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👋 Hi, I’m Andre and welcome to my newsletter Data Driven VC which is all about becoming a better investor with data and AI.
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The 10 Best AI Stocks to Own in 2026
AI is moving from experiment… to essential.
Every major industry is integrating it.
Every major company is investing in it.
By late 2025, AI was already an $800B market — growing at a pace that could push it well beyond $1 trillion in the years ahead.
Cloud infrastructure is scaling fast.
AI-enabled devices are multiplying.
Automation is becoming standard.
But here’s the real question…
When trillions flow into this transformation — which stocks stand to benefit most?
Our new report reveals 10 AI stocks positioned across the backbone of this shift — from the companies powering the infrastructure… to those embedding intelligence into everyday systems.
If you want exposure to one of the defining growth trends of this decade, start here.
Welcome to another Data Driven VC “Insights” episode where we cover the most interesting research and reports about startups, VCs, LPs, AI & automation.
AI Task Horizon Now Doubling Every 4 Months
METR’s updated Measuring AI Ability to Complete Long Tasks study shows agent capability is accelerating faster than their 2025 paper estimated, with doubling times now at 4.3 months versus the originally measured 7 months.
130 Days, Not 7 Months: Post-2023 doubling time for AI task-completion horizons is 130.8 days (about 4.3 months), roughly 20% faster than METR’s prior estimate. The 2024 to 2025 sub-period showed doubling every 4 months, with the trend accelerating rather than plateauing.
228 Tasks, 31 of Them 8+ Hours: The expanded task suite grew 34% to 228 tasks and more than doubled the number of tasks lasting 8 hours or more, from 14 to 31. Claude Opus 4.6 hit an estimated p50 horizon of approximately 718 minutes, putting month-long task completion potentially within reach by 2027.
Domains Diverge Sharply: Software and reasoning tasks have 50 to 200+ minute horizons doubling every 2 to 6 months. Visual computer use lags 40x to 100x behind on absolute horizon length. Self-driving improves at roughly 0.6 doublings per year, a fraction of the software pace.
✈️ KEY TAKEAWAYS
If horizons continue doubling every 4 months, the agent thesis investment window is closing faster than typical fund deployment timelines and entry prices already reflect the acceleration. The domain divergence matters more than the headline number: betting on agents in software is structurally different from betting on physical-world AI. Founders building “AI does X for Y hours” should assume their moat from current model limitations has a 4-month half-life.
Pre-Seed Is Now Two Markets, Not One
Hamza Shad at Carta released the State of Pre-Seed Q1 2026 report, based on roughly 3,000 US pre-seed rounds adding up to over $2.3B in cash raised in the quarter.
AI now takes 50% of pre-seed dollars, up from 30%: A few years ago, AI startups received around 30% of all pre-seed dollars. By Q1 2026 that figure hit 50%, mirroring concentration patterns already visible at later stages.
The middle ground is disappearing: Rounds between $1M and $2.5M fell from 24% of pre-seed rounds in Q1 2023 to just 18% in Q1 2026. Sub-$1M rounds are growing in share while $2.5M+ rounds stay stable, leaving a bifurcated market.
SAFEs at record dominance, convertible notes at 7%: Convertible notes hit a record low of just 7% of pre-seed rounds and 8% of pre-seed dollars in Q1 2026. The remaining convertible-note volume clusters in biotech, energy and medical devices, where investors still prefer the additional terms.
✈️ KEY TAKEAWAYS
Pre-seed is no longer one distribution but two: a long tail of small AI-driven SAFEs and a cluster of mega-rounds backing presumed category winners. The squeeze on the $1M to $2.5M band is the practical signal, since that is exactly where most pre-seed funds were built to operate. Funds in that range face a choice between going smaller and earlier or moving up to compete on $2.5M+ rounds.
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$3 Trillion of Unrealised VC Value Stuck on Balance Sheets
The World Economic Forum and Stanford GSB published The Future of Venture Capital: Unlocking Liquidity and Growth, mapping the structural pressures on a $3.5T asset class that funds less than 0.1% of businesses but drives 42% of US public market capitalisation.
$3T trapped, 1,920 private unicorns, $7.3T in paper value: Roughly 1,920 VC-backed unicorns remain privately held globally, holding more than $7.3T in valuation and roughly $3T in unrealised NAV on fund balance sheets. Of those unicorns, 59% were founded more than 10 years ago and 20% more than 15 years ago.
DPI collapsed from 20% to 12% for prime-vintage funds: Funds in their 5-to-10-year prime return window historically returned around 20% of their value to LPs. By end of 2025, that figure had fallen to 12%. Among 2021-vintage funds, 75% had returned less than a quarter of LP capital by year four, versus roughly half for 2017 vintages.
Capital concentration intensifying at both ends: The 10 largest VC funds captured 42.9% of all capital raised in 2025. Median time to close a US VC fund stretched to a record 15.3 months, up from 9.7 months in 2022. AI alone took more than 50% of global deal value, with OpenAI, Scale AI, Anthropic, Project Prometheus and xAI absorbing 20% of total global VC across just five rounds.
✈️ KEY TAKEAWAYS
The capital recycling mechanism the venture model was built on has stalled, and the top 10 funds capturing 43% of 2025 commitments is what that stall looks like in fundraising data. Secondaries are now the most active liquidity lever at roughly 30% of US VC exit value, but the top 20 names absorb 86% of that volume, leaving the long tail without a real exit path. Emerging managers and sub-decacorn portfolio companies are taking the squeeze first.
What investing in software looks like 2026
Christoph Janz at Point Nine published a reflection on how a B2B SaaS firm's portfolio has shifted, with recent bets sitting at the intersection of AI and either the engineered world or nature.
From bits to atoms, photons and proteins: Recent Point Nine investments include Sensmore (200-ton autonomous dump trucks for open-pit mines), Serova (personalized cancer vaccines), EraDrive (autonomous navigation for spacecraft), a stealth micro-drone company targeting mosquitoes, and a stealth nitrogen-fixing crop.
AI for the world outside the office: Software-and-AI portfolio additions include Hula Earth (on-device AI identifying nearly 10,000 animal species), Sereact (warehouse robots picking objects they have never seen), Rerun (data infrastructure for robotics and computer vision), Draxon (VR training for airport ground crews) and a stealth operating system for weather modification.
Pure-software bets now require extraordinary domain depth: Where Point Nine still backs pure software, it is foundation models or agentic systems, including Vercept (foundation model for computer use, recently acquired by Anthropic), Poolside (foundation model for software agents), Forithmus (foundation models for medical imaging) and Zauber (AI agents for sea and air freight forwarders).
✈️ KEY TAKEAWAYS
When the canonical European B2B SaaS investor publicly says 2010-style SaaS investing is dead, it confirms what the a16z piece argues from the other direction: application-layer defensibility is collapsing, and the durable bets are migrating to physical, biological and foundation-model domains where AI plus capital intensity creates real moats. SaaS unit economics still apply since most of these companies sell as-a-service, but the question at seed is now domain depth and proprietary data, not GTM motion. Generalist software theses are aging out faster than most LP decks acknowledge.
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The Case for Backing Emerging Managers
Dan Gray at Odin published a long-form essay on emerging manager selection, drawing parallels between LPs evaluating first-time GPs and early-stage VCs evaluating founders.
Persistence is real but slight, 33% to 45%: Top-quartile VC funds repeat top-quartile status roughly 45% of the time based on fully realized performance, but only 33% of the time based on data known at the time of investment. Pitchbook analysis lands in a similar range. Track record matters less than LPs assume.
The relationship discount is 1.78x for emerging managers: An LP is 1.78x more likely to select an emerging manager if there is an existing personal relationship, versus only 1.21x for established GPs. That gap is the relationship compensating for uncertainty rather than information.
Founder NPS is inversely correlated with DPI: Drawing on 20VC data, the highest-NPS managers tend to deliver lower DPI. Hands-off, founder-friendly behavior often correlates with weaker realized exits, because it can mean less monitoring and more tolerance for inflated marks.
✈️ KEY TAKEAWAYS
The piece reframes emerging-manager allocation as an early-stage investment decision rather than a fund-of-funds exercise. The signal LPs should weight most is qualitative coherence between thesis, sourcing and fund size, not interim TVPI or pedigree. With distributions stalled and the top 10 funds capturing 43% of new commitments per the WEF data above, the structural opportunity for LPs willing to do that work is widening even as the field thins.
The GTM Layer That Sits Above CRM
Steph Zhang, Gio Ahern and Alex Immerman at a16z argue that the system-of-record era of GTM software is ending, and that the next decade of enterprise value will accrue to the orchestration layer that reads and writes across the CRM rather than the CRM itself.
Salesforce at ~$140B vs HubSpot at ~$9B captured 20 years of GTM value: A thousand companies were founded to help salespeople sell, but nearly all the value ended up in two names. The thesis is that the database that produced that outcome is being demoted to infrastructure, much as the social graph was demoted to one input among many for the news feed.
Seat counts are falling but spend is rising: Citing Jason Lemkin at SaaStr, the piece notes one customer cutting Salesforce seats from 10+ to 2 human seats and 1 API seat, while spend rose 83% from $12K to $22K per year. CRM usage has actually risen since AI tools were adopted at scale.
Software is still 5 to 10% of GTM spend: Historically GTM software has been 5 to 10% of total GTM spending versus 90%+ on payroll. The a16z thesis is that AI is the first wedge that lets software expand into the labor budget without cutting headcount, with reps using AI tools hitting attainment at noticeably higher rates than those without.
✈️ KEY TAKEAWAYS
If the thesis holds, the durable moat in GTM software shifts from data accumulation to orchestration across systems, and the unit of value migrates from seats to outcomes. The clearest investor signal is the seat-to-API substitution at constant or rising spend, since it shows AI labor budget being unlocked without payroll erosion. Seed and Series A bets in GTM should be screened on whether the company is building a reasoning layer on top of CRMs or merely another feature inside one.
That’s it for today!
Stay driven,
Andre
