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The official channel of V3V Ventures. We share updates on our investments, portfolio companies, and fund activities.

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🔔 Oura Ring nears $11B valuation with fresh $875M raise

Bloomberg reports Oura Health, maker of the Oura Ring, is raising about $875M, doubling its valuation to $11B less than a year after its last round.

🖱 Impact on product: Funds will fuel R&D and new features in sleep, fitness, and women’s health tracking, while scaling hardware production.
🖱 Impact on market: Positions Oura as a heavyweight in wearables, taking on Apple and Samsung as it expands globally.
🖱 Impact on users: With 5.5M rings sold and $1B in projected 2025 sales, expect more personalized health insights and faster global rollout.

Oura’s surge shows how niche hardware can carve out a dominant space in consumer health tech, if it can outpace big-tech rivals.


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💬 Facebook is getting an AI dating assistant

Meta is rolling out a new AI assistant in Facebook Dating designed to help users find better matches. You’ll be able to ask it things like “find a Brooklyn girl in tech” or get help sharpening your profile. They’re also introducing “Meet Cute”, a surprise match weekly suggested by the algorithm to reduce swipe fatigue.

🖱 Impact on product: The AI layer could make dating more conversational and personalized. Instead of swipe-based choice overload, users may get guided matching, profile tweaks, and context-aware suggestions.
🖱 Impact on market: By embedding AI into dating, Meta competes more aggressively with Match/Tinder, Hinge, etc. It raises the bar: competitors must add smarter assistants or risk falling behind.
🖱 Impact on users: Users will see more curated results and less randomness. But it also means more dependency on AI’s “judgment”, biases, privacy, and transparency will be critical factors.

Meta is essentially weaving AI deeper into the social fabric: your love life might soon be steered, at least in part, by chat models. Let me know if you want to unpack the risks (bias, data use) or compare with what Tinder / Hinge are doing.


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📊 Carta’s VC performance cheat sheet

Carta analyzed 2,700+ U.S. VC funds, highlighting the three core metrics every LP and GP watches: Net IRR, Net TVPI, and DPI.

🖱 Impact on product: IRR shows the annual growth rate of fund capital, giving a sense of speed. (Ex: 15% IRR → money grows ~15% yearly).
🖱 Impact on market: TVPI tracks total value creation, realized + unrealized. (Ex: 2.0x → $1 invested now worth $2).
🖱 Impact on users: DPI reflects real cash back to LPs. (Ex: 0.5x → $1 invested, $0.50 actually returned).

Together, IRR = speed, TVPI = scale, and DPI = cash in hand, the quick framework for judging fund performance.


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⚡️ Google’s $5 AI Plus goes global

Google rolled out AI Plus in 40 countries, bundling Gemini 2.5 Pro, 200 GB cloud, and creative tools (Flow for images, Whisk, Veo 3 Fast for video).

🖱 Smarter tools: Gemini 2.5 Pro brings advanced AI chat + reasoning at consumer pricing.
🖱 Global rollout: Expansion across Asia, Africa, LatAm, plus Ukraine and Central Asia, targets underserved regions.
🖱 Price war brewing: OpenAI fires back with a $4.5 plan in India & Indonesia.

Cheap AI is no longer U.S.-first, it’s going frontier.


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🚀 Cathie Wood’s contrarian playbook

Cathie Wood, founder of ARK Invest, is known for making bold bets on disruptive tech, AI, robotics, biotech, and space, often against Wall Street consensus.

🖱 Early conviction calls: Tesla investment showed belief in EV adoption when most analysts called it overvalued.
🖱 Unorthodox assets: Bitcoin & blockchain positions revealed foresight before institutions recognized them.
🖱 Core philosophy: Focus remains on “disruptive innovation” — companies reshaping industries, not following trends.

Together, it’s a framework of data + courage against consensus, the formula she believes drives superior returns.


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⚖️ Legal tech funding smashes records

Investors are pouring money into startups building AI and software for lawyers, with 2025 already a record year.

🖱 Tools for firms: Filevine ($400M) scales practice management platforms.
🖱 AI in the courtroom: Harvey ($600M across two rounds) dominates as the go-to AI legal assistant.
🖱 Specialist plays: Blue J ($122M) and Eudia ($105M) go deep into tax law and Fortune 500 legal analytics.

Legal workflows are becoming the next big AI battleground.


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🤖 OpenAI hires an ads chief for ChatGPT

OpenAI is creating a new executive role to lead advertising and monetization across ChatGPT. The position will report to Fidji Simo, CEO of Applications, and oversee both ads and subscriptions.

The move comes alongside Sam Altman’s internal memo on a project called “Stargate,” hinting at broader business expansion.

🖱 Signals OpenAI’s shift from subscriptions to ad-driven revenue
🖱 Puts monetization squarely under Simo’s product group, not research
🖱 Raises risks around user trust and alignment of incentives

A clear sign that OpenAI is entering its revenue-maximization phase, with ads set to reshape the ChatGPT experience.


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📊 Carta’s take on startup fundraising cycles

Carta shared a chart showing the share of startups that secure their next round within a given number of quarters.

Their analyst framed the picture as “healthy”, not a bubble (too much capital too fast) and not a crisis (too few rounds).

Skeptics note the optimism feels baked in:
🖱 The same chart could have been spun as bullish or bearish
🖱 The message is always that the market is “just right”
🖱 Real conditions may be less balanced than the framing suggests

A reminder that data can be solid, but interpretation often follows the storyteller’s bias.


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🧠 Meta hires ex-OpenAI scientist Yang Song

Meta has recruited Yang Song, a former research lead at OpenAI, to join its Superintelligence Labs as a principal researcher.

🖱 Song led OpenAI’s “strategic explorations” team, focused on multimodal and large-scale data
🖱 Earlier at Stanford, he pioneered techniques later used in DALL-E 2
🖱 At Meta, he’ll report to Shengjia Zhao, another OpenAI alum now steering parts of the lab
🖱 Adds to Meta’s string of hires from OpenAI, Google, and Anthropic
🖱 Signals Meta’s intent to strengthen leadership for its superintelligence efforts

The move underscores how Meta is aggressively stacking top AI talent to challenge OpenAI.


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✅ Trump greenlights TikTok sale via executive order

Former President Donald Trump signed an executive order approving a proposed deal to transfer TikTok’s U.S. operations to American and international investors.

🖱 Under the plan, TikTok US would be valued at $14 billion.
🖱 A consortium including Oracle, Silver Lake, and Abu Dhabi’s MGX will control ~45%, while ByteDance retains less than 20% and limited governance rights.
🖱 The executive order grants 120 days to finalize the deal.
🖱 A key clause: the algorithm powering TikTok in the U.S. must be controlled, retrained, and monitored under the new U.S. entity.

This is a big pivot, instead of banning TikTok outright (as required by the 2024 law unless it was divested), the U.S. is shifting toward an ownership solution aimed at preserving the app under more direct U.S. control.


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✅ Sierra Space pivots after NASA changes the Dream Chaser deal

Sierra Space’s ambitions for its Dream Chaser spaceplane got a jolt after NASA altered their contract: the agency is no longer guaranteeing cargo missions to the ISS.

🖱 Instead of docking at the ISS, Dream Chaser will first fly a free-flying demonstration (no docking) in late 2026, with NASA offering only “minimal support.”
🖱 That shift removes the guaranteed revenue and support Sierra had counted on, forcing it to reimagine Dream Chaser’s market role.
🖱 Sierra is pushing hard on the defense and commercial angles now, they say the vehicle’s runway-landing, reusable nature gives flexibility for diverse payloads and mission profiles.
🖱 Timing is tight: the ISS is scheduled for deorbit around 2030, leaving a narrow window for Dream Chaser to prove itself as a cargo or multipurpose vehicle.

NASA’s pivot leaves Sierra with less certainty but more freedom, the question is whether Dream Chaser can turn flexibility into a sustainable business before the ISS retires.


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✅ Meta bets big on humanoid robots as its next AR-sized play

Meta is investing billions into humanoid robotics, positioning them as the company’s next frontier after augmented reality. The strategy leans less on hardware and more on becoming the dominant software platform for robots.

🖱 Internal project “Metabot” serves as Meta’s prototype, but the real goal is licensing robotics software, an Android-style model for humanoids.
🖱 Focus is on “world models” and simulation: training robots to grasp, move, and manipulate objects safely in real environments.
🖱 Meta’s new Superintelligence Lab works alongside its robotics team, bringing in top hires like MIT roboticist Sangbae Kim and ex-Cruise CEO Marc Whitten.
🖱 The company believes “good enough” designs (not perfect human mimicry) will win faster adoption across homes and industries.

Instead of chasing flashy humanoids, Meta wants to own the operating system that powers them, hedging its bet so no matter who builds the body, Meta controls the brain.


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📈 Solo founders are on the rise

More startups are being launched by a single founder rather than co-founding teams. In 2015, just 17% of companies were started solo. By 2024, that number has more than doubled to 35%.

Factors: faster prototyping tools, AI copilots, remote work culture, and less stigma around going it alone.

The solo-founder path is shifting from exception to norm, raising the question: will investors adapt their playbooks to back more “one-person startups”?


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✅ 10 tools to check a startup in 24 hours

With deal speed accelerating, investors and founders often have just a day to vet opportunities. These tools help you run fast, high-impact checks without a full analyst team.

🖱 Clay — auto-finds founders, clients, and key signals across open databases.
🖱 Valuer.ai — instant startup valuation using market comps and trend data.
🖱 DocSend — tracks pitch deck engagement: who opened it, which slides, and how long.
🖱 SimilarWeb — quick analytics on traffic, audience, and competitor channels.
🖱 Crunchbase Pro Alerts — real-time pings on new rounds, hires, and partnerships.
🖱 Ghostwriter — generates sharp founder questions from submitted materials.
🖱 Glassdoor & Blind — employee reviews for culture insights and hidden risks.
🖱 GitHub / Product Hunt signals — commit activity and early user feedback momentum.
🖱 PitchBook Instant — express reports on markets and comparable players.
🖱 OpenAI / Claude — automated verification of data, spotting weak spots in models and finances.

Instead of weeks of research, these tools give investors a rapid “go/no-go” lens, surfacing both red flags and hidden opportunities in a single day.


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🔔 SentinelOne raises $850M to push AI-first cybersecurity

Israeli startup SentinelOne has closed an $850M round at a $12.5B valuation, making it one of the largest cybersecurity deals of 2025. The focus: scaling AI systems to detect attacks before they happen and building critical infrastructure in Europe.

🖱 Lead investors include Tiger Global and SoftBank Vision Fund 2, with Accel and Insight Partners joining the round.
🖱 Funds will fuel AI-powered proactive defense and a dedicated European data center for sovereign clients.
🖱 SentinelOne already secures 10,000+ enterprises, from global banks to energy majors.
🖱 The company is positioning itself as the AI layer of cybersecurity, betting on automation to replace reactive models.

As attacks on infrastructure escalate, SentinelOne is shaping itself not just as a security vendor, but as the operating system for corporate defense.


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🤔 Is AI a bubble? or the new railroads?

AI infrastructure spending has reached historic levels, with data centers alone driving 33% of US GDP growth in 2025. That’s $3T in the US and another $1T worldwide, numbers that recall past bubbles, but with key differences.

🖱 In scale: AI accounts for <1% of GDP, less than telecom (1.2%) and far below the railroads of the 1870s (4%), whose crash was brutal.
🖱 In capital intensity: GPUs depreciate faster than almost any asset (“chips depreciate like fish,” says Bill Gurley), leaving concrete and cooling as the only salvage value.
🖱 In revenue: AI revenues jumped from ~$0 five years ago to $60B today, doubling annually, unlike past bubbles, where growth slowed before the collapse.
🖱 In valuations: Nasdaq’s P/E peaked at ~72 in 2000; today it sits at ~32 high, but still tethered to earnings.
🖱 In funding: Unlike debt-fueled bubbles, today’s AI buildout is financed mostly from Big Tech free cash flow, not leverage.

The catch? Sustaining this boom will require another $2.9T. Giants can cover half, but outside capital must carry the rest.

AI today looks less like a bubble and more like an intensive investment boom, underpinned by real, accelerating revenue growth, a key distinction from railroads and dot-coms.


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🚀 International Venture news for the past week

🖱 Healthtech consolidation accelerates: 2025 has already seen 102 healthtech M&A deals, nearly matching last year’s full count (112). The sector is rapidly consolidating as scale becomes survival. 🔗 WSJ

🖱 VCs use AI to source startups: San Francisco’s Touring Capital deployed its “R2-D2” algorithm to scout deals, closing a $330M fund for AI startups. AI is moving upstream into venture itself. 🔗 WSJ | Economic Times

🖱 Brazilian legal AI attracts US giants: Enter, a São Paulo startup automating legal workflows, raised $35M from Founders Fund and Sequoia at a $350M valuation. Clients include Airbnb and Nubank. 🔗 WSJ

🖱 Fewer deals, larger checks
2025 deal value up 42.8%, but deal count down 31.4%. The shift favors bigger rounds for fewer winners. 🔗 Axios

🖱 Pharma doubles down on venture: Sanofi Ventures boosted its capital to $1.4B, focusing on biotech and digital health bets. Big Pharma is increasingly playing as LP + operator. 🔗 MedCityNews

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🔔 Trump’s DOE bans “climate change” and “green” from staff vocabulary

The U.S. Department of Energy’s Office of Energy Efficiency & Renewable Energy has circulated a memo forbidding staff from using terms like “climate change,” “green,” “decarbonization,” and “sustainability.” The directive comes as the Trump administration reframes federal energy policy.

🖱 The banned list also includes “emissions,” “carbon footprint,” and “transition,” signaling a retreat from standard climate terminology.
🖱 The change is led by a Trump-appointed official and reflects political positioning rather than scientific consensus.
🖱 Global clean energy investment, meanwhile, hit a record $386B in H1 2025, highlighting the gap between U.S. rhetoric and market momentum.
🖱 DOE staff now face constraints in reporting, grants, and external communication, raising concerns over transparency and accountability.

By policing language, the administration isn’t just shaping optics, it risks reshaping how U.S. energy policy is written, tracked, and enforced.


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💰 TikTok’s US sale looks like a steal

The Trump-approved deal values TikTok US at $14B, a fraction of what comparable platforms trade for. The app generates an estimated $10–20B annually in the US, yet sold for roughly 1× revenue.

🖱 By comparison: Snap trades at ~2.5× revenue, Reddit at ~25×, making TikTok’s price look like daylight robbery.
🖱 The Chinese side has not commented publicly, leaving questions about consent or coercion.
🖱 Trump claims the agreement is final, though details on buyer structure and governance remain vague.

If the valuation holds, this may go down as one of the most lopsided deals in tech history.

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🔔 How Larry Ellison Kept 40% of Oracle

Oracle’s founder still owns ~40% of his $300B company, a rare feat when most tech founders get diluted down to 5–15%. The reason? In 1977, no venture capitalist would fund software.

🖱 Late ’70s VC dollars flowed to hardware and biotech, Intel, Apple, Genentech all raised millions.
🖱 Ellison couldn’t even get a meeting. He and two co-founders pooled $2,000 and lived off consulting.
🖱 Their first break was a $50K CIA contract; nights were spent building their own database product.
🖱 Oracle launched in 1979 with “Version 2” (nobody trusted a Version 1). By 1984 revenue hit $70M; by 1987 it was the world’s largest database company.

By necessity, Ellison bootstrapped, making money from day one, avoiding dilution, and keeping control.

The irony: being ignored by VCs turned $2K of founder capital into one of the greatest fortunes in tech history.


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