapidly evolving landscape reshaping how legal services are delivered across law firms, corporate legal departments, and the consumer market.
Of course, this article was written with the research assistance of AI. But hallucinations and mistakes can be made by AI tools, so careful review and checking is prudent. For example, I also ran this article through Claude, Google Gemini, and ChatGPT, and asked for corrections and improvements.
This article provides an overview of leading AI-powered legal technology companies.
AI-Native Legal Platforms
Harvey AI
Harvey is an enterprise AI platform purpose-built for legal professionals. It assists attorneys with legal research, document drafting, contract review, due diligence, regulatory analysis, and litigation support. The platform is built on large language model infrastructure and adapted for legal workflows through integrations, prompt engineering, and domain-specific enhancements.
Harvey integrates with law firm knowledge management systems, enabling users to query internal precedent libraries alongside external legal sources. It supports multiple practice areas, including corporate transactions, litigation, tax, and compliance, and can generate memoranda, contract drafts, and structured research outputs. As with all generative AI tools, outputs require attorney verification.
The platform was initially designed for large law firms and enterprise legal departments, with an emphasis on data security and auditability. It then expanded to be useful for mid-size law firms, boutique specialty firms, banks and enterprise compliance groups, and alternative legal service providers.
Legora is a collaborative AI legal workspace that combines AI-assisted drafting and research with real-time multi-user collaboration. It enables attorneys to work simultaneously on documents while receiving contextual AI suggestions.
The platform integrates with existing workflows and supports a range of legal tasks, including contract drafting, memos, and regulatory analysis. Its context-aware approach aims to improve relevance, though outputs still require review.
Location: Stockholm, Sweden
CEO: Max Junestrand
Selected Investors: Accel, Benchmark, Bessemer, General Catalyst, ICONIQ, Y Combinator, Redpoint, Bain Capital, Menlo Ventures, Salesforce Ventures
Spellbook is an AI-powered contract drafting and review tool delivered as a Microsoft Word add-in. It enables attorneys to generate and analyze contract language within their existing drafting environment.
The platform suggests clauses, flags risks, and drafts sections based on user prompts. It is particularly accessible to smaller firms.
Location: Toronto, Canada
CEO: Scott Stevenson
Selected Investors: Khosla Ventures, Y Combinator, Two Small Fish Ventures, Garage Capital
Stella Legal is a leading legal and AI technology consultancy and advisory firm. The company operates as an “AI-native” strategic partner for enterprise legal departments and corporate procurement teams.
The company’s core offerings include AI enablement, e-discovery, strategic implementation of Contract Life Cycle Platforms, and software spend advisory. Their global team of lawyers and technologists work across Europe, Africa, and the United States. Their services are particularly valuable for in-house legal teams.
The company also has an M&A Division that allows M&A buyers and sellers to more efficiently prepare for and successfully close M&A transactions. The M&A team has participated in over 300 deals as founders, buyers, sellers, Board members, venture investors, and CEOs. The company’s AI platform and tools assist strategic acquirers, M&A sellers, venture capital firms, and private equity firms. The M&A AI-enabled assistance includes data room review, disclosure schedules, and due diligence/red flags analysis.
Location: New York, New York; London, United Kingdom; and Phoenix, Arizona
CEO: Tyson Ballard
Selected Investors: Venture and strategic investors
Ironclad is a leading contract lifecycle management (CLM) platform that enables legal and business teams to streamline the entire contract process from request through drafting, negotiation, execution, and renewal.
Location: San Francisco, California
CEO: Dan Springer
Selected Investors: Accel, Sequoia Capital, Emergence Capital, Y Combinator, BOND, Franklin Templeton
Luminance is an end-to-end Legal AI platform empowering businesses at every touchpoint they have with their contracts. Luminance's multi-agent platform automates entire workflows across the contract lifecycle, from creation and negotiation through to risk review and compliance. Built for enterprise legal teams, it acts as an intelligent layer that understands, reasons, and acts on contracts autonomously, continuously learning from every interaction and carrying context forward to become increasingly attuned to each organization.
Developed by AI experts from the University of Cambridge, its Legal-Grade™ AI is trained on over 220 million verified legal documents. This legal-specific specialism underpins its ability to understand and reason over complex contractual language. A proprietary orchestration layer, known as the “panel of judges” coordinates multiple specialist AI agents, cross-checking outputs to ensure accuracy and consistency.
Location: London, United Kingdom
CEO: Eleanor Lightbody
Selected Investors: Point72, Forestay Capital, March Capital, RPS Ventures, Schroeders Capital, National Grid Partners
Avvoka is a document automation and AI-assisted drafting platform designed for law firms and in-house legal departments. It enables legal teams to build intelligent templates with conditional logic, automate document generation through guided questionnaires, create a “drafting engine” via the API and manage the document lifecycle from first draft through final execution.
The platform's AI layer analyzes incoming documents and converts them to automated templates. Its drafting features combine deterministic drafting with AI to enable complex and accurate drafting at scale.
Avvoka is used by 20% of the Am Law 100 and leading in-house legal teams across the world.
LegalOn is an AI-powered contract review platform that analyzes commercial contracts against market-standard playbooks, identifying non-standard terms, high-risk provisions, and missing clauses with specific redline suggestions tailored to the reviewer's perspective. The platform covers a broad range of commercial contract types across multiple industry verticals.
Location: San Francisco, California
CEO: Daniel Lewis, CEO of LegalOn’s U.S. operations; Nozomu Tsunoda, co-founder and Group CEO
Selected Investors: Goldman Sachs, World Innovation Lab
Juro is a British legal technology company that develops browser-based contract lifecycle management (CLM) software, used by corporate legal and business teams to draft, negotiate, and manage contracts. The platform accelerates contracting through the full lifecycle with AI automation for drafting, collaborating, and post-signature analysis, and offers plug-and-play integrations that let teams initiate and manage contracts in the tools they already use every day.
Juro was co-founded in 2015 by Richard Mabey, a former corporate lawyer at the Magic Circle firm Freshfields, and Pavel Kovalevich, a former IT consultant. In 2024, the company was named one of the 100 fastest-growing private technology companies in the UK by The Sunday Times, ranking 31st based on annual sales growth, and in 2025, European technology investment bank GP Bullhound named Juro "Software Company of the Year" at its Allstars Awards.
Location: London, United Kingdom (headquarters), with an engineering presence in Riga, Latvia, and a U.S. office in Boston, Massachusetts
CEO: Richard Mabey
Selected Investors: Eight Roads, Union Square Ventures (USV), Point Nine Capital, and Seedcamp, along with the founders of Indeed, Gumtree, and Wise
Draftwise is an AI-powered contract and negotiation platform founded in 2020, built specifically to help law firms work more efficiently by giving lawyers instant access to their firm's collective institutional knowledge and data. The company was co-founded by former Palantir engineering leaders James Ding and Emre Ozen, alongside Ozan Yalti, a Stanford Law graduate who practiced at top global firms including Clifford Chance.
The platform provides precedent-driven drafting, automated playbook creation, and AI-assisted contract review and redlining, integrating directly into Microsoft Word and Outlook to streamline the contract process from first draft through final negotiation. Top law firms across North America, Europe, and Australia use the platform, including Vault 10, Am Law 100, Magic Circle, and Seven Sisters firms. The platform states that it is SOC 2 Type II and ISO 27001 certified, GDPR compliant, and mirrors document management system permissions to maintain tight control over firm data.
Location: New York, New York
CEO: James Ding
Selected Investors: Index Ventures , Y Combinator, and Earlybird Digital East Ventures
Sirion is a leading AI-native Contract Lifecycle Management platform that allows enterprises to manage the entire lifecycle of contracts, from creation to compliance management. The company specializes in using advanced artificial intelligence, including specialized agents and Large Language Models, to automate contract authoring, negotiation, and risk management. Sirion has the ability to convert static, text-based contracts into actionable digital data, aiding legal, procurement, and sales teams in improving compliance and reducing risk.
Sirion serves major global enterprises such as BNY Mellon, Vodafone, IBM, Morgan Stanley, and DHL. In 2026, Sirion secured a majority investment from the private equity firm Haveli Investments, valuing the company at approximately $1 billion.
Location: Lehi, Utah (with 10 offices globally)
CEO: Ajay Agrawal
Selected Investors: Haveli Investments, Lumina, Sequoia Capital India (Peak XV Partners), Tiger Global Management
LexisNexis is one of the world’s leading providers of information, analytics, and AI-powered workflow solutions. Its flagship legal AI platform, Lexis+ with Protégé, helps legal professionals draft, summarize, analyze, and validate legal work faster and with greater confidence using AI grounded in trusted LexisNexis content.
The platform combines multiple AI models, plugins and skills, agentic workflows, and LexisNexis-developed legal AI capabilities within a single, intuitive, private, and secure environment. Shepard’s® Verify Trust Markers and citation capabilities help legal professionals validate work against authoritative sources.
All capabilities are grounded in LexisNexis’s repository of more than 200 billion legal documents and records, enabling users to generate final formatted, ready-for-review legal documents and spreadsheets linked to verifiable legal authority.
LexisNexis recently announced the integration of Anthropic’s Claude legal plugins and skills into Lexis+ with Protégé.
Westlaw is a premier legal research platform, offering comprehensive access to case law, statutes, regulations, administrative materials, secondary sources, and legal analytics across all U.S. jurisdictions and many international legal systems. Thomson Reuters has invested heavily in integrating AI capabilities throughout the Westlaw platform.
Westlaw's AI features include natural language search, key number system-enhanced research, KeyCite citation analysis, and the integration of generative AI through CoCounsel. The platform's legal analytics tools allow attorneys to analyze judicial and attorney behavior, help predict outcomes, and identify strategic insights from historical court data.
Bloomberg Law is a comprehensive legal research and analytics platform with deep integration of AI-powered features, including AI-assisted research summaries, brief analysis, draft document assistance, and transactional intelligence tools. Its AI capabilities are built upon Bloomberg's extensive database of legal content, court filings, regulatory materials, and business and financial information.
Bloomberg Law's Brief Analyzer uses AI to review draft legal briefs, identifying cited authorities, checking for negative treatment of relied-upon cases, and suggesting additional supporting precedents. The AI research features enable attorneys to receive synthesized answers to complex legal research questions with citations to Bloomberg Law's authoritative content.
Wolters Kluwer is a global information services company with a major legal division that provides research, compliance, workflow, and practice management tools to legal professionals worldwide. Its legal technology portfolio includes VitalLaw (legal research), ELM Solutions (enterprise legal management for corporate legal departments), and CT Corporation (registered agent and compliance services).
Wolters Kluwer has been integrating AI across its legal product suite, with AI-enhanced research tools, contract analytics capabilities, and intelligent workflow features embedded in platforms used by thousands of law firms and corporate legal departments globally. The company's AI strategy focuses on practical, domain-specific AI applications within its established product ecosystem.
ChatGPT, developed by OpenAI, is a large language model that has been extensively adopted by legal professionals for drafting, research, summarization, legal strategy brainstorming, and client communications. Its broad capabilities and high-quality language generation make it useful across a range of legal tasks, from drafting NDAs to analyzing complex regulatory frameworks.
Legal professionals use ChatGPT to draft initial contract language, summarize legal documents, explain complex concepts in plain language, prepare deposition outlines, and structure legal arguments. ChatGPT Enterprise offers enhanced privacy protections, larger context windows, and administrative controls appropriate for law firms and legal departments handling confidential client information.
Claude, developed by Anthropic, is a large language model with strong capabilities in legal research, contract drafting, document review, regulatory analysis, and legal writing. Claude has been adopted by a number of legal professionals and legal technology companies due to its sophisticated reasoning, nuanced language understanding, acknowledgment of uncertainty, and ability to handle very long documents.
Claude's extended context window allows it to process entire contracts, briefs, regulatory filings, or deposition transcripts in a single session, making it uniquely capable of large-document legal analysis. Legal technology companies have also built specialized legal applications on top of Claude's API, making it an important infrastructure component in the broader legal AI ecosystem.
Anthropic recently launched Claude for Legal, a dedicated AI offering for law firms and in-house legal teams, featuring more than 20 integrations with tools lawyers already rely on. It includes 12 role-specific plugins covering areas such as commercial contracts, employment, privacy, corporate, litigation, and AI governance. The platform also includes connectors that wire Claude directly into legal software lawyers often use—such as DocuSign, iManage, Ironclad, and Westlaw. Claude for Legal is designed to act as an AI layer across a lawyer's entire work environment, handling the time-consuming first-pass work of reviewing, drafting, and researching, while keeping a human attorney responsible for every final decision.
Location: San Francisco, California
CEO: Dario Amodei
Selected Investors: Amazon, Google, Salesforce Ventures, Sequoia, General Atlantic, Goldman Sachs, Blackstone, Hellman & Friedman, Apollo, Leonard Green
Google Gemini is Google's multimodal large language model, available to legal professionals through Google's consumer products, Google Workspace integration (Gemini for Workspace), and enterprise API access. Legal professionals use Gemini for drafting, document summarization, legal research assistance, and analysis of documents that include text, tables, charts, and images.
Gemini's deep integration with Google Workspace—including Google Docs, Gmail, and Google Drive—makes it practical for organizations already operating within the Google ecosystem, enabling AI-assisted drafting and review within familiar tools. Gemini can draw on a user's existing Google Drive documents to provide contextually relevant assistance.
Microsoft Copilot is an AI-powered assistant embedded within the Microsoft 365 suite—Word, Outlook, Teams, Excel, and PowerPoint. It uses large language models combined with an organization's private data, including emails, documents, and calendars, to generate personalized, actionable insights. Microsoft has increasingly taken a multi-model approach across its AI products.
For legal work, Copilot's primary strength is eliminating operational friction. Word with Copilot drafts and summarizes documents; Teams with Copilot generates meeting summaries and captures action items; and Excel with Copilot handles data analysis useful for damages calculations and billing. Legal departments also use it for contract review against standard terms, analyzing negotiation language, processing large document sets for relevant arguments, and supporting intellectual property monitoring.
Hebbia provides AI tools for analyzing large volumes of unstructured data, including legal and financial documents used in due diligence and investigations.
Location: New York, New York
CEO: George Sivulka
Selected Investors: Andreessen Horowitz, Index Ventures, GV, Peter Thiel
EvenUp is a legal AI platform purpose-built for personal injury law, focused on improving one of the most time-intensive parts of plaintiff-side practice: case valuation and demand letter generation. The platform uses artificial intelligence trained on large datasets of medical records, case histories, and settlement outcomes to produce structured, data-driven demand packages. These outputs include detailed narratives, medical chronologies, liability summaries, and calculated damages estimates, helping attorneys present more comprehensive and consistent claims to insurers.
Darrow is a litigation intelligence platform that uses AI to identify meritorious litigation opportunities, assess case strength, predict outcomes, and support litigation strategy. The platform scans large volumes of public data including court filings, regulatory enforcement actions, corporate disclosures, and news to identify patterns that suggest viable mass tort, class action, or commercial litigation opportunities.
Darrow's AI analyzes historical litigation data to estimate damages, assess the strength of potential legal claims, and predict judicial and settlement outcomes. It is primarily used by plaintiff-side law firms and litigation finance companies to identify and evaluate high-value litigation opportunities more efficiently than traditional intake and investigation methods.
Clio is a leading cloud-based legal practice management platform for small to mid-size law firms, used by hundreds of thousands of legal professionals worldwide. In 2025, Clio acquired vLex for $1 billion, an AI legal intelligence platform with comprehensive global research resources. Clio Duo is the platform's integrated AI assistant, which provides attorneys with intelligent support for drafting client communications, summarizing matter history, suggesting next actions, and analyzing billing and time data.
Unlike most legal AI tools that focus exclusively on substantive legal analysis, Clio Duo addresses the operational and administrative dimensions of running a legal practice. It helps attorneys draft professional emails, prepare client-facing summaries, identify matter patterns, and manage their practices more efficiently within the Clio ecosystem.
Filevine is a legal practice management platform designed for litigation-focused firms, offering tools for case management, document handling, and workflow automation.
The platform has incorporated AI features for document generation, summarization, and process optimization, aiming to improve efficiency in case-based legal work.
Location: Salt Lake City, Utah
CEO: Ryan Anderson
Selected Investors: Insight Partners, Accel, Halo Fund
LegalZoom is one of the most recognized consumer and small business legal services platforms in the United States, offering self-service legal document preparation, business formation services, registered agent services, legal plan subscriptions, and on-demand access to attorneys. The platform serves millions of consumers and small businesses who need legal services but seek alternatives to traditional law firm representation.
Location: Mountain View, California; Operations at Glendale, California
Rocket Lawyer is an online legal services platform offering document creation, attorney access, business formation, and legal plan subscriptions to consumers, small businesses, and legal professionals. The platform provides AI-assisted document generation tools that guide users through legal form completion, along with access to a network of attorneys for consultation and review. The company recently announced a new AI-native Rocket Copilot platform designed to help SMBs and consumers navigate legal issues more affordably and efficiently.
Clerky is a legal technology company focused on startup legal document preparation, offering automated generation of incorporation documents, financing documents, employee agreements, and other legal paperwork commonly needed by technology startups and venture-backed companies. The platform is designed to help early-stage companies complete standard legal processes with minimal attorney involvement.
Clerky has built expertise in startup-specific legal requirements, particularly in Delaware corporate law and Silicon Valley standard financing documents (including Y Combinator SAFEs and standard venture financing agreements). Its document generation tools guide founders through complex legal processes step by step, reducing errors and ensuring completeness.
General Legal is an AI-native law firm founded in 2025 and backed by Y Combinator, built to solve commercial legal work for founders and fast-growing companies. The firm was co-founded by Ryan Walker, a former CTO of Casetext (acquired by Thomson Reuters in 2023), and Javed Qadruddin and J.P. Mohler, both Harvard Law graduates who practiced at elite firms including Fenwick & West, Cooley, and WilmerHale. The company's premise is that most startup founders are overpaying for slow, opaque legal work—and General Legal aims to fix that by combining experienced BigLaw-caliber attorneys with AI-powered workflows.
General Legal operates as a law firm that delivers contract review, drafting, and negotiation to growth-stage companies at flat-fee pricing—typically around $500 for most contracts—with turnarounds measured in hours rather than the days or weeks typical of traditional firms. Communication happens through a private Slack channel with the client's dedicated attorney, and AI tools are used to control quality and speed, while a human lawyer remains responsible for every deliverable.
Location: San Francisco, California
CEO: Ryan Walker
Selected Investors: Y Combinator, SUSA Ventures, BoxGroup, AME Cloud Ventures, and Audacious
Crosby is an AI-first law firm that reviews commercial contracts in a few hours, including non–disclosure agreements, sales contracts, service agreements, and data processing agreements. Crosby’s AI agents collaborate with in-house lawyers to speed up review and suggest changes to commercial contracts. Unlike traditional law firms, Crosby charges a per page amount in the contract, not by a billable hour rate.
Location: New York, New York
CEO: Ryan Daniels
Selected Investors: Index, Lux, Sequoia, Bain Capital, Elad Gil
Of course, there are many additional companies in the growing AI-enabled legal technology space, and I couldn’t list all of them here. I will update this article periodically with new companies and fix any mistakes/hallucinations. If you have suggestions, contact me through LinkedIn.
In the meantime, here are some sites that provide valuable wisdom and updates:
Artificial intelligence is reshaping the legal industry, enabling new levels of efficiency while introducing important considerations around accuracy, oversight, and risk. The companies profiled here illustrate the breadth of innovation across the legal AI ecosystem.
While the technology continues to evolve, its most effective use will likely remain in augmenting—not replacing—legal professionals, combining computational capability with human judgment.
Michael “The Grinder” Mizrachi is a poker god—a true legend whose career has spanned decades, multiple game formats, and historic triumphs.He accomplished the almost impossible in the 2025 World Series of Poker (WSOP): he won the prestigious Poker Player Championship for $1.3 million and then won the Main Event for $10 million.Mizrachi's Unprecedented 2025 Run1. Fourth $50K Poker Players Championship (PPC)In June 2025, Mizrachi made WSOP history by winning the $50,000 buy-in PPC for the fourth t
Michael “The Grinder” Mizrachi is a poker god—a true legend whose career has spanned decades, multiple game formats, and historic triumphs.
He accomplished the almost impossible in the 2025 World Series of Poker (WSOP): he won the prestigious Poker Player Championship for $1.3 million and then won the Main Event for $10 million.
Mizrachi's Unprecedented 2025 Run
1. Fourth $50K Poker Players Championship (PPC)
In June 2025, Mizrachi made WSOP history by winning the $50,000 buy-in PPC for the fourth time, topping a highly elite field and earning $1,331,322. This win not only broke his own record, but reaffirmed his status as the premier mixed‑game poker player in the world.
The PPC is widely considered the toughest event in poker—testing every major poker discipline. Mizrachi navigated rounds of Hold 'Em, Omaha, Stud, Razz, and more. The final table’s energy was electric, with spectators and competitors alike recognizing the rarity of a fourth win in this event.
2. Main Event Glory
July 2025 saw Mizrachi enter the $10,000 buy-in Main Event aiming for a career-defining victory. A massive field of 9,735 players competed for a $90.5 million prize pool. After surviving a short‑stack scuffle near Day 8—famously getting down to just crumbs in chips—he turned a pivotal double-up to vault himself into contention.
At the final table, he carried massive momentum. On Day 10, he eliminated the third- and fourth-place finishers in the first two hands, earning the victory in just 20 hands—one of the swiftest Main Event endings in history. His final victory (a flush against two pairs) earned him $10 million and his first Main Event bracelet. (A “bracelet” is the equivalent of winning a gold medal at the Olympics.)
3. Hall of Fame Induction
Immediately following his Main Event victory, Mizrachi received a rare and spontaneous Poker Hall of Fame induction, bypassing the usual waitlist of years. The unanimous vote came as his peers—including Phil Ivey, Brian Rast, Daniel Negreanu, and Phil Hellmuth—hailed his historic accomplishment of winning both the PPC and Main Event in the same year.
WSOP CEO Ty Stewart praised him, calling it “the most impressive feat in poker history."
Who Am I to Claim to Have Beaten Mizrachi at the World Series of Poker?
I am an amateur poker player. I have done a few other things noted in my bio below. My main poker accomplishment has been being the lead author of Poker for Dummies, which has outsold almost every poker book ever written. Timing is everything—I wrote that book right before the poker boom started.
Through luck, I have made four final tables at some of the World Series of Poker events, highlighting the important poker phrase that "it's better to be lucky than good."
So How Did I Beat Mizrachi at the World Series of Poker?
Ok, stay with me here. It's 2008 and I have entered the Pot Limit Omaha Championship at the World Series of Poker. I was doing terribly at Hold 'Em events, so I decided to try my luck at Omaha. It's a much trickier game than Hold 'Em (you get 4 starting cards in Omaha versus the 2 you get in Hold ‘Em and the strategy is more complicated). There are world-class experts in Omaha, like Noah Schwartz. I’m a less-than-world-class novice at the game.
But somehow, miracle upon miracle happened and I made the final table of that PLO Championship. And who was at the final table with me? Yes, Michael Mizrachi.
I wanted to avoid being in a hand with Michael—I knew his reputation and was not eager to play against him. But I found myself in a hand against him. He ended up with three 9's but I made a flush. A minor victory but a victory nevertheless.
Even after so many years, I am sure that hand still stings for Michael.
Now some of you may quibble and nitpick that beating Michael in one hand 17 years ago isn’t really “beating” him.
To that, I say…pshawww. It’s my delusional fantasy and I’m sticking with it.
If Michael wants to redeem himself, I challenge him to a winner-take-all heads-up Hold 'Em match. Mano a mano. Maybe Wynn, MGM, or Caesar’s can sponsor the event and put up the prize pool (hint, hint).
If he wins, I will also throw in my $12.99 poker bracelet that says “Poker Champion” on it that good friends gave to me.
If Michael beats me in that heads-up match, I will also admit that he is a slightly better poker player.
PokerChampion.com
I bought the domain name www.PokerChampion.com many years ago, hoping I would be able to use it someday. It looks like I will have to wait until next year's World Series of Poker.
But maybe Michael will want to buy it from me? It's for sale at slightly under $10 million. But he should hurry up and contact me, as I expect that other poker legends like Phil Hellmuth, Daniel Negreanu, and Phil Ivey will want it as well.
And congrats to Michael! What an unbelievable accomplishment!
By Richard D. Harroch and David A. LipkinThe legal landscape, particularly in the area of mergers and acquisitions, is undergoing a significant transformation driven by artificial intelligence (AI). What once often required a large team of analysts, lawyers, and advisors working around the clock can now be accomplished more efficiently and accurately with AI-powered tools. From initial valuation assessments to final contract negotiations, AI is reshaping many phases of the M&A lifecycle, ena
The legal landscape, particularly in the area of mergers and acquisitions, is undergoing a significant transformation driven by artificial intelligence (AI). What once often required a large team of analysts, lawyers, and advisors working around the clock can now be accomplished more efficiently and accurately with AI-powered tools. From initial valuation assessments to final contract negotiations, AI is reshaping many phases of the M&A lifecycle, enabling faster transactions, better decision-making, and more favorable outcomes.
Of course the AI tools are available to both buyers and sellers, so it remains to be seen which party will ultimately benefit the most. This article addresses primarily the use of AI tools on the seller side of private transactions, but AI will soon be in pervasive use on all sides of both private and public transactions.
The integration of AI into M&A processes represents more than just incremental improvement—it's a fundamental shift in how deals are sourced, evaluated, negotiated, documented, and closed. Traditional M&A transactions have always been resource-intensive, requiring extensive manual review of financial documents, legal contracts, due diligence materials, and market research; manual development of the purchase agreement and ancillary documents; and a lengthy and laborious process of negotiating, editing and proofreading them over weeks or months.
The complexity of the tasks and the volume of the information involved in modern M&A deals has only increased, making human-only approaches increasingly impractical. AI tools can process vast amounts of data in seconds, identify patterns and risks that humans might miss, and provide insights that dramatically improve deal quality and execution speed.
For M&A professionals, understanding how to leverage AI effectively has become essential to remaining competitive. Whether a deal participant is a business owner preparing to sell, an investment banker structuring deals, a serial acquirer, or legal counsel negotiating agreements, AI tools are now available to enhance the transaction process.
This article explores critical stages of M&A transactions and examines how AI is now available for deployment at each stage, along with specific tools that are transforming the industry. Of course, we used AI for research and editorial assistance in writing this article.
A word of caution: no matter how advanced AI-powered tools become, it will always remain important for humans to ultimately evaluate the output from such tools to ensure that it makes sense and does not have obvious errors.
1. Analyzing Whether the Seller Is Ready for an M&A Transaction
Before embarking on an M&A process, a seller must honestly assess whether its business is truly ready for a transaction. This assessment involves evaluating financial performance, organizational structure, customer concentration, legal compliance, intellectual property protection, and dozens of other business attributes that will be scrutinized during due diligence by the buyer and its legal and financial advisers, using their own AI tools.
AI tools can significantly accelerate and improve this readiness assessment. For example, Claude, Anthropic's AI assistant with advanced analytical capabilities, can review financial statements, organizational charts, customer lists, and contract portfolios to help a seller identify potential red flags that might concern buyers. By uploading key business documents to a secure site that can be evaluated by AI in a secure and confidential setting, sellers can receive comprehensive feedback on areas requiring attention before going to market.
ChatGPT and other large language models can analyze business operations and provide structured readiness checklists tailored to specific industries. These tools can review descriptions of business operations and compare them against typical buyer requirements, highlighting gaps that should be addressed. For legal readiness, tools like Harvey and Legora, and legal information services like Stella Legal, can employ a multitude of AI processes to scan corporate records, board minutes, and governance documents to identify compliance issues, missing documentation, or organizational irregularities that could derail a transaction.
More specialized AI tools can analyze financial data to identify unusual trends or inconsistencies that sophisticated buyers will discover, particularly now that they too will be using similar sophisticated tools. By catching these issues early, sellers can address them proactively before being forced into uncomfortable diligence discussions or demands for price reductions during negotiations, or even risking termination of the deal. The key advantage of using AI tools at this stage is the ability of a seller to see its business through a buyer's eyes before any actual buyer involvement, allowing it to strengthen weak points and maximize value.
2. Determining a Range of Valuation for the Seller
Accurate valuation is fundamental to successful M&A transactions. Overpricing scares away serious buyers, while underpricing leaves money on the table. Traditional valuation methods include analyzing comparable transactions, applying industry multiples, conducting discounted cash flow analyses, and adjusting for company-specific factors.
AI tools have transformed valuation analysis by providing access to vastly larger datasets and more sophisticated modeling capabilities. Platforms like PitchBook and CapIQ, increasingly enhanced with AI features, can identify comparable transactions across multiple dimensions—industry, size, geography, growth rate, and profitability. AI-powered algorithms can weight these comparables based on relevance and generate valuation ranges that reflect current market conditions.
The advanced data analysis capabilities of AI tools allow users to upload financial statements and receive detailed valuation assessments using multiple methodologies. But users should be mindful of data privacy and attorney-client privilege issues. By providing historical financials and business descriptions, sellers can generate comprehensive valuation reports that consider revenue multiples, EBITDA multiples, precedent transactions, and discounted cash flow projections. The AI tools can also identify which valuation metrics are most commonly used in specific industries and adjust valuations accordingly.
Machine learning models can also analyze how specific business characteristics impact valuation. For example, AI tools can quantify the valuation premium associated with high recurring revenue percentages, strong customer retention rates, or proprietary technology. These insights can help sellers understand which value drivers matter most to buyers interested in making acquisitions in their industry and focus their preparation accordingly. These tools can also review previous M&A transactions in specific sectors to identify valuation trends and patterns that inform realistic price expectations.
3. Identifying Logical Potential Buyers
Finding the right buyers—those who will see maximum strategic value in an acquisition and pay accordingly—is crucial to achieving optimal M&A outcomes. The universe of potential buyers includes strategic acquirers, private equity firms, family offices, and individual investors, each with different investment criteria and valuation approaches.
AI-powered market intelligence platforms can identify potential buyers by analyzing acquisition histories, stated strategic priorities, portfolio gaps, and geographic expansion plans. These tools scan press releases, SEC filings, earnings calls, and industry publications to build comprehensive profiles of active acquirers in specific industry sectors. Machine learning algorithms can predict which companies are most likely to be interested in a particular acquisition target based on their historical deal behavior and practices, as well as their current strategic positioning.
AI tools can also assist in researching potential buyers by analyzing publicly available information about companies and investors. By describing its business and its key characteristics, a seller can receive curated lists of likely acquirers along with reasoning about why each would find the company attractive. This analysis can include identifying specific synergies, competitive advantages the buyer would gain, and strategic rationales that could justify premium valuations.
LinkedIn and other professional networks, increasingly powered by AI-powered recommendation algorithms, can help identify relevant corporate development executives and private equity professionals who focus on the industry in which a seller operates. AI tools can analyze these contacts' backgrounds, recent activities, acquisition history, and stated current acquisition focus to prioritize outreach. CRM platforms with AI capabilities can even draft personalized initial outreach messages that reference specific reasons why a particular seller would be attractive to each potential buyer, significantly improving response rates compared to generic mass emails.
4. How to Use AI to Create a Pitch Deck for an M&A Seller
When a company prepares to sell, the M&A pitch deck—sometimes called a "teaser"—is one of the most critical documents in the process. It needs to tell a compelling story and give prospective buyers enough confidence to move forward. AI tools have made it much faster to build a document that is more polished than ever. Even if a seller only uses the AI tools to develop a first draft, it will save an immeasurable amount of time and reduce the risk that something critical has been omitted or misstated.
What a Seller's M&A Pitch Deck Typically Includes
A well-structured M&A pitch deck for a seller generally covers the following sections:
Executive Summary: A concise overview of the business, the opportunity, and the traction the company has achieved. This is often the first thing buyers read and must immediately capture attention.
Company Overview: History, mission, business model, products or services, and key competitive advantages.
Market Opportunity: The size and growth trajectory of the addressable market, along with the company's positioning within it.
Financial Performance: Historical revenue, EBITDA, gross margins, and growth trends, typically covering three to five years. Sellers often also include forward projections.
Customer and Revenue Analysis: Customer concentration, retention rates, recurring revenue breakdowns, and key contracts.
Operations and Team: Organizational structure, key management bios, and operational infrastructure that will facilitate the transition and maximize the likelihood of a smooth integration process.
Technology: Description of the company's key technology.
Intellectual Property: Description of key patents, trademarks, copyrights, and other intellectual property
Competitive Landscape: A discussion of the company's principal competitors and the advantages the company has over those competitors.
Growth Opportunities: Strategic levers a buyer could pull post-acquisition, such as geographic expansion, new product lines, or operational efficiencies.
AI Tools That Can Help Build the M&A Pitch Deck
AI tools can accelerate the creation process. For example, ChatGPT and Claude are excellent for drafting narrative sections, refining executive summaries, and generating compelling language around financial performance. Beautiful.ai, Genspark.ai, and Gamma.app use AI to design slides with professional layouts, saving hours of formatting work. For financial modeling and data visualization, Microsoft Copilot in Excel can help clean up and chart financial data quickly. The capabilities of these and other AI-powered tools are rapidly expanding.
Where to Find Sample M&A Pitch Decks
Before building a pitch deck, reviewing examples is invaluable. Strong resources include DocSend (which hosts real startup and M&A decks), SlideShare (searchable by deal type), Axial.net (focused specifically on middle-market M&A), and Pitchbook's blog, which regularly publishes deal decks.
With the right AI tools and a clear understanding of what buyers expect, a seller can produce a pitch deck that stands out in a competitive process
5. Identifying Investment Bankers or M&A Advisors
Selecting the right M&A advisor can dramatically improve the prospect of a successful transaction outcome. The best advisors bring industry expertise, buyer relationships, negotiation skills, and process management capabilities that justify their fees many times over. However, the M&A advisory landscape is crowded, and identifying advisors with relevant experience and strong track records requires careful research.
AI tools can streamline the advisor selection process by analyzing deal databases to identify which investment banks and advisory firms have completed transactions in the seller’s industry, size range, and geography. Platforms like Refinitiv and Bloomberg, enhanced with AI search capabilities, allow users to filter transactions by multiple criteria and identify which advisors consistently work on relevant deals.
AI tools can help a seller evaluate potential advisors by analyzing their websites, deal announcements, and published thought leadership to assess their industry expertise and transaction experience, and by developing comparative analyses highlighting each firm's strengths, specializations, and potential fit for a specific transaction. Of course these tools are also adept at identifying potential advisors of which a seller was not previously aware.
AI tools can also help prepare questions to ask during advisor interviews, ensuring a seller gathers the information needed to make an informed selection. For example, key questions to ask potential advisors may include:
How many M&A deals has the team that will be involved in this transaction done?
Can you provide us with a list of potential buyers and the contacts you have with those potential buyers?
How would you position our company to attract maximum value?
What is the likely range of valuation for the company? Why?
How long do you anticipate the process taking?
How do you calculate your fees?
Would you target a narrow list of buyers or do a broad outreach?
What particular expertise do you have in our market sector?
What suggestions would you have to make our M&A process faster and smoother?
Harvey, Legora, and similar legal AI tools can also review engagement letters from multiple advisors, comparing fee structures, expense provisions, indemnification obligations, tail periods, and other terms, and potentially suggesting clauses (such as a key person provision) that might protect a seller if its key advisor switches firms in the middle of a process. This analysis helps a seller ensure that it understands exactly what it is agreeing to and can negotiate more effectively.
Online reviews and reputation analysis tools powered by AI can aggregate feedback about various M&A advisors from multiple sources, providing insights into their responsiveness, effectiveness, and client satisfaction. While personal references remain important, AI-powered reputation analysis can supplement direct feedback and help identify advisors worth pursuing further.
6. The Use of AI in Drafting and Negotiating NDAs for Mergers and Acquisitions
The non-disclosure agreement (NDA) is an important document in M&A transactions. Before a seller shares financials, customer lists, or proprietary technology with a prospective buyer, the parties should agree on the scope of confidentiality, permitted uses of disclosed information, employee non-solicitation restrictions, and more.
What was once a straightforward preliminary step has grown increasingly complex, with sophisticated counterparties negotiating aggressively over definitions, carve-outs, and remedies. AI tools are now changing how NDAs are drafted, reviewed, and negotiated in M&A practice.
These tools can generate a first-draft NDA within seconds by drawing on vast training libraries of precedent agreements and current market standards. This first draft can be pro-buyer oriented or pro-seller oriented, or “middle of the road,” if that is called for, and one-way or two-way with respect to the scope of the covenants.
Rather than starting from a stale form, counsel can receive a jurisdiction-specific, deal-specific draft calibrated to the nature of the transaction. The AI tools can factor in the sensitivity of the information to be shared and applicable law to recommend appropriate definitions of confidential information, exclusions for publicly available information, and disclosure permissions for advisors, accountants, lenders, and regulators.
On the review side, AI tools can accelerate the redline process. Machine learning algorithms can compare a buyer’s proposed NDA against market standards and the seller’s preferred positions, flagging deviations in key provisions such as the definition of confidential information, the duration of confidentiality obligations, the scope of any standstill, and remedies for breach.
Rather than spending hours analyzing a buyer’s markup, counsel can receive a prioritized issue list identifying high-risk departures from standard terms alongside AI-generated suggested language to resolve each point. This enables attorneys to focus their expertise on genuinely contested issues rather than routine analysis of gaps between the two forms.
AI tools also enhance negotiation strategy by providing data-driven market intelligence. By analyzing many executed NDAs across comparable transactions, AI tools can suggest provisions (such as employee nonsolicitation provisions) that may be appropriate in certain contexts but not others, tell counsel what percentage and type of deals include such provisions, make intelligent recommendations with respect to how disputes are to be resolved, and guide the analysis of what residuals clauses are standard in technology sector deals.
Perhaps most valuably, AI reduces the risk of overlooking critical provisions in NDAs, the absence of which could create long-term risks. NDA breaches in M&A—particularly unauthorized disclosure of a seller’s proprietary technology or premature announcement of a deal—can result in significant damages and reputational harm. AI quality-control tools cross-check every draft against a checklist of essential provisions, ensuring that no clause is inadvertently omitted and that definitions are internally consistent.
For serial acquirers managing multiple simultaneous processes, AI makes it possible to maintain rigorous standards across every NDA without proportionally scaling legal costs.
Streamline AI, Legora, Luminance, and Harvey are particularly helpful in drafting and negotiating NDAs. M&A deal consultants such as Stella Legal deploy a number of these tools, rather than leaving it up to the client to navigate among individual tools themselves.
7. How AI Tools Can Be Used to Develop Disclosure Schedules for M&A Transactions
Disclosure schedules are an integral part of any M&A transaction. The disclosure schedules contain information required by the acquisition agreement—typically including lists of important contracts, intellectual property, employee information, and other material matters, as well as exceptions or qualifications to the detailed representations and warranties of the seller contained in the acquisition agreement.
An incorrect or incomplete disclosure schedule could result in a breach of the acquisition agreement and potentially significant liability to the seller or its stockholders. In contrast, a well-drafted disclosure schedule will provide substantial protection against post-closing allegations that the seller breached its representations and warranties.
Because poorly prepared disclosure schedules increase the risk of significant post-closing liability, it is important that they be compiled carefully and thoroughly. Disclosure schedules prepared at the last minute are likely to be incomplete or inadequate, creating problems to closing a deal or injecting unnecessary risk into the transaction.
Typically, the disclosure schedule process is undertaken by employees of the seller together with inside and outside M&A legal counsel. But the disclosure schedules can require a significant amount of time to assemble, and the initial drafting should be undertaken early on. It is not uncommon for disclosure schedules to go through a dozen or more drafts and negotiations with the buyer’s counsel.
The traditional process demands hundreds of attorney and employee hours and carries substantial risk—both from inadvertent omissions that trigger indemnification claims and from over-disclosure that provides buyers with renegotiation leverage. AI tools are changing this process by automating document review, ensuring consistency, and reducing both cost and liability exposure.
In contrast, AI-powered document review platforms can analyze thousands of contracts and corporate records in a fraction of the time required for manual review. Natural language processing algorithms can identify key provisions, extract material terms, flag unusual clauses, and automatically categorize documents by type and subject matter.
AI tools can also maintain consistency between the disclosure schedule and the underlying purchase agreement to which it relates, which will itself be undergoing multiple rounds of negotiations and revisions.
When preparing material contracts schedules, AI tools can scan entire contract repositories to identify agreements meeting specific materiality thresholds—such as annual payments exceeding defined amounts. The system then can extract critical metadata including party names, effective dates, payment terms, and material obligations, automatically populating structured schedules that would otherwise require days of manual compilation.
One of AI's most valuable capabilities is intelligent exception mapping. A single contract might contain provisions requiring disclosure across multiple schedules—for instance, customer agreements with indemnification provisions, liability limitations, and intellectual property warranties might need disclosure on litigation, obligations, and IP schedules respectively. AI systems can map documents to appropriate disclosure sections by analyzing both purchase agreement language and the substance of disclosed items, reducing the risk of incorrect placement or missing cross-disclosure.
For litigation and regulatory compliance, AI tools can conduct systematic searches of public records, court databases, and regulatory filings to identify matters requiring disclosure.
Intellectual property schedules can benefit significantly from AI's ability to interface with patent and trademark databases. The technology can extract patent numbers, filing dates, and legal status while analyzing claim language to assess scope and identify potential prior art affecting validity. For trademarks, AI tools can conduct comprehensive conflict searches and verify registration status across jurisdictions. AI tools can also identify gaps in IP protection by comparing product offerings against registered rights, and can review codebases for open-source licenses that impose restrictions requiring disclosure.
Beyond initial drafting, AI tools can provide crucial quality control by cross-checking schedules for completeness and consistency. Algorithms verify that disclosed information matches underlying records and identify inconsistencies across schedules—for example, ensuring contracts on material contracts schedules have corresponding related party disclosures when applicable.
Cost Savings. The financial impact is substantial. Traditional disclosure schedule preparation can consume large amounts of legal fees in middle-market transactions. AI tools can reduce these costs significantly while improving quality and comprehensiveness.
Virtual data rooms have become standard in M&A transactions, serving as secure repositories for a seller’s due diligence documents. However, organizing and populating data rooms—traditionally involving hundreds of hours of document collection, review, and indexing—remains one of the most time-consuming aspects of deal preparation and execution.
AI-powered document management systems can dramatically accelerate data room preparation. These tools can automatically classify documents by category, extract key information, identify missing items, and flag potential issues requiring attention. Platforms like Datasite, Intralinks, and DealVDR now incorporate AI capabilities that suggest appropriate folder structures based on industry and transaction type, then automatically organize uploaded documents into the correct locations.
AI tools can help create comprehensive data room indices and checklists tailored to a specific transaction. By describing its business and transaction type, a seller can receive detailed lists of documents typically requested during due diligence, organized by category with explanations of why each document is important.
AI tools can review documents before they have been uploaded to data rooms, identifying privileged information that should be redacted, spotting inconsistencies between related documents, and flagging potential problems that might concern buyers. This pre-screening can prevent embarrassing discoveries during due diligence and allows sellers to prepare explanations for potentially problematic information before buyers raise concerns.
AI-powered optical character recognition (OCR) and document processing tools can convert paper documents and image files into searchable PDFs, extract data from scanned contracts and financial records, and create searchable databases of key terms across thousands of documents. This technology makes historical records accessible and useful rather than merely archived, significantly improving due diligence efficiency for both sellers and buyers.
9. Drafting and Negotiating a Letter of Intent
Letters of intent (LOIs) establish the basic framework for M&A transactions, including purchase price, deal structure, key terms, exclusivity periods, and conditions to closing. While not traditionally fully legally binding, LOIs set expectations and momentum that can strongly influence final outcomes.
AI tools can assist in drafting LOIs by providing relevant templates and suggesting terms based on market standards for similar transactions. They can generate initial LOI drafts based on deal parameters provided, incorporating provisions appropriate to the seller’s industry and transaction type. These tools can also explain each provision's purpose and implications.
These tools can review proposed LOIs from potential buyers, identifying unusual or unfavorable terms, and suggesting alternative language. Business advisors such as Stella Legal can also provide coordinated review across multiple AI tools. These services and tools can compare proposed terms against market standards, highlighting provisions that fall outside typical ranges. For example, if a buyer proposes an unusually long exclusivity period or unfavorable working capital adjustment, AI tools can flag these as negotiation points and suggest more balanced alternatives.
AI tools that are used more generally can now be customized for use in the M&A process. For example, that legal plugin for Claude enhances its ability to analyze complex legal provisions in LOIs, identifying potential ambiguities, conflicts between provisions, or missing terms that could cause problems later. By uploading buyer-proposed LOIs, sellers can receive detailed analyses of strengths, weaknesses, and recommended negotiation positions before responding.
10. Drafting and Negotiating M&A Purchase Agreements
The definitive purchase agreement represents the culmination of M&A negotiations, documenting all transaction terms, representations and warranties, indemnification provisions, closing conditions, and post-closing obligations. These complex documents, often exceeding 100 pages in length, including extensive exhibits and schedules, require sophisticated legal drafting and careful negotiation.
AI-powered tools are transforming the process of drafting and analyzing M&A purchase agreements. They can generate initial agreement drafts based on transaction parameters, incorporate specific deal terms, and adapt standard provisions to unique circumstances. More importantly, they can review draft agreements from opposing counsel, identifying unusual provisions, comparing terms against market standards, and suggesting specific language changes to better protect clients' interests.
M&A consultants such as Stella Legal can provide contract analysis capabilities through their partnerships with AI platforms (such as Sirion and Luminance). As an integration layer across AI tools, Stella Legal and other consultants can extract key terms from lengthy agreements, create summary charts comparing different draft versions, and highlight where negotiated changes have been accepted or rejected. This tracking capability is invaluable during multi-round negotiations involving complex agreements with numerous disputed provisions.
AI tools such as Claude's legal plugin enhance the contract review capabilities of a seller or buyer, allowing detailed analysis of representations and warranties, indemnification baskets and caps, material adverse change definitions, and closing conditions. By uploading agreement drafts, parties can receive explanations of complex provisions in plain language, analysis of how specific terms allocate risk between buyer and seller, and identification of potentially problematic language that could cause disputes later.
AI-powered redlining tools can automatically identify changes between agreement versions, generate comparison documents, and even suggest compromise language when parties are deadlocked on specific provisions. These tools accelerate the negotiation process by eliminating confusion about what has changed and focusing discussions on substantive issues rather than tracking edits.
11. Protecting and Rewarding Management and Employees in an M&A Transaction
AI tools can be helpful in suggesting steps to reward and protect the CEO, management team, and employees in an M&A transaction. Such suggestions could include:
Success bonuses and “carveouts” for the management team
Enhanced severance protection in the event of termination of employment without cause
Accelerated stock option vesting on close of the deal or on a “double-trigger” basis for a period following closing
Continuation of Indemnification agreements and charter protections for officers, and the procurement of the proper D&O tail policies
Employee hiring terms with the buyer
Analysis of proposed employment agreements for the management team by the buyer (including with respect to retention bonuses, non-competes, non-solicits, etc.)
AI tools can be useful in preparing the many corporate and shareholder documents necessary in an M&A deal, including:
Board of Director written consents or meeting minutes
Stockholder written consents or meeting minutes
Stockholder Proxy or Information Statements
Letters of transmittal
Secretary of State filings
Certificates of Merger
Officer certificates
Director resignations
Stockholder voting or support agreements
13. Closing the M&A Deal
The closing process involves satisfying all conditions precedent, obtaining required approvals, exchanging final documents, and transferring consideration. While conceptually straightforward, closings involve intense coordination among multiple parties and careful attention to detail to ensure nothing is missed at the finish line.
AI-powered closing management platforms can create comprehensive closing checklists based on transaction agreements, track completion status for each item, send automated reminders about approaching deadlines, and flag potential delays before they become critical problems. These systems can help avoid something falling through the cracks during the hectic final weeks of a transaction.
AI tools can assist in preparing closing documents by generating initial drafts of closing deliverables. By providing relevant information about the company and the transaction, a seller can quickly produce properly formatted documents that require review but eliminate the task of drafting from scratch. This capability is particularly valuable for smaller transactions where parties may not have extensive in-house resources.
These tools can review closing documents to ensure consistency with the definitive purchase agreement, verify that required deliverables have been prepared, and check that conditions precedent have been satisfied. This verification can prevent embarrassing last-minute discoveries that conditions weren't actually met or required documents are missing.
Document execution platforms like DocuSign and Adobe Sign, enhanced with AI capabilities, can automatically route signature pages to appropriate signatories, track signing status, send reminders about pending signatures, and compile fully executed documents. These platforms eliminate the logistical challenges of coordinating signatures across multiple parties, time zones, and jurisdictions, ensuring closings aren't delayed by administrative issues.
14. Post-Closing Integration and Compliance
While often overlooked in discussions of the use of AI in M&A, post-closing activities including integration planning, earnout tracking, purchase price adjustment provisions, indemnification claim management, and compliance with transaction covenants represent critical areas where AI tools can add significant value.
AI-powered integration management tools can help acquirers plan and execute post-closing integration by identifying synergies, tracking integration milestones, monitoring combined financial performance, and flagging integration risks requiring attention. These tools can analyze data from both legacy organizations to identify operational inefficiencies, redundant systems, and quick-win opportunities for cost reduction or revenue enhancement.
For transactions with milestones or other earnout provisions, AI tools can monitor financial performance against earnout targets, calculate earnout payments based on agreement formulas, and identify potential disputes before they escalate. Machine learning algorithms can even predict whether earnout targets are likely to be achieved based on current performance trends, allowing parties to proactively address problems.
Harvey, Legora, and similar tools can monitor compliance with post-closing covenants, track survival periods for representations and warranties, manage indemnification claims, and organize documentation supporting or defending against claims. This capability is particularly valuable for sellers who need to track multiple obligations across extended time periods.
These tools can also assist in preparing regular reports required under transaction agreements, analyzing whether specific events trigger notification obligations, and drafting required communications to transaction parties. By maintaining a clear record of post-closing compliance, parties can avoid disputes and demonstrate good faith performance of their obligations.
15. How AI Tools Can Be Improved for Mergers and Acquisitions
Despite the progress AI tools have made in transforming M&A processes, significant opportunities remain for improvement. Current AI tools, while powerful, still have limitations that prevent them from reaching their full potential in facilitating transactions. Understanding these limitations and the pathways to improvement can help shape the development of next-generation M&A AI solutions. Opportunities for improvement include the following:
Most current AI tools are generalists trained on broad datasets that span multiple industries and transaction types, and do not have industry-specific training and specialization in all areas. While this provides versatility, it often means the AI tools lack the deep industry expertise that human M&A advisors develop over decades of focused work.
Integration between different AI tools represents another significant opportunity for improvement. Currently, M&A professionals often use separate AI tools for legal review, financial analysis, buyer identification, document management, virtual data rooms, and other functions. These disconnected systems require manual data transfer, create inefficiencies, and prevent holistic analysis that considers all transaction aspects simultaneously. Future AI platforms should offer seamless integration across all M&A functions, allowing data to flow automatically between modules and enabling comprehensive analysis that considers legal, financial, strategic, and operational factors together.
It can be advantageous to use a service such as Stella Legal that has access and subscriptions to all the important AI legal tools, and can act as the implementor/manager of those tools for a specific deal.
Real-time market intelligence and predictive capabilities need substantial enhancement. While current AI tools can analyze historical transactions and identify patterns, they struggle to predict future market conditions, buyer appetite, or optimal timing for transactions. Advanced machine learning models should incorporate real-time data feeds from financial markets, M&A announcements, regulatory changes, economic indicators, and industry trends to provide dynamic recommendations about when to launch sale processes, which buyers are most active, and how market conditions might affect achievable valuations.
The abilityto handle complex, multi-jurisdictional transactionsrequires improvement. Current AI tools generally work well for straightforward domestic transactions but struggle with cross-border deals involving multiple regulatory regimes, tax jurisdictions, currency considerations, and cultural factors.
M&A lawyers have built up expertise by having done hundreds of deals. The authors of this article alone have participated in over 500 M&A transactions and have acquired expertise that incorporates judgment, knowledge of the legal risks, and understanding of deal dynamics. Today’s AI tools do not fully reflect this type of expertise and the judgment it brings. By infusing this type of expertise into the capabilities of AI tools, these tools will be continuously improved over time.
The explanation and transparency of AI-powered recommendations need improvement to build user trust and facilitate adoption. Many current AI systems operate as "black boxes" that provide conclusions without adequate explanation of their reasoning. M&A professionals, particularly lawyers and advisors with fiduciary duties to clients, are understandably reluctant to rely on recommendations they cannot explain or validate. Enhanced AI systems should provide clear, detailed explanations of how they reached conclusions, cite specific data sources or precedents supporting their recommendations, and allow users to interrogate the reasoning behind suggestions. This transparency would enable professionals to trust AI insights while maintaining the ability to exercise independent judgment and explain recommendations to clients.
Cybersecurity and data privacy protections can be enhanced as AI systems handle increasingly sensitive M&A information. Current data room and AI analysis platforms maintain strong security protocols, but the integration of AI across multiple platforms and the use of cloud-based AI services can create new vulnerabilities. Future systems should incorporate advanced encryption, architectures that allow AI analysis without exposing underlying data, and robust audit trails that track every access to sensitive information. As regulatory scrutiny of AI data practices increases, particularly in jurisdictions with strict privacy laws like the European Union.
Parties should also be mindful that materials created with the use of AI tools may not be protected by attorney-client or work-product privileges. In February 2026, the U.S. District Court for the Southern District of New York in United States vs. Heppner ruled that materials an executive created using Anthropic's Claude and later shared with his lawyers were not protected by attorney-client or work-product privileges. See the discussion here on lessons learned from that case.
The developmentof industry standards and best practices for the use of AI tools in M&Acould significantly accelerate improvement and adoption. Currently, each AI provider operates independently with its own methodologies, data sources, and quality standards. The M&A industry would benefit from collaborative efforts to establish standards for AI accuracy, transparency, security, and ethical use. Professional organizations, regulatory bodies, and leading AI providers should work together to create frameworks that ensure AI tools meet minimum quality thresholds, protect sensitive information, and serve the best interests of transaction parties. Such standards would give M&A professionals confidence in AI-powered recommendations and facilitate the responsible expansion of AI capabilities.
Conclusion on Use of AI in M&A
AI tools have already transformed how M&A transactions are conducted, bringing unprecedented efficiency, accuracy, and insight to every phase of the deal process, and this transformation will only accelerate as such tools improve rapidly over time. Tools like Harvey, Legora, Claude's legal plugin, and numerous other AI platforms are no longer experimental—they are becoming essential components of modern M&A practice. By their very nature, they automatically “learn” from each successive implementation, enabling exponential growth of their capabilities.
As these technologies continue to evolve and improve, M&A professionals who embrace AI capabilities will deliver superior results for their clients, while those who resist will find themselves increasingly disadvantaged in an AI-enhanced competitive landscape. The future of M&A is here, and it is critical that participants in M&A transactions not only be aware of these tools, but learn to use them effectively.
Richard D. Harroch is a Senior Advisor to CEOs, management teams, and Boards of Directors. He is an expert on M&A, venture capital, startups, and business contracts. He was the Managing Director and Global Head of M&A at VantagePoint Capital Partners, a large venture capital fund in the San Francisco area. His focus is on internet, AI, legaltech, and software companies, and he was the founder of several internet companies. His articles have appeared online in Forbes, Fortune, MSN, Yahoo, FoxBusiness, and AllBusiness.com. Richard is the author of several books on startups and entrepreneurship as well as the co-author of Poker for Dummies and a Wall Street Journal-bestselling book on small business. He is the co-author of the 1,500-page book “Mergers and Acquisitions of Privately Held Companies: Analysis, Forms and Agreements,” published by Bloomberg Law. He was also a corporate and M&A partner at the law firm of Orrick, Herrington & Sutcliffe, with experience in startups, mergers and acquisitions, and venture capital. He has been involved in over 200 M&A transactions and 250 corporate financings. He has acted as an M&A advisor to a number of Boards, companies, and CEOs. He is an advisor to Stella Legal and a number of legal and tech companies. He can be reached through LinkedIn.
David A. Lipkin is Senior Counsel in the Silicon Valley and San Francisco offices of the law firm of McDermott Will & Schulte LLP. He represents public and private acquirers, target companies, and company founders in large, complex, and sophisticated M&A transactions, primarily in the technology and life sciences spaces, as well as working with startups and other emerging growth companies. David has been a leading M&A practitioner in the Bay Area for over 25 years, prior to that having served as Associate General Counsel (and Chief Information Officer) of a subsidiary of Xerox, and practiced general corporate law in San Francisco. He has been recognized for his M&A work in the publication “The Best Lawyers in America” for a number of years, and is the co-author of the 1,500-page book “Mergers and Acquisitions of Privately Held Companies: Analysis, Forms and Agreements,” published by Bloomberg Law. David has also been a member of the Board of Directors of the Giffords Law Center to Prevent Gun Violence for over 20 years, and has served on additional educational and charitable boards. He has been involved in over 250 M&A transactions. He can be reached through LinkedIn.
Securities litigation is undergoing a quiet but consequential transformation. The rise of artificial intelligence and a shifting regulatory environment are changing not only the types of claims being brought, but also how plaintiffs plead cases, how regulators shape and enforce rules, and how companies manage litigation risk. Together, these forces are challenging traditional approaches that no longer fit the realities of today’s market.As these dynamics evolve, companies and their advisors are
Securities litigation is undergoing a quiet but consequential transformation. The rise of artificial intelligence and a shifting regulatory environment are changing not only the types of claims being brought, but also how plaintiffs plead cases, how regulators shape and enforce rules, and how companies manage litigation risk. Together, these forces are challenging traditional approaches that no longer fit the realities of today’s market.
As these dynamics evolve, companies and their advisors are being pushed to rethink disclosure practices, litigation strategy, and the role of experts earlier than ever in the process. Precision, innovation, and the ability to translate complex financial and market data into defensible positions have become increasingly critical, particularly at the motion to dismiss stage, where cases are often won or lost outright.
We sat down with Eric Poer, Managing Director at Secretariat International, an expert advisory and disputes consulting firm whose professionals have worked on some of the most impactful matters across the globe, to discuss the changing landscape of securities litigation.
Q: Can you tell us about Secretariat and your role within the firm?
A: Secretariat is a leading advisory firm that specializes in disputes and investigations with more than 700 experts and advisors worldwide. Our experts have been engaged by most of the Am Law 100 law firms and have completed more than 10,000 engagements on six continents. We operate strategically, growing by selectively hiring top-tier professionals who not only bring deep subject matter expertise, but also fit a highly collaborative and entrepreneurial culture.
I now lead Secretariat’s securities litigation and complex financial disputes practices. Our work sits at the intersection of financial markets, regulation, and litigation, supporting clients in matters where the factual, economic, and accounting issues are both highly technical and highly consequential. I have practiced in this area for more than 20 years and have worked on some of the largest, most complex, and highest-profile securities litigation and investigations matters in the country, including the Wells Fargo sales practices investigation, one of Apple’s most significant securities litigation matters, the recent Rivian Automotive securities litigation related to its IPO, and many more.
Q: What types of clients and matters does your practice focus on?
A: Our primary clients are Am Law 100 law firms and directors and officers facing regulatory inquiries, enforcement actions, or securities litigation. We are most often engaged in situations where the issues are technically demanding and time-sensitive, and where early strategic decisions can materially affect the trajectory of a case. From an industry perspective, we routinely work with large, global financial institutions and many of the most highly regarded Fortune 100 technology companies in the world.
The matters we typically work on include securities class actions, derivative litigation, complex financial disputes, including damages assessments, and forensic investigations involving disclosure issues, market activity, valuation, or transaction-related allegations. Increasingly, we are brought in early, often before a motion to dismiss is even filed. At this early stage, we help to shape defense strategy before positions harden and costs escalate.
Q: What differentiates your securities litigation practice from others in the market?
A: The core differentiator is our expert-led, technology-enabled team model. We operate with a lean, deeply experienced group of professionals who have worked together for more than 15 years. That continuity matters. It allows us to move quickly, communicate efficiently, and apply judgment that has been refined across decades of dealing with similar matters.
Our size also enables us to take a highly strategic and tactical approach. Rather than applying a one-size-fits-all framework, we tailor our analysis to the specific allegations and strategic objectives of each case. We are technology-enabled, but expert-driven—the tools support the analysis, not the other way around.
Just as important, we are comfortable going very narrow and deep. In many cases, the most impactful issues hinge on a very specific nuance and our clients require niche expertise to support their needs. We have access to more than 1 million industry experts that we frequently partner with to supplement our accounting, economic, and financial experts. Our experience allows us to surface those nuanced issues early and help clients focus their resources where they matter most.
Q: How have client expectations in securities litigation evolved in recent years?
A: Securities litigation is quickly changing—both procedurally and due to technological shifts.
Settlements are increasingly growing, with median settlement value in 2025 at a 10-year high, particularly if a matter survives a motion to dismiss. As a result, clients increasingly expect advisors who can help them win—or significantly narrow—the case early. There is far less appetite for broad, unfocused analysis. Instead, clients want precision, credibility, and a clear articulation of why certain theories fail under scrutiny at the pleadings stage.
This has elevated the importance of targeted financial and market analysis and the ability to respond creatively and credibly when translating complex technical issues into persuasive, defensible positions under intense judicial scrutiny.
In addition, clients increasingly expect that experts know how to use AI effectively and responsibly. Deliverables that rely on generative or analytical AI must meet rigorous and defensible standards—with robust human oversight.
Q: What major enforcement and regulatory shifts are influencing securities litigation this year?
A: One of the most notable shifts is the increased role of state attorneys general in enforcement activity. As federal enforcement priorities have shifted and staffing reductions have affected agencies like the SEC and DOJ, state attorneys general appear poised to step in to fill perceived gaps. That dynamic introduces new risks for companies that may have historically focused their compliance and litigation strategies on federal regulators.
At the same time, the SEC itself has undergone significant changes, with more anticipated. These developments are creating uncertainty, but also opportunity, for public companies as they reassess disclosure practices, governance structures, and litigation risk.
Q: What about arbitration provisions as a way to limit litigation?
A: One of the more interesting and potentially transformative developments is the emerging opportunity for public companies to enforce arbitration provisions that could limit or eliminate securities class actions as we know them. The SEC has taken a more neutral stance on arbitration clauses in registration statements, opening the door for companies to revisit this issue.
What’s especially significant is that costs for companies and insurers could skyrocket, while opportunities for plaintiffs could diminish. Securities class actions in their current form, often lasting for several years, are still far more efficient than dozens or even hundreds of individual arbitration claims, each requiring separate defense.
It is an area that warrants close attention, as future challenges and regulatory responses will shape how viable this strategy ultimately becomes.
Q: We haven’t really talked about the focus that seems to be on everyone’s minds these days: artificial intelligence. How is AI influencing securities litigation?
A: AI-related securities claims have emerged as one of the most significant recent developments. Plaintiffs are increasingly focusing on alleged misrepresentations about companies’ AI capabilities, deployment, or strategic importance. Many of these cases revolve around what has been described as “AI-washing,” where companies are alleged to have overstated the sophistication or impact of AI initiatives.
Plaintiffs are testing how courts will evaluate statements about AI that may be aspirational, forward-looking, or grounded in rapidly changing technical realities. As a result, we are seeing that AI-related filings generally have been dismissed at a lower rate than other filings but are settled at a higher rate.
Outside of trends in securities class actions, my personal view is that we need to be leaning into AI or we’ll be left behind. At Secretariat, we are actively pursuing and implementing opportunities to utilize AI in a smart and responsible way.
We like to equate AI to a first-year analyst. It’s smart and capable but needs to be checked for accuracy and reasoning. So yes, we are always looking for ways to deploy advanced technology to benefit our clients, but that technology is always supported by expert judgment and never replaces it; the stakes are simply too high in our business to do otherwise.
Q: AI is certainly an increasingly litigated area. What other topics are seeing increased litigation?
A: Crypto-related disputes and enforcement remain a growing area. In 2025, there were 14 cases with crypto-related claims, 75% more than in 2024. In addition, healthcare-related filings always account for a significant portion of securities litigation filings, and this continued in 2025, with healthcare-related filings accounting for more new filings than any other sector. Each of these trends is likely to continue in 2026.
Conclusion on the Securities Litigation Landscape
Although the SEC has shifted from its regulation by enforcement era, it is evident that private litigation and state attorneys general will fill at least some of the enforcement void. In addition, the potential for public companies to enforce arbitration provisions could have a transformative effect on securities litigation as we know it; however, at this time, it is not clear that a significant number of companies have or will adopt such provisions. Undoubtedly, this year will be a year of change in the securities litigation space.
A virtual data room (VDR) (sometimes called an online data room) is a secure online repository for a company’s most important and confidential agreements and documents. In mergers and acquisitions (M&A), virtual data rooms have become core pieces of infrastructure because they make it dramatically easier to share information with potential buyers, investors, lenders, legal counsel, and other approved participants while maintaining confidentiality and control.In a typical acquisition, the buy
A virtual data room (VDR) (sometimes called an online data room) is a secure online repository for a company’s most important and confidential agreements and documents. In mergers and acquisitions (M&A), virtual data rooms have become core pieces of infrastructure because they make it dramatically easier to share information with potential buyers, investors, lenders, legal counsel, and other approved participants while maintaining confidentiality and control.
In a typical acquisition, the buyer conducts extensive due diligence to understand the target company’s financial performance, contracts, liabilities, intellectual property, customer concentration, employee matters, and more.
The VDR is where that diligence is facilitated. It is populated with critical materials—often thousands of documents—organized in a structured way so a buyer can quickly locate and evaluate what matters most. A well-run VDR can speed up a transaction, reduce friction between parties, and help prevent misunderstandings that derail deals.
Just as importantly, a VDR enables the seller to disclose information in a controlled manner. Access can be limited to pre-approved individuals, permissions can be tailored by role or bidder, and activity reporting can help the seller (and its advisors) understand who is reviewing what—and how seriously.
Below is a guide on why virtual data rooms matter, how to prepare them, common pitfalls, what should be included, and the increasing integration of AI into these platforms for M&A deals.
Why Virtual Data Rooms Matter in M&A
A well-structured VDR is not just a file cabinet, it is also a transaction tool that supports speed, diligence quality, and risk management.
Key benefits of a VDR include:
Faster diligence and fewer delays Buyers can review documents immediately (from anywhere) rather than waiting for in-person access or email back-and-forth.
Centralized, searchable information Full-text search and consistent folder structures reduce time wasted hunting for documents.
Controlled confidentiality Sellers can provide access to all documents or a subset, and only to approved parties. This is critical when sensitive customer, pricing, or IP materials are involved.
Simplified updating As diligence requests evolve, the seller can upload, replace, or supplement files without reprinting or redistributing materials.
Reduced cost vs. physical data rooms Traditional physical rooms require printing, travel, supervision, and scheduling—VDRs eliminate most of that overhead.
Better transaction management and visibility Many VDRs support tracking and reporting to show which bidders are active, which documents they view, and how frequently they return—useful signals when managing an M&A auction process.
Vendors of Virtual Data Rooms
There are many providers of virtual data rooms in the market, and pricing typically depends on factors like storage, user counts, features, AI integration, and how long the room will be used.
Typical options include:
Dedicated VDR providers (often built specifically for M&A workflows)
Enterprise file-sharing platforms that offer strong security controls (sometimes used for smaller transactions)
Law firm-hosted or advisor-supported rooms for clients engaged in complex M&A deals
When evaluating vendors, the real question is not, “Can it store files?” but, “Can it support the diligence process smoothly and securely?”
Features that often matter in M&A include:
Granular permissions (folder and document-level)
Watermarking and download restrictions
Audit logs and activity reporting
Q&A workflow support (or integrations)
Strong encryption and authentication options
AI search tools
High-level indexing capabilities
Tips for Preparing the Virtual Data Room
Preparation quality often correlates with deal velocity. Sellers that treat the VDR as an afterthought frequently pay for it later through delays, credibility loss, or retrades by the buyer.
Practical tips for preparing the VDR
Make VDR completeness a management priority The management team needs to recognize that a thorough, well-organized room is essential to a successful M&A process.
Assign accountable owners Give knowledgeable employees and functional leaders clear responsibility to collect and validate documents (legal, finance, HR, sales ops, IT/security, product). Make sure these employees have access to all important documents to ensure a complete data room
Start early—earlier than you think Building a strong VDR can be extremely time-consuming. Starting late can slow or even jeopardize a transaction.
Coordinate the VDR with disclosure schedules The diligence materials should align with the representations, warranties, and disclosure schedules in the acquisition agreement so that disclosures are complete and consistent.
Use a logical index and consistent naming A clear structure (e.g., Corporate, Cap Table, Employee Letters and Agreements, Financial, IP, Customers, HR) makes diligence more efficient and signals operational maturity.
Be thoughtful about sensitive items Consider redacting highly sensitive data (like customer-specific pricing) when appropriate, and carefully manage access to the most confidential folders.
Exclude privileged materials Do not upload attorney-client privileged communications or work product into the room; doing so can create significant legal risk.
Consider getting third-party assistance. Companies exist that can help in establishing, populating, and reviewing the data room, such as Stella Legal. This can lighten the load on the seller and its management team.
Problems Commonly Discovered When Building the Virtual Data Room
One underappreciated value of assembling the VDR is that it forces a company to confront gaps in its historical documentation. Buyers routinely uncover issues that must be fixed before closing.
Incomplete corporate records (especially around equity issuances)
Employee documentation gaps (e.g., missing confidentiality and invention assignment agreements or equity agreements)
IP files that are incomplete or inconsistent
An inaccurate or outdated capitalization table
Deficiencies like these can become closing conditions, increase escrow/holdback demands, extend timelines, or reduce valuation. In difficult cases, a buyer may require remediation that is operationally painful—such as locating former employees to sign missing IP assignments.
What Should Be in the Virtual Data Room?
As a general rule: everything material about the business that a buyer would reasonably need to evaluate the company, price risk, and draft the acquisition agreement should be included. However, what is “material” depends on the company’s size, industry, regulatory profile, and transaction structure.
Below is a comprehensive, practical checklist of document categories commonly expected in an M&A VDR.
1. Basic Corporate Documents
Certificate/Articles of Incorporation and all amendments
Bylaws and amendments
List of subsidiaries and ownership structure
Good standing certificates and key jurisdictional registrations
Board and stockholder minutes, written consents, and committee materials
List of officers and directors
Business licenses and permits
Summary of jurisdictions where the company does business or has property/operations
2. Capital Stock and Other Securities
Current capitalization table (and supporting schedules)
Stock purchase agreements and investor rights documents
Voting agreements, right of first refusal/co-sale, registration rights, information rights
Stock option plan(s), form grants, and key individual award agreements
Securities filings, blue sky compliance materials (as applicable)
Prior financing summaries and major term sheets (where appropriate and not overly sensitive)
3. Financial and Tax Matters
Audited financial statements for 3-5 years
Current unaudited financial statements
Monthly and quarterly financials from the last 3 years
Letters from auditors
Projections and assumptions/operating plans (current)
Federal income tax returns from at least 3 years
State income tax returns from at least 3 years
Foreign income tax returns from at least 3 years
Other tax returns/filings
Reassessment, deficiency, or audit notices
Banking accounts and signatories
Loans and promissory notes
Capital leases
Security agreements
Accounts receivable aging schedule
Accounts payable schedule
Description of any changes to accounting methods or principles
409A valuations
Guarantees
Bridge financings
Inventories if applicable: (i) inventory summary by major product as of most recent practicable date; (ii) schedule of consigned inventory; (iii) copies of the Company’s policies for providing for obsolete and slow-moving inventory and summary of obsolescence write-offs and provisions for slow-moving inventory for the last year; and (iv) description of the Company’s methods of inventory control
Schedule of material prepaid expenses and “other assets” as of most recent practicable date
Schedule of property, plant and equipment, and accumulated depreciation broken down into category (i.e., land, buildings, equipment, etc.) for the last year (indicating beginning balances, additions (or provisions), retirements, and ending balances
Cash flow and working capital analysis as of most recent practicable date
Pricing policies, including commission and rate schedules
Product return rate analysis for last fiscal year and current fiscal year to date
Capital expenditure programs for last and current fiscal year
List and copies of all tax sharing and transfer pricing agreements currently in effect (if there are no written transfer pricing agreements, explain the transfer pricing methodology used between affiliated entities)
Schedule of the amount, origin, and status of any U.S. net operating losses or credit carryforwards (including information on any ownership changes or other events to date which might affect such items)
Copy of most recently filed Form 5500 for 401(k) plan
Agreements waiving statutes of limitation or extending the time during which suit might be brought with respect to taxes
Correspondence regarding any tax liens
4. Material Contracts and Commitments
Summary of material agreements
Summary of agreements needing consent in the event of change in control
Material sales agreements
Intellectual property agreements (see Section 5 below)
Distribution agreements
Partnership or joint venture agreements
Leases (see Section 9 below)
Non-competition agreements
Employment agreements
Change in control agreements
Inter-company agreements
Agency agreements
Prior M&A agreements
Investment banker engagement letters
Indemnification agreements
Loan or credit agreements
Mortgages
Privacy policy
Terms of website use agreement
Other material agreements
5. Intellectual Property and Technology
Summary of patents and patent applications
Patent applications
Patents issued and patent expiration dates?
Summary of contracts where Company IP is licensed to a third party, and actual contracts
Software license agreements summary
Software license agreements
Employee non-disclosure and proprietary inventions assignment agreements
Consultant non-disclosure and proprietary inventions assignment agreements
IP litigation summary
IP litigation case filings
Claims or communications against the Company for IP infringement
Claims or communications against third parties for IP infringement
List of open source software used
Trademarks
Service marks
Technology license agreements
IP transfer or sale agreements
IP escrow agreements
Third-party non-disclosure or confidentiality agreements (consider redaction of names)
Internal policies to protect IP
List of registered copyrights
List of domain names, with expiration dates
Schedule of mask work registrations and applications
Clinical trial information (for biotech companies)
6. Employees, Consultants, and Benefits
Employee census (role, start date, location, compensation bands)
Employment offer letters and executive employment agreements
Pricing policies, discount frameworks, and approval thresholds
Sales collateral, marketing decks, and product positioning documents
Customer support metrics and SLA performance (if applicable)
Customer satisfaction surveys, NPS, and escalation logs (where appropriate)
8. Litigation, Compliance, and Regulatory
Pending, threatened, or settled litigation summaries and key documents
Government inquiries, subpoenas, or regulatory correspondence
Material compliance policies (privacy, anti-corruption, industry-specific)
Permits, certifications, and compliance audits
Insurance policies (D&O, E&O, cyber, general liability) and claims history
9. Real Estate, Property, and Tangible Assets
Leases, amendments, and landlord consents
Owned property deeds and title materials (if applicable)
Fixed asset schedules and major equipment lists
Environmental reports (where relevant)
UCC filings and liens/encumbrances
10. Corporate Strategy and Other Key Items
Organizational charts and management presentations
Board decks (often a curated set, depending on sensitivity)
Any competitive landscape analyses and market research
Product roadmaps (often staged by diligence phase)
Integration considerations (if the seller is proactively preparing)
11. Insurance
Summary of all insurance policies
Copy of directors and officers liability insurance (D&O) policies
Copy of liability policies
Copy of key person insurance policies
Copy of workers’ compensation policies
Other insurance policies
Insurance claims pending
Description of any self-insurance programs or captive insurance programs
12. Related Party Transactions
Written agreements (and description of oral arrangements) between the Company and any current or former stockholder, officer, director, or employee of the Company
Description of any direct or indirect interest of any stockholder, officer, director, or employee of the Company in any corporation or business that competes with, conducts any business similar to, or has any present (or contemplated) arrangement or agreement with (whether as a customer or supplier) (i) the Company or (ii) the acquirer
Documents not covered by the above relating to agreements of the Company in which either current or former stockholders, officers, directors, or employees of the Company are or were materially interested
List identifying any stockholders, officers, directors, or employees of the company who have an interest in any of the assets of the Company
How AI Can Help With Virtual Data Rooms
Artificial intelligence is increasingly being integrated into or used with virtual data room platforms and related deal-management tools. When used thoughtfully, AI can materially improve the speed, accuracy, and effectiveness of the M&A due diligence process, benefiting both buyers and sellers.
For example, the Luminance AI software can be integrated into VDRs to search among hundreds of thousands of contracts to spot any unusual provisions, such as:
Change-of-control clauses
Assignment restrictions
Unusual termination rights (such as termination for convenience rights by the customer)
Non-standard indemnities or liability caps
Auto-renewal provisions
Inconsistent terms across similar agreements
Key ways AI enhances virtual data rooms include:
Automated document organization and indexing: AI-powered tools can automatically categorize uploaded documents into appropriate folders (e.g., contracts, financials, HR, IP) based on content recognition. This reduces manual sorting, improves consistency, and accelerates VDR setup, which is particularly valuable when dealing with thousands of files.
Intelligent search and document retrieval: Advanced AI-driven search goes beyond keyword matching. Natural language processing allows users to ask questions such as “find agreements expiring in the next 12 months,” dramatically improving diligence efficiency.
Contract analysis and issue spotting: AI can review large volumes of contracts to flag potentially problematic provisions for an acquirer. This allows legal and business teams to focus their attention on higher-risk areas rather than routine review.
Redaction and confidentiality protection: AI-assisted redaction tools can identify and redact sensitive information—such as personal data, pricing terms, or confidential customer names—more quickly and consistently than manual processes, helping sellers balance transparency with confidentiality.
Q&A process optimization: In buyer-seller Q&A workflows, AI can keep diligence moving and reduce repetitive work for management teams by:
Suggesting answers based on prior responses or existing documents
Identifying duplicate or overlapping questions
Routing questions to the correct internal owner
Tracking response times and unresolved issues
Activity analytics and bidder insight. AI-enhanced analytics can help sellers and their advisors better manage competitive auction processes and prioritize follow-up. AI can interpret VDR activity data to provide insights such as:
Which bidders are most engaged
Which documents generate the most interest
Where diligence may be stalling or accelerating
Consistency checks and disclosure alignment. To reduce the risk of surprises late in the transaction and support cleaner representations and warranties, AI tools can help identify inconsistencies between:
Financial statements and management reports
Cap tables and equity documentation
Contracts and disclosure schedules
Faster diligence timelines overall. By automating routine review tasks and improving information accessibility, AI-enabled VDRs can materially shorten diligence cycles—often a critical factor in maintaining deal momentum and preventing buyer fatigue.
Important Caveats When Using AI in VDRs
Human judgment remains essential AI is a powerful assistive tool, but it does not replace experienced legal, financial, or business judgment—particularly when assessing risk, materiality, or deal-specific nuances.
Data quality still matters AI outputs are only as good as the underlying documents. Incomplete, outdated, or poorly scanned materials will limit effectiveness.
Confidentiality and security must remain paramount Companies should ensure AI tools comply with applicable data privacy, confidentiality, and security requirements—especially when sensitive customer or personal data is involved.
Bottom Line on AI Usage in Virtual Data Rooms
AI is rapidly becoming a meaningful tool in virtual data rooms. When integrated properly, it helps sellers run cleaner, faster processes and helps buyers conduct deeper diligence with fewer resources. As M&A transactions continue to demand speed without sacrificing rigor, AI-enabled VDRs are likely to become the standard rather than the exception.
Final Thoughts on Virtual Data Rooms
In modern M&A, diligence is won or lost on speed, accuracy, organization, and completeness. A strong virtual data room helps a seller run an efficient process, reduces buyer uncertainty, and limits the risk that issues surface late in the process—when leverage shifts and deal terms become more punitive.
If you are preparing for a sale process, treat the VDR as a strategic asset. Build it early, organize it thoughtfully, and ensure it tells a coherent story about the company that is supported by clean, complete documentation. Done right, the VDR becomes one of the most practical tools you have to protect confidentiality, preserve momentum, facilitate due diligence, and close a successful transaction.
Over the past decade, compensation for artificial intelligence (AI) professionals has surged at an unprecedented pace, reshaping the talent market and redefining what employers must offer to attract and retain top-tier technical talent. As companies across nearly every sector race to integrate machine learning, automation, and generative AI into their operations, the demand for skilled AI engineers, researchers, and product leaders has vastly outstripped supply. The result is a compensation envi
Over the past decade, compensation for artificial intelligence (AI) professionals has surged at an unprecedented pace, reshaping the talent market and redefining what employers must offer to attract and retain top-tier technical talent. As companies across nearly every sector race to integrate machine learning, automation, and generative AI into their operations, the demand for skilled AI engineers, researchers, and product leaders has vastly outstripped supply. The result is a compensation environment that is not only highly competitive, but increasingly aggressive.
What makes this shift especially striking is how rapidly it has accelerated. Even five years ago, AI roles commanded above-average compensation, but nowhere near the levels seen today. Now, seven-figure packages for senior AI experts are not only possible, they’re becoming increasingly common.
This surge is driven by a unique convergence of market forces: the explosion of generative AI capabilities, a shortage of qualified talent, escalating corporate reliance on AI strategy, and the emergence of new startup and investment ecosystems flush with capital. Together, these factors are pushing AI compensation to historic highs, with no signs of slowing down.
And of course, this article was written with the research assistance of AI.
The Talent Shortage Driving the Compensation Surge
AI is one of the few fields in which global demand massively exceeds global supply of qualified professionals. Only a small subset of software engineers possess the deep expertise required for advanced machine learning, reinforcement learning, natural language processing, and large-scale model development. Even fewer have hands-on experience with cutting-edge deep learning architectures or the ability to integrate foundation models into commercial products.
Companies are discovering that they are effectively competing for the same limited pool of elite talent. And that competition is fierce.
Here are a few key reasons AI talent is scarce:
AI research and engineering require advanced mathematical, algorithmic, and computational training.
Top-tier AI expertise is concentrated in a handful of universities and research labs.
Rapid technological change means experience becomes outdated quickly, raising the premium on continuous learners.
Many AI professionals gravitate toward startups or independent research labs rather than traditional corporate roles.
Immigration constraints limit access to global AI expertise in certain regions, especially the U.S.
This scarcity alone would elevate compensation, but the explosive commercial potential of AI has supercharged it.
Generative AI Has Reshaped the Compensation Landscape
The release of large-scale generative AI models has catalyzed a gold rush. Companies of all sizes now recognize that AI will determine competitive advantage in the coming decade. As firms shift from “AI experiments” to “AI strategy,” the urgency to hire expert talent has become acute.
Generative AI has created entirely new job categories, including:
Large Language Model (LLM) Engineers
Prompt Engineers and Prompt Architects
AI Product Managers and AI Strategy Leads
Applied AI Scientists
Multimodal AI Specialists
AI Safety and Alignment Researchers
Model Evaluation and Red Teaming Experts
AI Video Specialists
In many cases, these roles did not exist 18 months ago. Now, they are some of the highest-paying jobs in the technology sector.
Salaries Are Reaching Historic Highs
Compensation varies widely based on geography, seniority, company size, and specialization. But one trend is clear: AI salaries are increasing across the board, often dramatically.
Typical U.S. salary ranges for AI roles:
Machine Learning Engineer: $180,000–$350,000+ total compensation
Senior AI Scientist: $300,000–$600,000+
LLM Engineer or Generative AI Engineer: $400,000–$900,000+
AI Product Director: $350,000–$700,000+
Head of AI / VP of AI: $700,000–$2,000,000+
Distinguished AI Researcher at top tech firms: Often over $1 million, with equity packages that can reach multi-millions
And these figures do not account for extreme outliers—most notably the seven-figure offers made by OpenAI, Anthropic, Google DeepMind, Meta, and specialized hedge funds or trading firms.
Compensation for AI talent is highest in the Silicon Valley/San Francisco area, followed by New York and then Seattle.
Startups Are Offering Massive Equity Packages
AI startup funding is booming. Investors are pouring billions into companies developing foundation models, AI infrastructure, and vertical AI applications. With capital plentiful and competition intense, startups are offering generous equity to lure experienced AI hires away from Big Tech.
What startups are offering:
Sign-on equity that may exceed 0.5–2% of the company for early senior hires
Better vesting schedules (e.g., no cliff vesting, shorter vest cycles)
Performance-based equity refreshers
Access to secondary liquidity opportunities as they become available
Hybrid cash/equity compensation at levels competitive with major tech companies
For highly specialized engineers, particularly those with LLM or multimodal model experience, equity stakes can be extremely significant.
The big players are stepping up as well. In late 2025, OpenAI’s average stock compensation reportedly reached $1.5 million per employee for its 4000 person workforce.
Non-Tech Companies Are Entering the Bidding War
AI is no longer limited to technology firms. Industries such as healthcare, finance, manufacturing, retail, defense, and media all have aggressive AI build-out strategies. This has expanded the competition for talent beyond Silicon Valley, creating upward pressure on compensation.
For example:
Financial institutions are recruiting AI specialists for algorithmic trading and risk modeling.
Healthcare companies need AI leaders for diagnostics, drug discovery, and patient management systems.
Traditional industrial firms are hiring machine learning engineers to optimize robotics, forecasting, and supply chain operations.
These companies often have substantial cash reserves, enabling them to offer compelling salary packages more commonly associated with Big Tech.
Remote Work Has Globalized the AI Salary Market
Remote-first hiring has created a global bidding environment. Companies that once paid lower regional salaries are now forced to match global standards—especially when competing against deep-pocketed AI enterprises and venture-backed startups.
As a result:
Compensation is rising across Europe, Latin America, India, and Southeast Asia.
Remote AI contractors in lower-cost countries are sometimes commanding Silicon Valley–level pay.
Employers can no longer rely on geographic arbitrage to meaningfully cut costs.
This globalization has further driven compensation upward.
Retention Packages Are Becoming More Aggressive
As poaching becomes rampant, companies are creating elaborate retention structures, including:
Annual equity refresh grants
Retention bonuses tied to multi-year milestones
Stay bonuses during M&A or restructuring
Accelerated equity vesting for high performers
Companies recognize that replacing a senior AI engineer or researcher is extremely costly, and often impossible in the short term.
What This Means for Employers
Companies should expect:
Longer search timelines for AI roles
Substantially higher compensation budgets
The need for flexible, customized packages
Aggressive competition from startups and Big Tech
Ongoing retention challenges
Organizations that fail to invest in AI talent will struggle to compete strategically, technologically, and operationally.
What This Means for AI Professionals
For employees, the moment is historic. AI expertise, especially in LLMs, applied machine learning, infrastructure, safety, and AI product design, is one of the most valuable skill sets in the global economy.
Professionals should:
Negotiate assertively
Evaluate total comp (salary, bonus, equity, benefits)
Secure severance and change-in-control protections
Understand equity liquidity options
Consider both Big Tech stability and startup upside
Those with the right skills can expect strong compensation growth for the foreseeable future.
How AI Employees Can Negotiate High-Value Compensation Packages
This section outlines the most important strategies, components, and negotiation techniques AI employees can use to maximize compensation and secure long-term professional protection.
1. Evaluate Total Compensation, Not Just Salary
A common mistake candidates make is focusing on base salary alone. In AI roles—especially at high-growth startups—base salary may not be the most important part of the package.
AI employees should evaluate:
Base salary
Annual bonuses or performance incentives
Equity grants
Retention or milestone bonuses
Equity refresh cycles
Severance protections
Change-in-control payments
Total compensation packages in AI can vary by hundreds of thousands of dollars depending on equity and incentives, making it essential to evaluate the full structure.
2. Negotiate Equity—It’s Often the Most Valuable Component
AI startups and AI-first public companies rely heavily on equity to attract top-tier talent. But equity terms are nuanced and highly negotiable.
Key equity terms you should negotiate:
Size of the grant (expressed as % ownership or # of shares)
Equity type (options vs. RSUs)
Vesting schedule (you can ask for shorter vesting schedules and no cliff vesting)
Acceleration triggers (single- vs. double-trigger vesting)
Windows to exercise options after leaving the company (traditionally 90 days but you can request one year)
Ability to participate in secondary sales
A single percentage point of equity at a strong AI startup can be worth millions of dollars in a successful exit. Do not underestimate your ability to negotiate this component.
Pro tip: Ask for your equity in terms of percentage ownership, not number of shares. This forces companies to reveal the fully diluted share count.
3. Push for Clear and Achievable Bonus Structures
AI work is often tied to quantifiable outcomes: model accuracy, latency improvements, deployment milestones, or product releases. This makes it easier to negotiate objective bonus structures, rather than subjective or discretionary ones.
You can negotiate:
A signing bonus
A target bonus (often 20–50% of salary for senior roles)
A guaranteed minimum first-year bonus
Objective, measurable performance metrics
A clear timeline for bonus evaluation
Eligibility for multi-year performance awards
4. Benefits and Perks
Beyond salary and bonuses, benefits protect well-being and support work-life integration—particularly important for senior leaders.
Benefits can include:
Comprehensive health, dental, vision, life, and disability insurance
Retirement plans such as 401(k) with employer match and pension enhancements.
Vacation, sick leave, and paid time off accruals with carry-over provisions on termination.
Relocation assistance, travel allowances, and technology stipends.
Parental leave
5. Secure Strong Severance and Termination Protections
Given the velocity of change in AI—funding cycles, pivots, acquisitions, and leadership turnover, severance protections are essential. They are highly negotiable for AI professionals.
Negotiate for:
3–12 months of salary severance pay if fired without cause, together with 3-12 months of target bonus
Continuation of benefits or COBRA during the severance period
Accelerated vesting of equity upon termination without cause
Severance triggers if your role changes materially
Limit the “cause” definition– you want to avoid broad definitions of being terminated for “cause” to avoid losing out on severance
Mutual releases of liability and mutual non-disparagement clauses in the event of termination without cause
Many AI companies do not offer severance by default, but will add it if asked by a senior or highly valuable hire.
6. Leverage Competing Offers Strategically
AI employees who interview with multiple companies often have dramatically better outcomes. Even one additional offer can significantly increase your negotiation leverage.
Tips for handling competing offers:
Never bluff—only leverage real offers.
Share general ranges, not exact numbers (“my other offer is in the ~$500K range”).
Emphasize fit and culture, not financial extraction.
Allow employers to “revise” offers rather than demanding increases.
Companies expect AI talent to be in high demand. You should expect and encourage competition.
7. Protect Yourself from Liability
AI work often includes high-stakes systems, regulatory exposure, or sensitive data. Professionals should negotiate strong protections.
Indemnification for work done within the scope of your role
Reasonable limits on personal liability
AI professionals involved in model development, compliance, or safety can insist on explicit liability protection.
8. Remote Work and Flexible Arrangements Are Negotiable
AI talent is global, and many companies are remote-first. If location flexibility matters to you, negotiate it early.
You can request:
Fully remote work
Hybrid flexibility (e.g., two days in the office each week)
Home office stipends
Relocation packages, if required
Adjustments for time-zone differences
Given how scarce AI talent is, many companies will accommodate flexibility for the right candidate.
9. Consider Other Important Issues
Here are some additional important issues to consider when negotiating an employment contract or offer letter:
Avoid any non-compete clauses that would hinder you from finding a new AI job. In some states like California, those are for the most part unenforceable anyway
If there is a dispute with your employer, you will likely want the matter to be resolved by confidential binding arbitration to avoid lengthy and costly litigation
Make sure you are not taking any documents or confidential information from your old employer– this can lead to expensive and embarrassing litigation
Get any oral promises made to you in writing as part of your employment agreement or offer letter
Carefully review the terms of any rights of repurchase on equity, right of first refusal, and company buy-back terms, which could limit the value of your equity
10. Work with an Attorney or Advisor for Complex Packages
AI compensation packages, especially those involving equity, are increasingly complex. Understanding tax implications, vesting schedules, and contract terms often requires professional review.
An attorney or advisor can help you:
Interpret equity and vesting terms
Understand company cap tables
Identify red flags in employment contracts
Strengthen negotiation positions
Include protective contract terms
A modest legal investment can protect hundreds of thousands—and sometimes millions—of dollars in future compensation. And sometimes you can negotiate for the company to reimburse your reasonable legal fees incurred.
Conclusion on Compensation for AI Employees
AI employees today are in a uniquely powerful negotiating position. Compensation is skyrocketing. Companies are racing to hire scarce talent, and the strategic importance of AI expertise has never been higher. By approaching negotiations with clarity, confidence, and a deep understanding of total compensation, AI professionals can secure packages that reflect both their current value and their long-term contribution.
In an era defined by rapid innovation and intense competition, negotiating well is not just a financial decision, it’s a strategic career move.
Choosing the right name for your startup is one of the most important early decisions you’ll make as a founder. A compelling, clear, and memorable name can help your brand stand out, attract customers, ease marketing, and avoid legal headaches down the road. Conversely, a confusing or poorly chosen name can make growth harder, block discoverability, or even force a costly rebrand later.Here are 15 smart naming strategies to guide you—from brainstorming to legal checks—to help you choose a name t
Choosing the right name for your startup is one of the most important early decisions you’ll make as a founder. A compelling, clear, and memorable name can help your brand stand out, attract customers, ease marketing, and avoid legal headaches down the road. Conversely, a confusing or poorly chosen name can make growth harder, block discoverability, or even force a costly rebrand later.
Here are 15 smart naming strategies to guide you—from brainstorming to legal checks—to help you choose a name that works now and scales with your business.
1. Keep It Simple, Clear, and Easy to Spell
If people struggle to spell or pronounce your name, they may not find you online—which means lost website visits, missed word-of-mouth referrals, and lower discoverability. Avoid intentionally misspelled or overly stylized names (even if they seem cool at first).
SEO benefit: A name that’s easy to type and spell helps with organic search, link sharing, and avoids frequent misspellings.
2. Opt for Short, Memorable Names
Shorter names—ideally 1–3 syllables—tend to be easier to recall, type, and say.They also tend to work better as domain names or social-media handles.
Long names may become unwieldy in logos, URLs, business cards, or when spoken aloud. Avoid long names.
3. Ensure Domain and Online Availability Early
In today’s digital-first world, your domain name is like your storefront address. One of the worst mistakes is picking a great name—then discovering the .com domain, or other upper-level domain you pick, is already taken.
Before you fall in love with any name, check:
Availability of the .com (or your preferred top-level domain)
Availability of matching social media handles
Possible confusing variants, misspellings, or substitutes
A name that matches a clean domain and consistent social presence significantly improves your brand’s professionalism and search visibility. My strong preference is for .com domain names, as it has more credibility than some random other name. I particularly dislike the .io names.
4. Conduct a Trademark and Legal Availability Search
Beyond domain names, you must ensure you’re not infringing on existing trademarks (in your country or globally, if you’ll operate internationally). Otherwise, you risk legal demands, rebranding expenses, or forced name changes.
Check:
USPTO (or relevant national registry) databases for exact name matches and close variants
State-level business name databases
Potential overlap with similar-sounding or phonetically comparable brand names
Check before committing—that brilliant name isn’t so brilliant if someone else already owns it.
5. Align the Name With Your Brand Identity and Vision
A company name should reflect your startup’s values, mission, and brand personality. Are you playful or serious? Tech-forward or traditional? Luxury or mass-market?
Use your brand positioning to guide naming—a name that resonates with your ideal customer will strengthen brand connection and help in marketing messaging.
6. Use Creative Brainstorming—Then Shortlist Thoughtfully
Don’t settle on the first name that sounds good. Brainstorm extensively: mix keywords, try metaphors, invented words, alliterations, or evocative terms. Then shortlist.
When shortlisting, score each name on criteria such as:
Pronounceability
Memorability
Brand potential
Emotional resonance
Domain/handle availability
This methodical approach, recommended by branding experts, helps balance creativity with practical constraints.
7. Think Long-Term—Choose a Name That Scales
Your startup may evolve. You might expand into new products, markets, regions, or shift strategy entirely.
A too-narrow or overly descriptive name can box you in down the road (for instance, “San Francisco Tacos” becomes awkward if you expand nationwide or start delivering Mexican-style snacks rather than tacos).
Pick a name flexible enough to grow with your business.
8. Run “Crowded Bar” and Voice-Search Tests
Ask friends or colleagues to hear your candidate name in a noisy environment (or over a low-quality phone line). If they mishear or misspell it, it may not work in real-world word-of-mouth or voice-based search (Siri, Alexa, Google Voice, etc.).
If a name can be misheard as something embarrassing or confusing (e.g., “Sam & Ella’s” sounding like “salmonella”), it fails the test.
9. Consider How the Name Works Globally (Pronunciation & Cultural Appropriateness)
If you plan to reach international customers, make sure the name:
Is pronounceable across major languages
Doesn’t have unintended meanings or negative connotations in other languages
Avoids letters or sounds that are difficult for non-native speakers
10. Avoid Names That Are Too Generic or Descriptive
Names that simply describe your product or function (“Fast Delivery Service,” “Green Cleaning Co.”) may feel safe—but they rarely stand out or invite strong brand identity.
Strong brand names often lean toward evocative, abstract, or metaphorical terms that build meaning over time (think “Apple,” “Slack,” “Uber”).
11. Use Subtle Psychology—Sound, Rhythm, and Emotion Matter
A name can carry emotional weight or subconscious appeal. Simple, rhythmic, pleasant-sounding names are often more memorable and likable. A name that “feels right” often builds positive brand associations—comfort, trust, excitement—long before someone learns what the company does.
12. Validate With Real People—Get Feedback Early
Even a name you love may not land with customers. Test your top options:
With potential customers—get honest reactions.
Across demographics—different ages, cultures, languages.
In different forms—spoken aloud, read in print, typed as a URL, etc.
Real feedback can reveal mispronunciations, negative associations, or misleading impressions you didn’t foresee.
13. Confirm Domain, Social Handles, and URLs at the Same Time
Don’t assume domain availability—check it first. Ideally, secure the .com immediately once you’ve selected your name. Also check whether key social media handles (X, Instagram, LinkedIn, etc.) are available or can be adapted consistently. Understand that if it’s a good name, it is probably already owned by someone else and you may have to purchase it for a premium. See Key Steps in Obtaining a Great Domain Name.
Consistent naming across web, social, and branding channels builds trust, recognition, and avoids confusion.
14. Consider SEO & Discoverability—Think Like Your Customers
Search engine optimization (SEO) can make or break early growth. When naming your startup, you should consider:
Whether the name includes or evokes keywords relevant to your business (without being too generic).
How easy it will be to rank for the brand name.
Whether it’s easy to type, pronounce, and search—which helps both web traffic and voice-search discoverability.
A name that supports SEO and branding from the start can save you costly rebrands or SEO work later.
15. Act—Don’t Get Paralyzed: Set a Naming Deadline
Naming can be overwhelming. If you overthink it, you risk delaying your launch or getting stuck in endless brainstorming. Instead:
Set a firm deadline to choose your name
Once your criteria are met (pronounceable, domain available, trademark clear, brand-aligned, feedback OK)—choose and move forward
Perfect is almost never obtainable—but a “good enough, strong” name that meets all core criteria is often far better than continuing indecision. Better to be good and done than trying for perfection. Even if you come up with the perfect name, the price to obtain the .com name may be over your budget.
Why These Naming Strategies Matter
Brand identity and positioning: The name shapes first impressions—conveying tone, professionalism, values, and vision.
Marketing and word-of-mouth: A name that’s easy, memorable, and pronounceable gets shared, typed, talked about, remembered.
Digital visibility: Domain names, SEO, and consistent branding ensure discoverability and avoid confusion.
Scalability: A flexible name adapts as your startup evolves or pivots to include new products, services, or geographies.
A strong name becomes a long-term asset, foundational to your brand, marketing, user acquisition, and growth.
Final Thoughts: Build Your Brand on a Name That Works
Naming your startup isn’t just a creative fun exercise—it’s a strategic decision that affects your identity, visibility, and future growth. By combining creativity with practical tests, legal checks, and real-world validation, you can choose a name that gives your startup a strong foundation.
Use these 15 smart strategies as a checklist. Brainstorm boldly. Research thoroughly. And—once you find a name that checks the essential boxes—commit confidently.
Your startup deserves a name that not only sounds good, but also helps you grow, scale, and succeed.
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