The a16z Show

The Skype transaction is a historical example, not a current public-stock opportunity: investors reportedly sold it to Microsoft (MSFT) about 18 months later for four times their purchase price. The discussion provides no current investment thesis or price target for MSFT, eBay (EBAY), or Cisco (CSCO), so it does not support a buy recommendation for those stocks.

The discussion offers no high-conviction stock trade: Peloton (PTON) is mentioned only as a possible HSA/FSA-eligible purchase, with no evidence on sales or outlook. Treat preventive-health financing, consumer diagnostics, and wellness products as themes to research—not investment recommendations—since no company financials, valuations, or price targets are provided.

• Treat preventive diagnostics, medical imaging, genomics, and AI-enabled health analysis as research themes, not ready-to-buy trades: no public tickers, valuations, or business results are provided.
• Avoid investing on the basis of Function Health, GLP-1 support services, or longevity supplements alone; the discussion names no investible public companies and offers no evidence on commercial performance.
• Regard emerging longevity interventions such as plasmapheresis and peptides as especially speculative, with safety, efficacy, and regulatory questions unresolved.

Salesforce (CRM) is the clearest public-market opportunity discussed: its AI, Slack, and enterprise distribution could support growth, especially if outcome-based pricing offsets pressure on software-seat counts. Treat this as a long-term thesis, not a short-term trade; the discussion gives no price target, and execution and acquisition valuations remain key risks. Avoid treating Snowflake (SNOW), DocuSign (DOCU), Twilio (TWLO), or nCino (NCNO) as buys based solely on Salesforce Ventures’ investments—the discussion provides no current outlook or valuation case. Be cautious about highly valued private AI and CRM startups, where limited exit options and uncertain durable customer value pose significant risks.

• Treat Figma (FIG) as a company to monitor for AI-driven finance efficiencies, but look for evidence of improved margins or stronger growth before drawing a bullish investment conclusion.
• Consider the broader finance-automation theme selectively: prioritize companies demonstrating reliable data integration, audit trails, and sustained customer adoption; these insights do not identify a specific stock to buy.

• Shopify (SHOP): Monitor whether merchants see measurable growth from AI-agent discovery and transactions; treat this as a developing opportunity, not a confirmed investment case.
• Amazon (AMZN): Watch whether it opens or restricts agent access to listings, purchases, and customer data, which could shape its position in AI-led shopping.
• Apple (AAPL): Track whether Apple can pair its privacy protections with competitive AI capabilities; privacy may help differentiate its offerings, but utility will matter too.
• For private or early-stage AI agents, prioritize evidence of reliable task completion, user trust, and retention over feature breadth; the discussion provides no financial data or explicit buy recommendations.

The discussion identifies sleep technology and AI-assisted focus tools as themes to monitor, but names no investable companies or evidence of commercial success. Eight Sleep, AG1, and Roka are mentioned only as products Huberman uses or has worked with—not as investment recommendations, and no tickers or price targets are provided. Avoid acting on these anecdotes until there is evidence of product effectiveness, adoption, and business performance.

• Treat industrial robotics and factory automation as a long-term theme, prioritizing companies with proven deployments and clear customer cost savings.
• Tutor Intelligence is private and has no public ticker; assess its valuation, operating costs, and reliance on human support before considering private-market exposure.
• The discussion provides no actionable stock recommendation, price target, or timeframe for FANUC, ABB, or Unitree.

Prioritize NVIDIA (NVDA) as an example of AI-driven growth, but treat its exceptional performance as uncertain—not a forecast for other companies. For software and SaaS investments, favor businesses with durable customer relationships, network effects, proprietary data, switching costs, or scale, and assess whether AI could pressure pricing or customer acquisition. Evaluate Broadcom (AVGO) and TransDigm (TDG) by their cash generation and ability to manage debt through adverse conditions; leverage can accelerate growth but also magnify risk. Across companies, focus on sustainable free cash flow, disciplined reinvestment, and reasonable valuations rather than acquisition counts or optimistic growth assumptions; no specific price targets or timing were provided.

Cosign is an early-stage private company to watch, but the discussion provides no valuation, financial results, or way to assess access to shares. Track whether its AI-agent integrations generate useful matches and attract job seekers and employers; rough onboarding and product maturity remain key risks. The broader AI-enabled professional-networking theme is promising but unproven, with no specific public-market ticker, price target, or investment timeframe identified.

• Consider private legal-AI companies such as EVE only as a high-risk, early-stage opportunity: its software aims to help plaintiff firms handle more cases, but no valuation, financial results, or investment terms were provided.
• Before investing in legal-AI software, look for evidence across multiple firms that it improves case outcomes, drives renewals and expansion, and can automate workflows beyond isolated tasks.
• Treat the reported increase from about 100 to 500 clients per attorney at one firm as an unverified company example, not a typical or guaranteed result; adoption, human oversight, and sensitive-data risks remain.

• Focus on publicly traded companies enabling U.S. factory and supply-chain modernization, including suppliers of critical components, factory software, and production equipment; prioritize firms with repeat customer demand and proven production quality.
• Track grid modernization, battery storage, and power electronics as long-term beneficiaries of rising data-center and factory power needs, while accounting for permitting and construction delays.
• Treat TSLA, PLTR, BA, HON, JOBY, ACHR, RIO, and JPM as examples tied to these themes—not buy recommendations; the discussion offered no valuations, price targets, or near-term catalysts.
• Approach private industrial and defense companies such as Anduril, Hadrian, and Mariana Minerals cautiously: capital needs, execution, and years-long scaling timelines create substantial risk.

• The discussion offers no high-conviction public-stock trade: it provides no buy/sell calls, valuation analysis, or price targets for UNH, CVS, CI, or AMZN.
• Treat criticism of large healthcare companies as a long-term business-model risk, and monitor whether transparency rules, direct contracting, and new competitors pressure their economics.
• Cost Plus Drugs and its modular manufacturing plans are promising but private; watch for verified customer growth, hospital contracts, FDA approvals, and pod sales rather than relying on management ambitions.

Monitor Alpha School for independently verified learning outcomes and campus-level profitability as it expands; its reported 50 campuses alone do not establish that the model scales successfully. Treat AI-enabled education and practical entrepreneurship learning as promising themes, but seek evidence of measurable student gains, paying demand, and sustainable economics before investing. The discussion names no public securities or investable terms, so it does not support a specific buy recommendation.

• Focus on the AI computer-use agent theme, especially solutions automating repetitive procurement, finance, and sales workflows across existing ERP and CRM systems.
• Watch for industry-focused startups that can integrate, deploy, and support these tools; prioritize evidence of reliable automation, human approvals, and audit trails.
• Treat OpenAI as a prominent private-sector example, not a directly investable opportunity; the discussion provides no public tickers, price targets, or investment timeframe.

Favor enterprise AI opportunities, especially agents automating costly workflows in legal, healthcare, life sciences, and consulting; prioritize companies with deep industry expertise, clear customer demand, and reliable human review.
Treat AI coding as the clearest near-term example of agent-driven software change, but view the cited market estimates as broad industry opinions—not valuations or buy targets for Replit, Cursor, Windsurf, Lovable, Cognition, or GitHub.
Before investing in any AI company, look for proven product-market fit and durable customer distribution; the discussion provides no specific price targets or company-level buy recommendations.

• Treat AI as a promising long-term theme, but avoid buying on the label alone; prioritize companies with strong customer retention, proprietary data, deep workflow integration, and products meaningfully better than alternatives.
• Watch AI coding tools and agent-based services for opportunities, but recognize that competition and the durability of small-team businesses remain uncertain.
• Monitor crypto’s potential role in agent-to-agent payments and AI-generated entertainment as emerging themes, not established investment opportunities; no specific stocks, tokens, price targets, or timelines are recommended.

• AMZN is the clearest high-conviction way to participate in AI infrastructure: AWS grew 36.7% year over year, and its 2026 capacity is sold out, with much of 2027 also committed.
• Amazon plans about $220 billion in 2026 capital spending, so monitor AWS growth and free cash flow to see whether the investment is earning adequate returns.
• NVDA remains positioned to benefit from strong AI-computing demand, but track competition from Amazon’s Trainium and other custom chips, which could pressure third-party chip demand.
• Treat Anthropic and OpenAI as private-company exposure, not publicly traded stock opportunities; the cited investment figures were not confirmed.

• Track companies enabling practical AI adoption, stablecoin payments, and asset tokenization, but treat these as themes to research rather than direct buy recommendations; no tickers, price targets, or timelines were provided.
• Be cautious with businesses dependent on website referrals, advertising, or human attention, as AI could redirect traffic and weaken those models.
• Treat META and NVDA as companies to monitor—not buy calls—because the discussion offered no valuation analysis or stock targets.
• VR, AI-agent networks, and frontier hardware are longer-term, high-risk themes; the speakers noted adoption, technical, and regulatory uncertainty.

• Focus on the AI document-processing and vertical-AI theme, especially tools that improve measurable workflows in document-heavy fields like healthcare, legal, finance, and accounting.
• Treat Reducto, Cursor, Harvey, Rogo, and Basis as private-company watchlist names, not directly investable public stocks based on the information provided.
• Before investing in public companies pursuing this theme, look for proven accuracy on customers’ real-world documents, reliable error checks, and evidence of recurring usage and cost-effective scale.