The a16z Show

• Treat healthcare AI as a long-term theme, prioritizing companies that integrate into existing clinical workflows and demonstrate sustained clinician adoption and measurable improvements.
• Track AI-enabled drug discovery and clinical trials over the next five to 10 years, but favor companies with validated candidates and clinical progress over those making AI-generated discovery claims alone.
• OpenAI and Epic are private, and the discussion provides no public ticker, valuation, or price target—so no direct stock trade is identified.

• Consider Walmart (WMT) as a conditional relative beneficiary if AI shopping agents redirect purchases away from Amazon (AMZN), whose advertising-driven marketplace could face pressure; no timeline or price target is provided.
• Monitor Amazon (AMZN) for signs that agent-led shopping is reducing ad revenue or marketplace engagement, while recognizing that product discovery may sustain its model.
• For AI investments, favor companies that turn AI into easy-to-use, repeatable workflows and own trusted customer relationships; Town is a private company, not a publicly traded stock.

• EliseAI is privately held, with no public ticker; standard stock-market investors cannot buy its shares.
• Monitor vertical AI in housing and healthcare, but prioritize companies that prove reliable integration into real workflows and measurable customer savings.
• Before investing in this theme, look for evidence of customer retention, revenue growth, deployment costs, and safe handling of errors; the discussion provides no financial results or specific public-market trade.

• Zocdoc is private, so it has no directly investable public ticker; its AI and payer partnerships are worth monitoring for evidence of adoption and revenue growth.
• Alphabet (GOOGL/GOOG) has partnered with Zocdoc through Gemini, but no deal economics or usage figures were disclosed, so the partnership alone does not support a buy recommendation.
• Track healthcare-access and scheduling companies for measurable gains in completed bookings and payer integrations; the insights provide no public-stock price targets or near-term trade signals.

The insights provide no high-conviction, actionable stock recommendation: DDOG, UBER, CSCO, and MSFT are mentioned without a company-specific investment thesis, while Grepr and Anthropic are private and lack valuation or financing details.
For now, treat AI-assisted software development and observability-cost reduction as themes to monitor, not as buy signals; reported productivity and 90% cost savings are company anecdotes, not independently verified forecasts.

• Monitor Visa (V) and Mastercard (MA) for progress on agentic-payment standards that preserve token security and cardholder protections; the insights give no price targets or direct buy recommendations.
• Watch PayPal (PYPL) and Shopify (SHOP) for evidence that their wallets can connect consumers and merchants to AI-driven purchases, but treat this as an emerging opportunity rather than a proven earnings catalyst.
• Track payment-security infrastructure such as Very Good Security and Stripe as potential beneficiaries of agentic commerce, noting that both are private and the opportunity depends on adoption and clearer rules for authorization and disputes.

• Consider Meta (META) for public-market AI exposure, but treat its positive trajectory as a thesis to verify through actual product launches and business results; a rumored new open-weight model is unconfirmed.
• Monitor AI data, evaluation, and reinforcement-learning infrastructure as a potential growth theme, prioritizing providers that demonstrate measurable model improvements and strong quality controls.
• Track AI coding and enterprise applications for evidence of cost-effective performance on specific tasks; customized or open models may appeal to businesses seeking privacy and control.
• OpenAI, Anthropic, and several tooling startups are private, so they are not directly available as public-stock investments; the discussion offered no price targets or specific buy recommendations.

Do not panic-sell BTC or ETH: the discussion cited no evidence that current cryptography has been broken and recommended no immediate action for everyday holders. No actionable buy or sell signal was given for COIN. For longer-term exposure, consider the AI-enabled software-security theme, particularly tools for automated vulnerability scanning, but treat it as a broad opportunity rather than a specific stock recommendation.

Monitor AI inference and serving infrastructure for businesses that can demonstrate repeatable reductions in total deployment costs without sacrificing capability. Treat recursive self-improvement (RSI) and MakerMaker.ai as high-risk, long-term themes; MakerMaker.ai is private, and its early performance claims are not independently verified. Avoid acting on broad AI-chip or energy-efficiency claims from this discussion, as it provides no specific securities, price targets, or investment timelines.

• Consider AI safety and cybersecurity as long-term themes, focusing on model monitoring, interpretability, and infrastructure protection; no specific companies or price targets were identified.
• Look for pandemic-preparedness investments in defensive tools such as air purification, PPE, vaccines, and diagnostics, while treating dual-use technologies cautiously.
• Treat AI-enabled medicine and longevity as speculative, long-term opportunities: the potential is significant, but the discussion offered no reliable timeline or specific investment picks.

• NVIDIA (NVDA) has strong near-term chip demand, but weigh that against potential power and cooling bottlenecks and the risk that AI hardware needs may evolve.
• Favor AI companies with useful, specialized products, customer access, and proprietary feedback data; ElevenLabs is cited as an example, but it is private and no investment terms are provided.
• Be selective with SaaS stocks such as HubSpot (HUBS): AI could make some products easier to replace, while enterprise complexity and differentiated data may offer protection.

• Treat AI accelerator startups as high-risk: favor companies with proven customer demand, software support, funding, and a credible path to manufacturing and deployment; the discussion offered no specific stock picks.
• Consider optical networking a longer-term AI infrastructure theme, with broader in-package adoption projected for 2028–2029, subject to supply, reliability, and integration challenges.
• Track data-center power and cooling as potential bottlenecks and investment themes, including nuclear power, grid equipment, power conversion, and liquid cooling; prioritize firms able to deliver at scale.
• Don’t interpret the discussion as a buy or sell call on NVIDIA (NVDA), Intel (INTC), or Oracle (ORCL); it provided no stock recommendations or price targets.

• AWS demand is accelerating, with revenue around $169–170 billion, up 37%, and strong AI workload growth; monitor whether that growth generates returns on Amazon’s planned $220 billion 2026 spend before treating it as a buy signal.
• NVIDIA (NVDA) has a demand tailwind from Amazon’s plan to purchase 2 million GPUs over the next couple of years, though chip, power, and data-center constraints could delay deployment.
• Track data-center power and infrastructure as a research theme: access to electricity, construction capacity, and specialized equipment may determine how quickly AI demand becomes revenue.

Monitor Valon as a private-market opportunity in mortgage-servicing software, but verify its reported deal volume, efficiency gains, and path to high margins before investing. More broadly, watch for companies combining reliable industry-specific data, workflow software, and AI in regulated sectors; the discussion names no public-stock recommendation or price target. TOST and Harvey were examples, not actionable recommendations.

• Prioritize CrowdStrike (CRWD) and Palo Alto Networks (PANW) for monitoring: their potential integration with AI-driven testing and rapid defensive responses aligns with the cybersecurity shift expected over the next two years.
• Treat this as a sector theme, not a buy recommendation: the discussion provides no valuation analysis, price targets, or company-specific forecasts for either stock.
• Watch for demonstrated customer adoption, independently validated vulnerability discovery, and fast remediation; Armadin is private, and its performance claims are not independently verified.

• Prioritize differentiated AI software with defensible workflows, user context, or specialized audiences; the discussion highlights this as a stronger opportunity than undifferentiated model access, but names no specific public-stock buy.
• Track GOOGL for evidence that Gemini can convert its broad reach into paying users, and META for whether Muse gains traction while keeping AI serving costs manageable; neither has a stated price target.
• Monitor SHOP for growth in agent-enabled commerce, but wait for proof of repeat customer use and reliable economics before treating the theme as an investment case.
• Across consumer AI, focus on sustainable monetization, serving costs, retention, and user trust—not downloads or usage alone; the insights provide no valuation-based recommendations.

• Consider Palantir (PLTR) as an enterprise-AI implementation play, but weigh its commercial growth and customer adoption against valuation; the discussion gave no price target.
• Track Tesla (TSLA) for potential upside from autonomous driving and factory robotics, while recognizing that broader deployment may take years and the cited robotics inflection is an expectation within five years.
• Treat AI infrastructure and specialized enterprise software as long-term themes, but favor companies with demonstrable customer returns and adoption; supply constraints and slow integration are key risks.

• Favor the AI infrastructure and tooling theme—especially model routing, evaluation, security, and specialized models—as enterprises seek lower costs and less vendor lock-in; look for demonstrated savings and reliability before investing.
• OpenRouter was acquired by Stripe, so it is no longer a standalone investment opportunity; the deal highlights the strategic convergence of AI infrastructure, payments, and startup services.
• The discussion provides no specific stock recommendations, price targets, or timeframes for MSFT, PLTR, NVDA, or other public companies.

• Prioritize the vertical AI theme, especially procurement automation, but favor companies that demonstrate reliable end-to-end execution, handle exceptions, and deliver measurable customer savings—not just impressive demos.
• Treat Leo as a private-company example of this opportunity, not an available investment based on the information provided; monitor production adoption, customer value, and how much human review its agents still require.
• The discussion offers no actionable stock call, price target, or timeframe for CRM, SAP, or ORCL; evaluate incumbents’ distribution advantages against focused AI firms’ ability to automate workflows across systems.

• Treat DocsNet as an unproven private-company opportunity: the discussion provides no valuation, revenue, or customer data, so assess traction and infrastructure costs before considering investment.
• Watch the privacy, cybersecurity, and DDoS-protection sectors for companies that can demonstrate strong data controls and scalable infrastructure; the discussion offers no specific stock recommendation.
• Akamai (AKAM) and F5 (FFIV) are relevant public-company names, but no current investment case, price target, or timeframe is provided.