Tag: AI Models 2026

  • Claude Computer Use: The Complete Tutorial

    Claude computer use is a capability that lets Claude control a computer — click buttons, type text, navigate browsers, run applications, and execute multi-step tasks as if it were a human operator. As of 2026, it’s one of the most powerful and underexplored capabilities in the Claude ecosystem. This tutorial covers what it is, how to set it up, what it’s actually useful for, and where it still falls short.

    What Is Claude Computer Use?

    Computer use is an API capability (not available in the standard Claude.ai interface) that lets Claude interact with a desktop environment via screenshots and tool calls. Claude sees the screen, decides what to click or type, executes that action, sees the updated screen, and continues — iterating until the task is complete.

    This is different from a browser extension or web scraper. Claude is operating a real (or virtualized) computer environment the same way a human would — by looking at the screen and interacting with what it sees.

    Current Benchmark Performance

    On OSWorld — the leading benchmark for computer use agents — Claude currently scores around 22% task completion on the most complex tasks. ChatGPT’s computer use scores higher on this specific benchmark at approximately 75%. This gap is real and matters for production use cases requiring high reliability. For simpler, more structured tasks, Claude’s computer use performs considerably better.

    Setting Up Claude Computer Use

    Computer use requires API access. The basic setup:

    • Anthropic API key (API tier with computer use enabled)
    • A virtual machine or containerized desktop environment (Docker with a lightweight Linux desktop is the standard approach)
    • The Anthropic Python or TypeScript SDK

    Anthropic provides a reference implementation with a Docker-based Ubuntu environment, a noVNC interface for monitoring, and starter code. This is the fastest path to a working computer use setup.

    Best Current Use Cases

    • Web research and data extraction: Navigate websites, extract structured data, fill in forms — tasks that don’t have APIs
    • Software testing: Navigate UI flows, test edge cases, verify visual behavior
    • Repetitive desktop workflows: Tasks that require clicking through multiple application screens
    • Legacy software interaction: Applications without APIs where the only interface is visual

    Key Limitations to Know

    • Reliability: Computer use is significantly less reliable than direct API calls for the same tasks. Where an API returns structured data, computer use can misread a screen or click the wrong element
    • Speed: Screenshot-based interaction is slow compared to direct integration
    • Cost: Each screenshot and tool call consumes API tokens; complex tasks can be expensive
    • Sensitive actions: Never use computer use for high-stakes irreversible actions (sending emails, making purchases) without human-in-the-loop verification

    Frequently Asked Questions

    Is Claude computer use available in Claude.ai?

    No. Computer use is an API capability available through the Anthropic API, not the standard Claude.ai web interface.

    How does Claude computer use compare to ChatGPT’s?

    On OSWorld benchmarks, ChatGPT’s computer use currently leads at approximately 75% vs Claude’s ~22%. For production use cases requiring high reliability, this gap matters. Both are improving rapidly.


    Need this set up for your team?
    Talk to Will →
  • Claude AI for Email: Templates, Cold Outreach, and Professional Communication

    Email is where productivity goes to die — and it’s one of Claude AI’s highest-leverage use cases. Whether you’re writing cold outreach, responding to a difficult client, following up after a meeting, or drafting an important internal announcement, Claude can cut your writing time by 70% while improving quality. This guide covers the email types where Claude generates the most value, with prompts and templates you can use immediately.

    How to Get the Best Email Results from Claude

    The quality of Claude’s email output is directly proportional to the context you provide. The three most important inputs are: (1) who you’re writing to and their likely mindset, (2) what you want them to do after reading, and (3) the tone and relationship dynamic. Spend 30 seconds on these inputs and you’ll spend zero time editing the output.

    1. Cold Outreach Emails

    Write a cold email to [Name], [Title] at [Company]. They [brief context about them/their company]. I’m reaching out because [specific, relevant reason]. I want them to [specific call to action — 15-minute call, reply with interest, etc.]. My credibility for this outreach: [1 sentence]. Tone: [direct / conversational / formal]. Under 100 words. Don’t start with “I hope this finds you well.” Don’t use the word “synergy.”

    2. Meeting Follow-Up Emails

    Write a follow-up email after a [meeting type] with [Name]. We discussed: [key points]. Action items: [who does what by when]. Next meeting: [date/TBD]. Tone: [professional / warm]. Keep it under 150 words — just the essentials, no filler.

    3. Difficult Conversations and Sensitive Topics

    This is where Claude genuinely shines. Delivering bad news, setting limits, addressing conflict — these emails are hard to write because the stakes are high and the emotional charge is real. Claude helps you find the right words:

    Help me write an email to [Name] about [sensitive situation]. The key message I need to convey: [core message]. What I want them to feel: [heard and respected / clear on the consequences / aware of next steps]. What I want them to do: [action]. I want to be [direct / empathetic / professional] without being [harsh / vague / overly apologetic]. Draft 2 versions: one more direct, one softer.

    4. Client Communication Templates

    Build a library of templates Claude can maintain in a Project:

    • Project kickoff welcome email
    • Scope creep or change order introduction
    • Project delay notification
    • Invoice and payment follow-up (escalating versions)
    • Contract renewal or upsell introduction
    • Difficult feedback delivery after poor performance

    5. Internal Announcements and Company Updates

    Write an internal company announcement about [topic]. Audience: [all-staff / managers / specific team]. Key information: [what’s happening, when, why it matters]. Tone: [transparent and direct / enthusiastic / matter-of-fact]. Length: [1 paragraph / full memo]. Include: [any specific elements — FAQs, links, contact for questions].

    6. Email Inbox Management

    Beyond writing emails, Claude can help manage your inbox: paste an email chain and ask Claude to summarize it, identify what’s being asked of you, draft a response, or flag what requires immediate attention.

    Frequently Asked Questions

    How do I make Claude emails sound like me?

    Paste 3-5 examples of emails you’ve written that you’re proud of and say “This is my writing style — match it in everything you write for me.” Claude will calibrate to your voice within a session, or you can save this instruction in a Claude Project for persistence.

    What is the best Claude plan for email writing?

    The free tier works for occasional emails. Claude Pro ($20/month) with Projects is the right choice for professionals who write dozens of emails daily — you can store your voice, templates, and common contexts for instant use.


    Need this set up for your team?
    Talk to Will →
  • Claude AI for HR: Job Descriptions, Policies, and Employee Handbooks

    Human Resources is one of the most document-heavy functions in any organization — and most HR documents are variations on established templates. Claude AI excels at this: generating professional, legally-aware (though not legally-binding) HR documents quickly, consistently, and at scale. This guide covers the core workflows where HR professionals are getting the most value from Claude in 2026.

    Important Note on Legal Review

    Claude can draft HR documents, but any policies, employee agreements, or handbooks should be reviewed by qualified employment counsel before implementation. Labor law varies by state, country, and industry. Use Claude to accelerate drafting — not to replace legal review.

    1. Job Description Writing

    Writing job descriptions is time-consuming and inconsistent when done ad hoc. Claude can generate complete, accurate, inclusive job descriptions in minutes:

    Write a job description for a [Job Title] at a [company type/size] in [industry]. The role reports to [title]. Key responsibilities: [list 4-5 main duties]. Required qualifications: [must-haves]. Preferred qualifications: [nice-to-haves]. The role is [remote / hybrid / on-site]. Salary range: [$X – $Y]. Company culture is [2-3 descriptors]. Write in an inclusive tone, avoid gendered language, and include an EEO statement at the end.

    Ask Claude to generate multiple versions — one more formal, one more culture-forward — and choose the best fit.

    2. HR Policy Drafting

    Claude can draft first versions of virtually any HR policy:

    • Remote work and flexible schedule policy
    • PTO, sick leave, and FMLA policy
    • Anti-harassment and anti-discrimination policy
    • Expense reimbursement policy
    • Social media use policy
    • Confidentiality and NDA policy
    • Performance improvement plan (PIP) templates

    Prompt: “Draft a remote work policy for a [company size] company in [industry]. Key elements: eligibility criteria, equipment stipend, core hours expectations, home office requirements, data security requirements, and a process for requesting exceptions. Tone: professional but not overly legalistic.”

    3. Employee Handbook Creation

    Building a full employee handbook from scratch is a multi-week project. With Claude, you can have a complete draft in days. Work section by section:

    Write the [section name] section of an employee handbook for a [company type]. Key points to cover: [list]. Tone: [approachable and human / formal and professional]. Length: approximately [X] words. Include subheadings for readability.

    Build a Claude Project with your company’s mission, values, and existing policies — Claude will maintain consistency across all sections automatically.

    4. Performance Review Templates

    Claude generates review templates, self-assessment forms, and manager feedback frameworks:

    • Annual review forms with competency-based rating scales
    • 90-day new hire assessment templates
    • 360-degree feedback questionnaires
    • Manager effectiveness surveys
    • Goal-setting frameworks (OKR, SMART goals)

    5. Onboarding Materials

    First-week onboarding experiences set the tone for employee retention. Claude can build:

    • 30/60/90 day onboarding plans by role
    • Welcome emails from hiring managers and executives
    • FAQ documents for new hires
    • Role-specific training checklists
    • Team introduction templates

    Frequently Asked Questions

    Can Claude draft legally compliant HR policies?

    Claude can produce well-structured, professional drafts, but it is not a lawyer and cannot guarantee legal compliance. All HR policies should be reviewed by qualified employment counsel before implementation.

    What is the best Claude plan for HR teams?

    Claude’s Team plan is ideal for HR teams, allowing shared Projects where company values, policies, and style guides can be stored centrally so every HR professional generates consistent output.


    Want this for your workflow?

    We set Claude up for teams in your industry — end-to-end, fully configured, documented, and ready to use.

    Tygart Media has run Claude across 27+ client sites. We know what works and what wastes your time.

    See the implementation service →

    Need this set up for your team?
    Talk to Will →
  • Claude AI for Product Managers: PRDs, User Stories, and Roadmaps

    Product management is one of the most document-intensive roles in a technology company, and Claude AI has become an indispensable tool for PMs who want to move faster without sacrificing quality. This guide covers the specific workflows where Claude generates the most value: PRD writing, user story generation, competitive analysis, roadmap planning, and stakeholder communication.

    1. Writing PRDs That Engineering Teams Actually Use

    Product Requirement Documents (PRDs) are only useful if engineering reads them. Claude helps write PRDs that are clear, complete, and structured in a way that minimizes back-and-forth.

    Write a PRD for [feature name]. Background: [1-2 sentences on why this feature matters]. Problem being solved: [specific user pain point with evidence if you have it]. Target users: [persona]. Proposed solution: [high-level description]. Success metrics: [what we’ll measure]. Out of scope: [what this specifically won’t do]. Open questions: [things engineering needs to decide]. Format: executive summary, problem statement, goals, user stories, requirements (must-have / nice-to-have / out of scope), success metrics, open questions.

    2. User Story Generation

    Claude generates complete user story suites from feature descriptions, including edge cases most PMs miss:

    Generate a comprehensive set of user stories for [feature]. Include: happy path stories, error and edge case stories, admin/internal user stories, and accessibility considerations. Format each as: As a [user type], I want to [action], so that [benefit]. Also note acceptance criteria for each story.

    3. Competitive Analysis

    Paste competitor feature pages, product blogs, or release notes into Claude for rapid synthesis:

    • Compare feature sets across competitors in a structured table
    • Identify positioning gaps your product can own
    • Summarize competitor pricing strategies
    • Extract customer complaints from review sites you paste in

    4. Roadmap Planning and Prioritization

    Claude can help apply prioritization frameworks to your backlog:

    Here is our current feature backlog: [paste list]. Apply a RICE scoring framework (Reach, Impact, Confidence, Effort) to each item. Make assumptions where needed and note them. Then rank by RICE score and identify the top 5 features for our next quarter.

    5. Stakeholder Communication

    The PM role requires translating technical complexity to executives and business context to engineers. Claude handles both:

    • Executive summaries: “Rewrite this technical spec as a 1-page executive briefing for a non-technical VP”
    • Engineering handoffs: “Add technical context and API considerations to this PRD section”
    • Roadmap slides: “Write the narrative for each slide of our Q3 roadmap presentation, connecting each initiative to our company OKRs: [paste OKRs]”
    • Launch comms: “Write an internal launch announcement for [feature] that explains what it does, who it helps, and how to use it”

    Frequently Asked Questions

    What is the best Claude plan for product managers?

    Claude Pro ($20/month) with Projects is the sweet spot. Create a Project with your company’s product context, OKRs, and writing style guide — Claude will use that context automatically in every PM document you generate.

    Can Claude read user research or interview transcripts?

    Yes. Claude’s 200K-token context window can handle lengthy user interview transcripts, survey results, or NPS feedback dumps. Ask it to identify themes, extract pain points, or generate insight summaries.


    Need this set up for your team?
    Talk to Will →
  • Claude AI for Resume Writing and Job Search in 2026

    Job searching is one of the most stressful, time-consuming activities most people undertake — and Claude AI can compress weeks of effort into hours. This guide covers how to use Claude for every stage of the job search: resume optimization, cover letter generation, interview prep, LinkedIn rewriting, and salary negotiation coaching.

    1. Resume Optimization: ATS and Human-Ready

    Most resumes fail before a human ever reads them — they’re filtered out by Applicant Tracking Systems (ATS) that match keywords from the job description. Claude helps you solve both problems.

    Step 1 — ATS keyword matching:

    Here is a job description: [paste full JD]. Here is my current resume: [paste resume]. Identify the top 10 keywords and phrases from the job description that are missing from my resume but that I can honestly claim based on my experience. Then suggest specific edits to my bullet points to incorporate those keywords naturally.

    Step 2 — Impact bullet rewrites:

    Rewrite these resume bullet points using the formula: [Strong action verb] + [specific task/project] + [quantified result]. Use numbers wherever possible. If I haven’t provided metrics, suggest what metrics I should try to add and placeholder them with [X%] format. [paste your bullets]

    2. Cover Letters That Don’t Sound Like AI

    The most common mistake when using AI for cover letters: asking Claude for “a cover letter” without sufficient context. The result is generic. The fix is specificity.

    Write a cover letter for [Job Title] at [Company]. Key things I want to highlight: [2-3 specific accomplishments most relevant to this role]. What genuinely excites me about this company: [specific reason — not “I’ve always admired your company”]. My biggest differentiator for this role: [what makes you the right person]. Tone: [confident and direct / warm and enthusiastic / formal]. Length: 3 paragraphs. Do not start with “I am writing to express my interest.”

    3. LinkedIn Profile Rewriting

    Your LinkedIn headline and About section are your digital first impression. Claude can rewrite both for maximum impact:

    Rewrite my LinkedIn About section. I want it to: (1) immediately communicate what I do and the value I create, (2) speak to my target audience of [hiring managers at X type of company / recruiters in Y industry], (3) include relevant keywords for [your field], (4) end with a clear call to action. Current About section: [paste]. My target role: [role]. My top 3 differentiators: [list].

    4. Interview Preparation

    Claude is an excellent mock interviewer. Give it the job description and your resume, then:

    • “Generate 15 interview questions this company is likely to ask for this role, including 5 behavioral questions using the STAR format.”
    • “I answered [question] with [your answer]. How can I improve this response? What’s missing?”
    • “What questions should I ask the interviewer at the end of this interview that would demonstrate strategic thinking?”
    • “Help me prepare a 2-minute ‘Tell me about yourself’ that connects my background to this specific role.”

    5. Salary Negotiation Coaching

    Claude won’t tell you what a specific company pays (it doesn’t have that data in real time), but it’s a powerful negotiation coach:

    I received an offer of [amount] for [role] at [company type] in [city]. My competing offers and market research suggest [range]. Help me: (1) decide whether to negotiate and what my realistic target is, (2) draft a negotiation email that is confident but maintains the relationship, (3) prepare for the most common pushbacks and how to respond.

    Frequently Asked Questions

    Is using Claude to write a resume or cover letter ethical?

    Yes. Using AI as a writing and editing tool is no different than using a career coach, resume service, or spell checker. The key is that the content reflects your actual experience and skills — Claude helps you express them more effectively, not fabricate them.

    Will recruiters know I used AI to write my resume?

    Not if you use Claude correctly. Generic AI output is obvious — but Claude can match your voice, incorporate your specific accomplishments, and produce content that reads as authentically yours if you give it proper context and edit the output.


    Need this set up for your team?
    Talk to Will →
  • Claude AI for Sales: Prospecting, Outreach, and Closing

    Sales is one of the highest-leverage use cases for Claude AI — and one of the most underserved in terms of dedicated content. This guide covers the specific workflows where Claude generates the most value for sales professionals: prospecting research, outreach sequences, call prep, proposal drafting, and objection handling.

    Why Sales Professionals Get Outsized Value from Claude

    Sales is fundamentally about communication quality and research depth — two areas where Claude excels. A well-researched outreach email dramatically outperforms a generic one. A tailored proposal beats a template. Claude lets individual sales reps operate at the research and writing capacity of a team.

    1. Prospect Research in Minutes

    Before Claude, deep prospect research took 30-60 minutes per account. Now it takes five. Paste a prospect’s LinkedIn profile, company about page, recent press releases, or earnings call transcript into Claude and ask:

    Based on this information about [Company Name], identify: (1) their top 3 likely business priorities this quarter, (2) potential pain points that my solution [describe your product] addresses, (3) 2-3 specific talking points for an initial outreach, (4) any recent news or initiatives I should reference to show I did my homework.

    2. Cold Email and Outreach Sequences

    Claude writes cold emails that don’t sound like cold emails. The key is specificity. Generic prompts produce generic emails. Specific inputs produce personalized outreach that gets replies.

    Prompt template:

    Write a cold email to [Name], [Title] at [Company]. Context: [1-2 sentences about what the company does and what’s happening with them]. My solution: [what you sell and the specific problem it solves]. Goal: get a 20-minute discovery call. Tone: [direct and confident / warm and curious / peer-to-peer]. Length: under 100 words. Include a clear call to action. Do not start with “I hope this email finds you well.”

    Ask Claude to write a 3-email sequence — initial outreach, first follow-up, final follow-up — each with a different angle and hook.

    3. Discovery Call and Meeting Prep

    Before any important call, feed Claude everything you know about the prospect and ask for:

    • 5 discovery questions tailored to their specific situation
    • Likely objections they’ll raise and responses
    • Relevant case studies or social proof to mention
    • A 60-second value proposition tailored to their industry

    4. Proposal and SOW Drafting

    Proposals are time-consuming and inconsistent when written from scratch. Give Claude your notes from discovery calls and a proposal template, and ask it to:

    • Draft a custom executive summary that reflects the prospect’s stated priorities
    • Write the problem/solution section using their own language from discovery
    • Generate pricing narrative and ROI framing
    • Suggest relevant case studies to include

    5. Objection Handling Prep

    Prompt: “I sell [product] to [target buyers]. List the 10 most common objections prospects raise and write a concise, confident response to each. Focus on redirecting rather than arguing, and always tie back to the prospect’s stated goals.”

    Use this to build an objection bank your whole team can reference.

    6. CRM Note Writing and Deal Updates

    After calls, paste your rough notes into Claude: “Clean up these call notes into a structured CRM entry with: summary, key pain points identified, next steps, decision timeline, and stakeholders involved.” This alone saves 10-15 minutes per call.

    Frequently Asked Questions

    What is the best Claude plan for sales professionals?

    Claude Pro ($20/month) works for individual reps. Teams should explore Claude for Teams or Enterprise plans, which offer shared Projects where team prompts, voice guidelines, and playbooks can be stored centrally.

    Can Claude connect to my CRM?

    Not natively, but Claude can connect to your CRM via MCP (Model Context Protocol) integrations, or you can paste prospect data directly into Claude for analysis and draft generation.


    Need this set up for your team?
    Talk to Will →
  • Claude AI for Real Estate: Listings, Analysis, and Client Communication

    Claude AI has become one of the most useful tools in a real estate professional’s toolkit — yet almost no dedicated content exists explaining how to use it effectively. This guide covers the specific workflows, prompts, and use cases that are generating real results for agents, brokers, and investors in 2026.

    Why Claude Works Especially Well for Real Estate

    Real estate is a document-heavy, communication-intensive, data-dependent business. Claude excels at exactly these three things. Its 200,000-token context window means it can process an entire transaction’s worth of documents in a single session. Its writing quality is among the best available for generating compelling, accurate listing copy. And its analytical capabilities let agents quickly synthesize market data without needing to be data scientists.

    1. Writing Property Listings That Convert

    Listing copy is one of the most time-consuming parts of an agent’s week — and one of the easiest to delegate to Claude. The key is giving Claude the right inputs.

    Prompt template for listing descriptions:

    Write a compelling MLS listing description for a property with these details: [bedrooms/bathrooms/sqft], [neighborhood name and its key characteristics], [standout features: kitchen remodel, original hardwood floors, mountain views, etc.], [recent upgrades], [lot details if relevant], [nearby amenities]. Target buyer: [first-time buyers / move-up buyers / luxury buyers / investors]. Tone: [warm and inviting / crisp and professional / neighborhood-focused]. Length: 250 words.

    Claude will generate multiple variations if you ask — try “give me three different versions, each emphasizing a different feature” to find the one that matches the property’s strongest selling points.

    2. Comparative Market Analysis (CMA) Assistance

    Claude can’t pull live MLS data, but it’s extremely useful for interpreting comp data you already have. Paste in a spreadsheet of comps (as text or CSV) and ask Claude to:

    • Identify price-per-square-foot trends
    • Flag outlier sales that may skew averages
    • Draft the narrative section of a formal CMA report
    • Generate price range recommendations with reasoning
    • Explain the analysis to a seller in plain language

    Prompt: “Here are 8 comparable sales from the past 90 days in the target neighborhood [paste data]. The subject property is [details]. Analyze the comps, identify the 3-4 most relevant, explain any price adjustments needed, and write a 2-paragraph narrative for a seller CMA presentation.”

    3. Client Communication: Letters, Emails, and Follow-Ups

    Claude handles the full spectrum of real estate correspondence:

    • Buyer tour follow-ups: “Draft a follow-up email to a buyer couple who toured 4 homes today. They loved home A and B but had concerns about the school district for home B. Next steps: schedule second showing of home A.”
    • Seller update letters: Summarize showing feedback, market activity, and recommended price adjustments in a professional letter format
    • Offer negotiation scripts: “Help me draft a counteroffer letter that maintains our price but offers a faster close and rent-back period”
    • Just-listed neighbor letters: Personalized mailers for new listings
    • Market update newsletters: Monthly or quarterly client communications

    4. Property Research and Due Diligence

    Upload inspection reports, HOA documents, title reports, or disclosure packages to Claude and ask it to:

    • Summarize key findings in plain language
    • Flag potential red flags or issues requiring follow-up
    • Extract specific items (HOA fees, special assessments, deferred maintenance)
    • Draft questions for the listing agent based on disclosure issues

    5. Social Media and Marketing Content

    Real estate agents who consistently post valuable content on social media generate more referrals. Claude can maintain that cadence without eating your week:

    • Instagram captions for listing photos
    • LinkedIn posts about market conditions
    • Facebook neighborhood guides
    • “Just sold” announcement copy
    • Market stat graphics (Claude writes the copy; you add the visuals)

    Getting Started: The Right Claude Plan for Real Estate Agents

    The free tier works for occasional use, but active agents will quickly hit rate limits. Claude Pro at $20/month is the right starting point — it includes Projects, which lets you store your brokerage’s voice guidelines, neighborhood knowledge, and standard templates so Claude uses them automatically across sessions. Heavy users who process lots of documents will want to consider the Max plan.

    Frequently Asked Questions

    Can Claude access MLS data?

    No. Claude cannot connect to MLS databases directly. However, you can paste or upload comp data, market reports, or property information and Claude will analyze and synthesize it effectively.

    What is the best Claude plan for real estate agents?

    Claude Pro ($20/month) is the right starting point. It includes Projects — which lets you store brokerage-specific context, tone guidelines, and templates that Claude uses automatically.

    Can Claude write listing descriptions?

    Yes, and it’s one of Claude’s strongest use cases. Provide property details, target buyer type, and desired tone, and Claude will generate professional listing copy in seconds. Always review and personalize before submitting to MLS.


    Need this set up for your team?
    Talk to Will →
  • Benjamin Mann: GPT-3 Architect and Head of Anthropic Labs

    Benjamin Mann is a co-founder of Anthropic and co-head of Anthropic Labs, the research division responsible for Claude’s most advanced capabilities. His path to one of the most consequential AI roles in the world ran through Columbia University, Google, and OpenAI — and yet, as of 2026, virtually no public biography of him exists. This profile fills that gap.

    Education: Columbia University

    Benjamin Mann studied computer science at Columbia University in New York City, graduating with a strong foundation in systems and algorithms. Columbia’s CS program has produced a notable number of AI researchers and startup founders, and Mann followed that tradition directly into product engineering and research roles.

    At Google: Waze Carpool

    After Columbia, Mann worked at Google as a senior engineer, where he contributed to Waze Carpool — Google’s carpooling feature built on top of the Waze navigation platform. The work gave him experience operating at massive scale and shipping consumer-facing products with millions of users. It also represented a departure from pure research: Mann has always moved between applied engineering and fundamental AI work.

    At OpenAI: Architecting GPT-3

    Mann joined OpenAI and became one of the core engineers behind GPT-3 — the 175-billion parameter language model that launched the modern AI era when it was released in 2020. While Tom Brown served as lead engineer, Mann was a key contributor to the architecture and training infrastructure that made GPT-3 possible. He is listed as a co-author on the landmark paper “Language Models are Few-Shot Learners.”

    Co-Founding Anthropic

    In 2021, Mann joined Dario Amodei, Daniela Amodei, and five other OpenAI researchers in founding Anthropic. The co-founders shared a commitment to building AI that is safe, interpretable, and beneficial — and a belief that a dedicated safety-focused lab was necessary to pursue that goal seriously.

    Role at Anthropic: Co-Leading Anthropic Labs

    Mann co-leads Anthropic Labs alongside Mike Krieger, the Instagram co-founder who joined Anthropic in 2023. Anthropic Labs serves as the research and experimentation arm of the company — the team responsible for exploring Claude’s frontier capabilities, running novel experiments, and developing the next generation of features before they ship to users.

    The pairing of Mann (deep AI research background) with Krieger (consumer product expertise at scale) reflects Anthropic’s increasing emphasis on making frontier AI research accessible and useful to everyday users, not just researchers and developers.

    Public Profile and Media

    Mann appeared on Lenny’s Podcast in July 2025, one of the rare public interviews he has given. The episode generated significant interest in the AI research community, touching on Anthropic’s product philosophy, the future of AI assistants, and the practical challenges of building systems that are both powerful and safe. Despite this, he remains one of the least-profiled founders of a major AI company.

    Frequently Asked Questions

    What is Benjamin Mann’s role at Anthropic?

    Benjamin Mann co-leads Anthropic Labs alongside Mike Krieger. Anthropic Labs is the research and experimentation division responsible for Claude’s frontier capabilities.

    Where did Benjamin Mann work before Anthropic?

    Mann worked at Google (on Waze Carpool) and OpenAI (as a core engineer on GPT-3) before co-founding Anthropic in 2021.

    Did Benjamin Mann work on GPT-3?

    Yes. Mann was a key architect and contributor to GPT-3 at OpenAI, and is a co-author on the landmark paper “Language Models are Few-Shot Learners.”


    Need this set up for your team?
    Talk to Will →
  • Sam McCandlish: From Theoretical Physics to CTO of Anthropic

    Sam McCandlish is the Chief Technology Officer and Chief Architect of Anthropic, the AI safety company behind Claude. Before helping build one of the most important AI companies in the world, he was a theoretical physicist studying complex systems. His journey from physics to AI is one of the more unusual and compelling founding stories in Silicon Valley — and as of 2026, no dedicated biography of him exists anywhere online.

    Academic Background: Theoretical Physics

    McCandlish earned his PhD in theoretical physics from Stanford University, where he specialized in the mathematics of complex systems — how large numbers of interacting components give rise to emergent behaviors. After Stanford, he completed a postdoctoral fellowship at Boston University, continuing his work in theoretical physics before pivoting to machine learning research.

    The leap from physics to AI is less dramatic than it appears. Theoretical physicists are trained in the same mathematical frameworks — statistical mechanics, dynamical systems, information theory — that underlie modern machine learning. Many of the most important AI researchers of the past decade came from physics backgrounds.

    At OpenAI: Discovering Scaling Laws

    McCandlish joined OpenAI as a researcher and quickly became interested in a fundamental question: how does AI model performance scale with compute, data, and parameters? The answer would have enormous practical implications for how AI companies allocate research budgets and design training runs.

    Working alongside Jared Kaplan (now Anthropic’s Chief Science Officer) and others, McCandlish co-authored the 2020 paper “Scaling Laws for Neural Language Models” — arguably the most practically important paper published in AI in the last decade. The paper demonstrated that AI performance improves predictably and smoothly as models get larger, datasets get bigger, and compute budgets increase. This insight transformed how AI labs plan and prioritize research.

    Co-Founding Anthropic

    In 2021, McCandlish joined six other OpenAI researchers — including Dario Amodei, Daniela Amodei, Jared Kaplan, Chris Olah, Tom Brown, and Jack Clark — in founding Anthropic. The group shared concerns about the safety implications of increasingly powerful AI systems and believed that a dedicated safety-focused lab was needed.

    Role at Anthropic: CTO and Chief Architect

    As CTO and Chief Architect, McCandlish is responsible for Anthropic’s technical direction — the architecture decisions, training methodologies, and infrastructure choices that determine what Claude can do and how efficiently it can be trained. His physics background gives him an unusual ability to reason about scaling and complexity at the systems level.

    Net Worth and Equity

    Forbes has estimated McCandlish’s net worth at approximately $3.7 billion as of early 2026, reflecting his co-founder equity stake in Anthropic at its current valuation. As Anthropic moves toward a potential IPO (targeting 2026), those figures could shift substantially.

    Frequently Asked Questions

    What is Sam McCandlish’s background?

    Sam McCandlish has a PhD in theoretical physics from Stanford University and completed a postdoctoral fellowship at Boston University before pivoting to AI research.

    What is Sam McCandlish’s role at Anthropic?

    McCandlish is the Chief Technology Officer (CTO) and Chief Architect of Anthropic, responsible for the company’s technical direction and AI architecture decisions.

    What research is Sam McCandlish known for?

    McCandlish co-authored the landmark 2020 paper “Scaling Laws for Neural Language Models,” which demonstrated that AI performance improves predictably with scale and transformed how AI labs plan research.


    Need this set up for your team?
    Talk to Will →
  • Tom Brown: The GPT-3 Engineer Who Co-Founded Anthropic

    Tom Brown is one of seven co-founders of Anthropic and the engineer most responsible for making GPT-3 a reality. His trajectory — MIT graduate, YC founder, OpenAI research lead, Anthropic co-founder — traces the arc of the modern AI industry itself. Yet as of 2026, no Wikipedia page exists for him, and no dedicated biography has been published anywhere on the internet. This profile aims to change that.

    Early Life and Education

    Tom Brown earned a Master of Engineering from the Massachusetts Institute of Technology, studying at the intersection of computer science and brain/cognitive sciences. This dual focus — computational systems and human cognition — would later prove formative in his approach to large language model design.

    Before OpenAI: Co-Founding Grouper

    Before entering the AI research world full-time, Brown co-founded Grouper, a social networking startup that went through Y Combinator (YC). Grouper connected strangers for group social outings — an early experiment in algorithmically-mediated human connection. The startup experience gave Brown practical exposure to building products at speed, a skill that would prove valuable in AI research environments.

    At OpenAI: Leading GPT-3 Engineering

    Brown joined OpenAI as a research scientist and quickly became central to the organization’s most ambitious project: building a language model large enough to demonstrate emergent general intelligence. He served as the lead engineer on GPT-3, the 175-billion parameter model that, when released in 2020, fundamentally changed the world’s understanding of what AI could do.

    GPT-3 was the first AI model to reliably produce human-quality prose, write working code, translate languages, and answer questions — all from a single model, with no task-specific training. The technical paper describing GPT-3, “Language Models are Few-Shot Learners,” listed Brown as the lead author. It has been cited over 60,000 times and remains one of the most influential papers in the history of machine learning.

    Leaving OpenAI: The Anthropic Founding

    In 2021, Brown was among seven senior OpenAI researchers who left to co-found Anthropic alongside Dario Amodei (CEO), Daniela Amodei (President), Jared Kaplan, Chris Olah, Sam McCandlish, and Jack Clark. The departure was motivated in part by disagreements about how quickly OpenAI was commercializing its technology relative to its safety research — concerns that have only grown more prominent as the AI industry has accelerated.

    Anthropic was incorporated as a public benefit corporation (PBC), a legal structure that formally embeds the mission of responsible AI development into the company’s governing documents.

    Role at Anthropic: Head of Core Resources

    At Anthropic, Brown leads Core Resources — the team responsible for the fundamental infrastructure, compute, and technical operations that make Claude’s training possible. In an AI company, compute is the most critical resource: access to sufficient GPU clusters determines what models can be trained and how quickly. Brown’s role sits at the intersection of infrastructure engineering and research operations.

    Anthropic’s Growth and Valuation

    Since its founding, Anthropic has raised billions from investors including Google, Amazon, Spark Capital, and others, reaching a valuation of approximately $61 billion as of early 2026. Claude — Anthropic’s AI assistant — has become one of the most widely used AI tools in the world, particularly among developers and enterprise users. As a co-founder, Brown holds a meaningful equity stake in the company.

    Frequently Asked Questions

    Where did Tom Brown go to school?

    Tom Brown earned an M.Eng from MIT in computer science and brain/cognitive sciences.

    What is Tom Brown’s role at Anthropic?

    Tom Brown leads Core Resources at Anthropic — the team responsible for compute infrastructure and technical operations supporting Claude’s training.

    Did Tom Brown work at OpenAI?

    Yes. Brown was a research scientist at OpenAI and served as the lead engineer on GPT-3, the 175B parameter model released in 2020. He is the lead author on the foundational GPT-3 paper “Language Models are Few-Shot Learners.”

    Why did Tom Brown leave OpenAI?

    Brown, along with six other OpenAI researchers, co-founded Anthropic in 2021 due to concerns about the pace of AI commercialization relative to safety research.


    Need this set up for your team?
    Talk to Will →