• The episode reported that OpenAI’s round was reduced from $30B to $20B, with about $11B committed, and its valuation from $1.4T to $1.25T; the hosts said this alone did not establish that AI demand was collapsing, but argued that demand would determine whether financing stress persisted or exposed leveraged infrastructure such as CoreWeave. 20VC with Harry Stebbings Anthropic’s bookings growth was described as slowing quarter over quarter even as absolute bookings rose; speakers pointed to discounting and cheaper models as sources of pricing pressure, while saying available signals did not show that demand itself had declined. 20VC with Harry Stebbings
• A host said switching models in his B2B AI recruiting app reduced costs by 72% for the same workflow at higher quality; the investment wager for early AI products is whether unit economics improve before their funding runs out. 20VC with Harry Stebbings
• DoorDash was reported to receive only several hundred agent-initiated orders against 3 million daily deliveries, but the hosts argued agents could still bypass marketplaces by finding direct restaurant pickup or delivery options, putting platform intermediation at risk. 20VC with Harry Stebbings
• An investment debate compared a $30B inference provider, a $70–80B coding-agent company and a $3B AI ITSM app. One investor saw a clearer potential 10x in the ITSM opportunity but questioned whether it expands beyond ServiceNow’s existing market; coding agents were associated with near-term enterprise spending, while inference raised competition and commoditization concerns. 20VC with Harry Stebbings
• Cohere’s round was described as potentially including up to $1.14B from the Canadian government as a sovereign-AI investment; speakers noted that Cohere had not disclosed revenue and questioned whether government support could translate into private-sector adoption. 20VC with Harry Stebbings
• Physical Intelligence was described as having relatively little revenue while reportedly discussing a $20B valuation. The hosts praised recent robotics-model capabilities but cautioned that deployment takes longer and the business model is less developed; they also saw potential strategic interest from foundation-model companies seeking to expand into physical work. 20VC with Harry Stebbings
• The hosts warned that an open-source Chinese penetration-testing tool, initially described as defensive, could be repurposed for attacks at scale using LLM agents, raising concerns about risks to critical infrastructure. 20VC with Harry Stebbings

• Trajectory released a one-click platform that uses production-agent traces and a customer’s definition of what “good” looks like to train, evaluate, and deploy models together, then sends deployed models back into production to gather new traces. It targets AI-native companies with an established agent harness; Harvey and Rogo are named users, as is larger company Nvidia. Sequoia Capital
• Trajectory’s founder says Harvey used its legal-domain expertise and legal-agent benchmark to train a model whose accuracy rose from around 4% for an open-source model to around 18%, at six times lower cost than frontier models; he noted substantial room to improve. For Rogo, he said the tailored model is several times more token-efficient, cheaper, and faster while improving on tasks Rogo values. Sequoia Capital
• Trajectory says it is advancing a group-size-one version of GRPO: learning from one behavior across 10,000 trajectories from different tasks, with the stated aim of removing the need to replay tasks in a constructed environment. For reward labeling, it proposes having customers rubric-score 100 representative traces from 10,000, then using a metagrader to generalize those judgments; the founder said this approach is not perfect. Sequoia Capital
• The founding team’s relevant prior work included learning from production edits and retries at Windsurf, robotics at DeepMind, and using artist feedback for multimodal models at Apple; the founder described real-world feedback as the shared challenge of improving models. Sequoia Capital

• Agentic trading: Robinhood says more than 150,000 customers opened agentic-trading accounts within a few months of launch, with about 30 million tool calls per day at its last reported count. Its in-account Robinhood Agents support recurring strategies and portfolio rebalancing, and an app store includes tools such as satellite-data analysis to count cars in retailers’ parking lots. No Priors: AI, Machine Learning… Tenev says the product aims to bring hedge-fund and quant-trading capabilities to individuals; accounts are separately funded and sandboxed, and he said agentic trading remained a small share of activity and was not imminently nearing double digits. No Priors: AI, Machine Learning…
• Mathematical AI: Harmonic aims to build mathematical superintelligence; Tenev said its Aristotle model was one of three to earn a gold medal at the International Math Olympiad, and that after its public release people used it on unsolved problems and mathematicians used it for new results. No Priors: AI, Machine Learning…
• Retail access to private startups: Robinhood describes Ventures Fund One as a publicly traded, NYSE-listed vehicle that raised capital from Robinhood customers and invests in late-stage private companies, including OpenAI. Fund Two is being developed with YC to provide exposure to seed and Series A companies, with Rich Aberman running it. No Priors: AI, Machine Learning… Tenev also said the SEC had directed FINRA to create a knowledge-based accreditation exam, which he viewed as positive but insufficient to make access low-friction. No Priors: AI, Machine Learning…
• Tokenized markets: Robinhood says it relaunched stock tokens with DeFi-enabled functionality alongside its new Robinhood Chain; it reports high developer activity and early experiments combining stock tokens with NFTs and memecoins. The company presents low-cost access to U.S. equities through non-custodial wallets in countries with weaker financial systems as a broader use case. No Priors: AI, Machine Learning…

• Figma finance teams are building workflow automations: a credit-and-collections employee rebuilt invoice tracking, cash collections, reconciliation, and credit checks; separately, an AI-assisted spend analysis joined data from multiple sources and suggested policy changes, with the CFO valuing a directionally useful result over perfect accuracy. a16z
• Figma’s CFO describes AI adoption as being in a “messy middle”: teams can prototype quickly, but a centralized CIO function is needed to identify successful tools and turn them into shared, durable foundations. a16z
• Figma uses the ability to “vibe code a product” as one diligence check, helping assess whether a product is real and how difficult it may be to build; the CFO says the product team still partners in the process. a16z
• For early-stage startups, a16z’s Ivon advises establishing reliable data access, integrity, and tracking before building reporting and metrics; he notes that startups may not track data at all and can calculate revenue or ARR incorrectly. a16z

• Women and non-binary investors rose from 9% to 18.8% of partner-plus roles at U.S. VC firms since Allra began tracking in 2018, but a senior title does not necessarily bring authority to lead deals, write checks, secure carry, or receive deal credit—all important to building a track record for future fundraises. TechCrunch
• Allra is tracking 303 women and non-binary investors who spun out to start funds one through three and 301 in principal-plus roles at firms with more than $1 billion in AUM. Among surveyed people who left firms, one in 20 said they felt pushed out; others described wanting to build a firm or gain ownership and better economics, despite the current difficulty of raising a fund. TechCrunch
• Paige Hendricks Buckner cited a PitchBook figure that women founders had received 1.1% of VC at the start of the year, and said most funding to teams with women founders was in AI, naming Anthropic and Scale AI as the top companies. She also cited data that firms with more than 30% female partners are 4.7 times more likely to invest in a female founder. TechCrunch
• Buckner said she has spoken with many people building VC firms who are using AI to scale operations before they have enough capital, making the cost of AI support a practical constraint. TechCrunch

• a16z says AI has compressed exploit development: its timeline puts the work at about three weeks for a professional in 2017, a week in 2023, and an afternoon with AI in 2025 @a16z. The article reports that in June 2026 a model turned a released Firefox patch into a working exploit in under an hour, while cautioning that lab results explain—but do not prove—field performance @asaf_ez.
• Early field signals include Google’s assessment that a zero-day developed by AI was in criminal hands and intended for mass exploitation, though disrupted before use; an AI agent chaining two zero-days to breach the Dutch Institute for Vulnerability Disclosure; and a government warning about AI-generated scripts targeting exposed Siemens controllers @asaf_ez.
• Patching remains slow: the 2026 Verizon DBIR reports a 43-day median to fully fix a known-exploited vulnerability @asaf_ez. a16z expects attacks to become viable against smaller organizations, and bets on a prolonged “worm era” for organizations without platform protection—not a grid or internet-wide collapse @asaf_ez. For systems where updates require physical work or are otherwise costly, it points to isolation, segmentation, network-level protection, and safe manual fallback @asaf_ez.

• AlphaFold is used by about 4 million researchers; Hassabis describes protein structure prediction as critical but only one part of drug discovery, with further work aimed at biochemistry, toxicity and ADME prediction. He also identifies Isomorphic as a drug-discovery spinout. Masters of Scale
• Hassabis calls closed-loop recursive self-improvement without human review the riskiest next phase, citing control challenges and the potential for increasingly hard-to-understand systems. He advocates stronger sandboxing, monitoring and formal safety bounds, and sees room for third-party AI evaluation and auditing companies, including red-teaming between labs before release. Masters of Scale
• He expects six to eight frontier or near-frontier model offerings, including Chinese providers, with competitive leads of roughly three to six months; open-source models may trail by six months to a year. He sees enterprises as able to choose among model strengths, and argues that countries and companies can build value on sensitive data or downstream applications such as healthcare rather than each trying to build a frontier model. Masters of Scale

• At its London Zero Click event, Profound announced Slides, in-product visual creation, a mobile app for launching agents, and access through email, Slack, and Teams; it described proactive, always-on cross-channel functionality as its “Aim” (“AI Marketer”) agent. Lightspeed Venture Partners
• Profound’s thesis is that personal agents will become customers, so brands must ensure agents can surface, compare, evaluate, and transact their offerings. Lightspeed Venture Partners The speaker cited Gartner’s prediction that agents will handle one in five online transactions by 2030, but said that could happen by 2028 or sooner. Lightspeed Venture Partners
• Profound aims to build a marketing workbench where marketers do their work, analogous to Salesforce for sales teams. Lightspeed Venture Partners

Sequoia is refocusing on high-conviction, selective VC, sunsetting its Arc accelerator and Scouts program, simplifying its fund and operating and investing teams, combining Early & Growth into one team, and closing its London office while saying it will support European founders from the Bay Area. @sequoia The firm says it has flattened its partnership, raised about $10 billion in new funds, strengthened its team, and moved away from a Monday partner-meeting cycle; its stated objective is quality and LP returns rather than scale. @sequoia YC CEO Garry Tan called the changes a winning “back to roots” strategy and praised Alfred Lin and Grady. @garrytan

DeepFinance launched as an ultra-long-horizon benchmark for high-finance work, with 21 problems based on real deal data and designed by experts with backgrounds at firms including Evercore, JPM and UBS and institutions including HBS and Wharton; each represents 20–40 hours of human work. @0interestrates It aims to evaluate end-to-end finance workflows involving assumptions and conflicting evidence, beyond financial-document QA or simple-model numerical checks. @0interestrates

Garry Tan characterized solving high finance as a path to AGI. @garrytan

• AI is compressing cyber offense: a16z says exploit development has shifted from days or months to minutes or hours, and attacks averaged seven days before vulnerability disclosure by 2025. It cites a campaign using cheap, open-weight-model agents in which 395 organizations across 48 countries were compromised, half of them schools. The article cautions that pre-disclosure attacks began before agentic AI; its claimed shift is widening who can exploit that window and speeding weaponization of known bugs. @asaf_ez
• The main risk is a patch-speed mismatch, not an expected collapse of the grid or internet: the median time to fully fix a known-exploited vulnerability was 43 days, while the article reports an AI model producing a working exploit in under an hour; only 26% of such vulnerabilities were fully fixed within a year. a16z expects worm-like outbreaks to hit systems that cannot be updated quickly, while saying it does not expect the grid or internet to go down. @asaf_ez
• Defense and thesis-monitoring signals: for systems where updates are costly or impractical, the article points to isolation, segmentation, network-level protection, and safe manual fallback. Its scorecard says the thesis would look more concerning if exploited-vulnerability counts exceed 2,000 in 2027, over half of exploited bugs are attacked on or before CVE publication by 2027, or median patch time exceeds 60 days in 2028. @asaf_ez

An unnamed startup’s growth was not linear: after a year of smoothly exponential growth, it stalled, then growth returned five months later @paulg. Paul Graham says the same company had a fall 2024 crisis that drove several months of revenue declines, and the later chart nearly obscures it—an example of how growth curves can hide difficult operating periods founders experience @paulg.

Garry Tan sees a bipartisan opportunity to curb Big Tech’s market power and urges Congress to open markets for startups, arguing that a few trillion-dollar companies’ control over distribution can keep new products—including AI applications—from reaching users and businesses; he says broader startup competition could lower costs and spread AI’s gains more widely. @garrytan

• A few hundred AI agents using a cheap open-weight model turned two recently disclosed bugs into a first PaperCut-server breach in under four hours; the campaign compromised 395 organizations in 48 countries, half of them schools, and the agents ignored a 28-country exclusion list. @asaf_ez a16z argues that AI is lowering the cost of every offensive step while defenders lag: the 2026 Verizon DBIR put the median time to fully fix a known-exploited vulnerability at 43 days, with only 26% fully fixed within a year. @asaf_ez
• a16z does not expect the grid or internet to collapse; it expects the heaviest impact on systems that cannot be patched quickly and smaller operators, with added risk from attacks spreading beyond their intended targets and more actors gaining advanced capabilities. @asaf_ez In healthcare, a16z reports confirmed ransomware attacks on providers grew about sixfold from 2018 to 2024; average US provider downtime per attack rose from 4 days in 2018 to 17 in 2021 and 18 in 2024. @a16z

• Capability shift: a16z reports that the longest cyber task a frontier model could reliably complete, measured in expert time, grew from 5.5 minutes in late 2024 to 40 minutes in early 2026 and 4.8 hours by April 2026; its article says cyber and coding task lengths have been doubling every 4–5 months. @a16z +1 It also cites field reports of AI-chained zero-days at a Dutch vulnerability-disclosure organization, a criminal AI-developed zero-day assessed by Google, and AI-generated scripts targeting exposed Siemens controllers. @asaf_ez
• Security implications: a16z says exploit development has fallen from a skilled person’s month to a few hours, while the median time to fully fix a known-exploited vulnerability is 43 days and only 26% are fully fixed within a year. @asaf_ez It expects cheaper attacks to reach smaller organizations and unsupported systems that were previously less attractive targets. @asaf_ez Its base case is a long, worm-like period of harm rather than the grid or internet going down, while flagging attack spillover and aggregation of capability against smaller critical operators as risks. @asaf_ez

YC W21 alum Tractian builds physical AI for asset-heavy industries, using proprietary sensors, software, and AI to monitor hundreds of thousands of assets in real time; it has crossed $100M in ARR, growing 90% year over year, and raised a $130M Series D at a $1.4B valuation. @ycombinator Tractian is moving from equipment monitoring toward factory autopilot, with AI changing the economics of industrial maintenance. @ycombinator

Garry Tan highlights Capy.ai’s “captains,” with communicating subthreads and subagents that coordinate and manage merge queues and CI, calling this a “huge gamechanger.” @garrytan

Reflex (YC W23) raised a $14M Series A; it builds custom software for companies’ operational needs, with a platform that takes a business problem to a working app using AI-written software and Reflex engineers to put it into production. @ycombinator The open-source framework has surpassed 29,000 GitHub stars, powers internal tools at about 30% of Fortune 500 companies, and has been used to build more than 1 million apps. @ycombinator

Standard Capital led Reflex’s $14M Series A. @daltonc +1 Reflex has evolved from an open-source, pure-Python web-app framework into a platform that takes business problems to working apps, with AI writing the software and Reflex engineers putting it into production. @ycombinator YC’s announcement reports 29,000+ GitHub stars, more than 1 million apps built, and internal tools at approximately 30% of Fortune 500 companies. @ycombinator Caldwell pitches the opportunity as replacing SaaS with customized software; this is an investment thesis, not evidence that enterprises have already replaced their SaaS. @daltonc

Martin Casado endorsed the view that formal methods should complement, not replace, guardrails, tests/evals, real-world use, and human judgment: verification can show that code matches a specification, but not that the specification captures user intent. @martin_casado +1 The linked post describes model checking (e.g., TLA+/TLC) as useful for checking system properties and finding bugs, while noting possible spec/code drift; theorem proving and bounded model checking have different effort and coverage tradeoffs. @poteto It also points to a Cursor plugins pstack /statechart skill for making implicit state machines legible and checking whether implementation matches intent. @poteto