Venture Capital

🔔 TechCrunch: 2025 has already minted 80+ new tech unicorns TechCrunch reports that more than 80 startups worldwide have crossed the $1B valuation mark so far in 2025, a sharp rebound after two slow years and a sign that capital is flowing again into AI, deep-tech, fintech, and robotics. 🖱 AI remains the top driver, but the unicorn class is unusually diverse this year from satellites and robotics to healthcare and fintech. 🖱 The typical 2025 unicorn was founded around 2018 and hit $1B by Series C, showing investors are rewarding multi-year execution rather than hype-first growth. 🖱 Many of this year’s unicorns come from outside Silicon Valley, with Europe, India, and East Asia producing a large share of the new billion-dollar companies. 🖱 The pace suggests late-stage investors are re-opening their checkbooks as public tech markets stabilize and AI infrastructure bets continue to compound. 🖱 Unlike the 2021 boom, where valuation inflation led the charge, 2025’s unicorn wave is rooted in revenue growth, enterprise adoption, and deeper technical defensibility.

The big picture: 2025 isn’t just an AI-only bull run, it’s a sign that full-stack innovation across multiple sectors is investable again, and capital is finally rewarding real scale.

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⚠️ SemiAnalysis claims OpenAI hasn’t run a single new full pretraining run since GPT-4o Analysts at SemiAnalysis especially Dillon Patel, say their infrastructure tracking shows no evidence that OpenAI has executed another massive pretraining run since GPT-4o. 🖱 In comparing Google TPUv7 cluster activity with OpenAI’s compute footprint, they found Google showing clear, sustained pretraining-level load while OpenAI showed none. 🖱 Since GPT-4o, OpenAI’s cluster consumption hasn’t meaningfully grown: no major new deliveries, no new large-scale GPU expansions, no hyperscaler spikes consistent with fresh pretraining. 🖱 The only visible activity resembles fine-tuning, reinforcement learning, or post-training adaptations not base-model training on hundreds of billions of tokens. 🖱 SemiAnalysis concludes that GPT-5 likely uses the same underlying pretraining as GPT-4o, meaning the “new model” may be a heavily optimized variant rather than a newly trained foundation. 🖱 If true, this is a major narrative shift: OpenAI’s headline releases may be increasingly dependent on optimization, distillation, and RL not brand-new foundation models.

If SemiAnalysis is right, the biggest story isn’t GPT-5 itself, it’s that OpenAI may have hit a compute, cost, or strategy ceiling that paused full-scale frontier pretraining.

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📌 NVIDIA just made a $2B move to lock itself deeper into the chip-design stack NVIDIA has invested $2 billion into Synopsys, the world’s dominant chip-design software provider, tightening its control over the semiconductor toolchain and accelerating a new wave of AI-driven hardware design. 🖱 The deal gives NVIDIA a significant ownership stake in Synopsys and expands their long-term partnership around AI-accelerated chip design. 🖱 Synopsys will integrate NVIDIA’s GPU and AI platforms directly into its EDA tools speeding up simulation, verification, and end-to-end semiconductor design workflows. 🖱 NVIDIA gains influence over the software layer that nearly every chip designer relies on, embedding itself not just in chips being built, but in the tools used to build them. 🖱 The partnership accelerates a shift toward “agentic engineering,” where AI agents assist or automate complex design and testing steps. 🖱 Analysts say this could give NVIDIA structural leverage across the industry but also raises concerns about ecosystem lock-in and the company’s tightening grip over the hardware supply chain.

NVIDIA’s bet is simple: the next advantage in chips won’t come from fabs or materials alone, but from AI-enhanced design pipelines and it wants to own the layer where every new chip begins.

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🫰 Founder Salary Report 2025, the real numbers behind how much startup founders actually pay themselves Pilot’s new annual report breaks down how founders compensate themselves and how funding, revenue, and market shifts (including AI) shape those decisions. 🖱 Average founder pay is $98K (median $75K) and 1 in 20 founders still take $0. That zero-salary cohort used to be almost twice as large in 2024 (9%). 🖱 How do founders decide? 31% say they set salaries based on what the company can afford. 54% feel that “what the startup can afford” is a fair number, yet 43% say they’re underpaying themselves even with that logic. 🖱 Compensation tracks directly with fundraising: Founders who raised $100K pay themselves $60K, while those who raised $10M+ earn $157K, a 3× jump driven purely by capital runway. 🖱 Same pattern with revenue: Under $100K revenue → founders average $62K. At $10M+ revenue → founder pay hits $163K. 🖱 Bootstrap vs VC: 18% of startups are fully bootstrapped. They pay themselves 40% less on average $63K vs $110K for venture-backed founders. 🖱 AI founders are not immune: Their average salary fell from $135K (2024) to $90K (2025), proof that even AI hype can’t compensate for tighter capital markets.

Founder pay is ultimately a mirror of a startup’s true health runway, revenue, and discipline, not the hype surrounding the sector.

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🚀 Peter Thiel–backed Quantum Systems just tripled its valuation to €3B after securing a fresh €180M round Quantum Systems, the German autonomous-drone maker used by NATO forces, has closed a €180M Series C extension, lifting total 2025 funding to €340M and pushing the company past a €3B valuation. 🖱 The raise was led by Balderton, with Porsche SE, Thiel, and Notion Capital returning, marking one of Europe’s largest dual-use funding rounds this year. 🖱 Quantum Systems builds AI-powered unmanned systems from long-range ISR drones to new land and maritime platforms with its tech already deployed in active defence operations. 🖱 The new capital will scale manufacturing and accelerate multi-domain expansion, as the company pushes beyond aerial systems toward a full autonomous-systems portfolio. 🖱 NATO contracts, rising defence budgets, and geopolitical pressure are driving demand, positioning Quantum Systems as one of Europe’s most strategically important hardware-AI companies.

Europe’s defence-tech moment is no longer theoretical, Quantum Systems is becoming one of the region’s anchor players in autonomous military systems.

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✈️ Airbus just ordered an emergency software fix for 6,000 A320-family jets after investigators tied a mid-air incident to solar-radiation–induced data corruption Airbus has issued a global directive requiring airlines to update flight-control software after a JetBlue A320 suddenly lost altitude in October, an investigation traced the failure to corrupted data caused by intense solar activity. 🖱 The fix affects roughly half of all A320-family aircraft worldwide, making it one of the largest safety-driven software recalls in Airbus history. The company is instructing airlines to roll back to an earlier, more resilient version of the flight-control software. 🖱 Most planes can be updated in 2–3 hours, but around 1,000 older jets will also need a hardware swap of the flight-control computer, which could extend ground time for some carriers. 🖱 Regulators including EASA have issued emergency airworthiness directives, meaning aircraft cannot return to service until the patch is applied, prompting overnight maintenance pushes across airlines to avoid major holiday-season disruptions. 🖱 The root cause: solar radiation strong enough to corrupt data inside the ELAC (Elevator & Aileron Computer), a key component in the A320’s fly-by-wire system. The incident exposed how modern avionics can be vulnerable to extreme space-weather events.

This is a rare reminder that as aircraft become increasingly software-centric, resilience to environmental edge cases like solar storms isn’t optional, it’s a critical part of aviation safety in the compute era.

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💬 Marc Andreessen’s quote on action over perfection hits differently in venture and Netscape’s story proves it. “Don’t be afraid to make mistakes, be afraid of doing nothing." Andreessen argues that venture is built on hypotheses, rapid execution, and learning through action not flawless planning. 🖱 At Netscape’s peak, the company had massive traffic and a clear chance to evolve from a browser into a full internet portal, the model Yahoo! and Excite later dominated. 🖱 Andreessen later admitted that “if Netscape had become a portal, it could have been a $100 billion company,” but the team doubled down on the browser and enterprise products instead. 🖱 The opportunity to turn attention into a media business, monetize user flow, and build a portal empire slipped by not because of a wrong move, but because of the move they didn’t make. 🖱 The lesson is consistent: in venture, mistakes generate data; hesitation erases options. Here are four practical online tests to help you understand your entrepreneurial and investment profile: 1. Entrepreneur Personality Test (Commit Action): evaluates motivation, discipline, and risk tolerance. 2. E-Scan Mini Entrepreneur Quiz: shows your entrepreneurship index and project-building tendencies. 3. Risk Appetite Quiz (SuperVistor): clarifies your investment risk profile. 4. Oltest.ru Investment Test: checks basic financial and investing knowledge. Growth rarely comes from perfect choices, it comes from increasing the number of attempts. ✔️Powered by Trade Watcher

📄 USPTO issues new rules for AI-assisted inventions, reaffirming that only humans can be inventors The U.S. Patent and Trademark Office has released updated guidance clarifying how inventions created with the help of AI tools should be treated and it marks a major reversal from its 2024 stance. 🖱 Under the new rules, AI systems cannot be named as inventors regardless of how much they contributed. Only humans can qualify as inventors, and AI is legally treated like software, lab equipment, or any other research tool. 🖱 The USPTO officially withdraws its earlier framework, which tried to adapt the “Pannu factors” for human AI joint creation. That test is now scrapped, and AI-assisted inventions will follow the same inventorship rules as traditional inventions. 🖱 Applicants must show human conception, meaning a person must have had the mental contribution that defines the inventive step even if AI generated ideas, designs, or solutions along the way. 🖱 The guidance aligns U.S. law with global patent practice, where human-only inventorship remains the standard. Patents filed abroad listing AI as an inventor will not be recognized in the U.S. 🖱 For startups and researchers, using AI won’t threaten patentability, but teams must keep clear records of how humans guided, interpreted, or refined AI outputs.

The move signals a stable IP environment: AI can accelerate innovation, but the legal credit and responsibility stays firmly with humans.

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🚀 Top 10 tools to spot and evaluate promising startups, the essentials every investor should have These platforms help you track emerging companies, validate markets, and understand where capital and talent are moving. 🖱 F6S gives access to one of the world’s largest databases of accelerators, grants, and early-stage startups, perfect for tracking teams just entering the ecosystem. 🖱 AngelList offers a full-stack environment for startup discovery: company profiles, round history, hiring signals, and early trend analytics. 🖱 StartUs Insights maps fast-moving innovation landscapes, helping you quickly identify startups by niche, technology, or frontier sector. 🖱 Owler provides continuous competitive intelligence, monitoring company news, growth indicators, comparisons, and key updates in real time. 🖱 SimilarWeb evaluates traffic, engagement, and digital momentum, giving a quick read on how healthy a startup’s acquisition funnel really is. 🖱 Data.ai delivers deep mobile analytics, downloads, revenue, user behavior, and category shifts across the global app economy. 🖱 GlobeNewswire surfaces official press releases, letting you catch funding rounds, partnerships, and product launches as soon as they hit the wire. 🖱 LinkedIn Sales Navigator helps verify founders, map teams, and identify key decision-makers through advanced search and filtering. 🖱 Google Trends reveals rising demand, growing topics, and early signals of market momentum that can guide niche scouting. 🖱 AI research tools (Perplexity, ChatGPT) accelerate market analysis, uncover patterns, and help form investment hypotheses dramatically faster.

Together, these tools give investors a sharper, real-time view of where new opportunities are forming and which startups are worth your attention next.

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💿 Warner Music Group just struck a deal with Suno, ending their lawsuit and signaling a new chapter for AI-generated music Warner Music and Suno have resolved their copyright dispute and agreed to collaborate under new licensing terms. 🖱 Under the agreement, Suno will roll out “advanced, licensed models” in 2026 to replace its existing AI tools putting permissions and artist rights at the forefront. 🖱 The deal includes the sale of Songkick (a live-music discovery platform) from Warner to Suno, moving Songkick’s fan engagement services under Suno’s umbrella. 🖱 From 2026, download access to Suno-generated music will be gated: free-tier users will only be able to play or share songs, while downloads will require a paid account (with monthly caps or extra fees). 🖱 Crucially, artists and songwriters under Warner will have full control over whether their names, likenesses, voices, or compositions are used, giving creators a real say in how AI-generated music involving their works is handled.

Suno’s pivot shows a shift: instead of AI music tools operating in murky legal waters, the industry may be moving toward licensed, consent-based AI creativity, where artists are protected and compensated.

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🎯 Michael Burry just reignited a showdown with Nvidia and markets may be entering a turning point According to TechCrunch, Burry is mounting a full-blown assault on Nvidia’s AI-driven valuation and accounting practices and this Thanksgiving drama could ripple across the entire AI industry. 🖱 Burry claims Nvidia’s stock-based compensation has cost shareholders roughly $112.5 billion, effectively slashing owner earnings by about 50%. 🖱 He argues many AI firms especially those using heavy GPU infrastructure are “cooking the books” by stretching depreciation on hardware that loses value fast, overstating profitability in the process. 🖱 In response, Nvidia fired back with a detailed memo to Wall Street analysts, stating Burry’s calculations are flawed: real buyback totals are closer to $91 billion, not $112.5 billion, and its compensation practices are “in line with peers.” 🖱 Burry says he isn’t calling Nvidia a fraud but likens it to Cisco during the dot-com bubble: overbuilt infrastructure, overhyped expectations, and a potential crash when reality sets in. 🖱 Beyond just Nvidia, his critique targets the broader AI infrastructure boom, a wave of optimism grounded more in accounting assumptions than hard cash flow.

Burry’s campaign isn’t just criticism, it’s a warning bell. If enough investors start to scrutinize AI companies’ accounting and cost structures, the next leg of the AI wave could be built not on hype, but on hard financials.

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📌 Trump just launched “Genesis Mission,” a national AI push to turbocharge U.S. scientific discovery President Trump signed a major executive order creating Genesis Mission, a federal program that unifies government data, national-lab supercomputers, and AI systems to accelerate breakthroughs in energy, biotech, materials science, and national security. 🖱 Genesis Mission’s core goal is to supercharge scientific discovery using large-scale AI and federal datasets. 🖱 The program directs the Department of Energy and national labs to build a unified AI platform that trains and deploys scientific models. 🖱 It aims to turn America’s huge stockpile of government data into a strategic AI advantage. 🖱 The initiative brings together federal agencies, universities, and private tech companies in one research pipeline. 🖱 Supporters frame it as the biggest U.S. science mobilization since the Apollo era, shifting focus from regulation to full-speed innovation.

Genesis Mission is a bet that the next wave of national power won’t come from new laws or new hardware but from AI-driven breakthroughs the government can produce at scale.

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🥲 NVIDIA posts a “congratulatory” tweet to Google with a knife hidden behind the smile NVIDIA suddenly dropped a very polite-but-not-really tweet today, congratulating Google on its AI progress but the subtext was impossible to miss. This comes right after the bombshell that Meta will buy Google TPUs for the first time, a move that hit NVIDIA’s stock by 6%. So NVIDIA decided to remind everyone who still runs the AI world. Here’s the literal meaning behind their velvet-toxic message: 🖱 “We’re happy for Google’s success… and we remain their supplier.” Translation: They still buy our stuff. 🖱 “NVIDIA is at least one generation ahead of the entire industry.” Translation: Your TPUs are cute, but we build the real frontier. 🖱 “We’re the only platform that can run any model anywhere compute happens.” Translation: Everyone else is specialized. We’re universal. 🖱 “NVIDIA offers higher performance, flexibility, and interchangeability than ASIC chips.” Translation: Google’s TPUs? Yeah they’re just ASICs. Limited. Narrow. Not NVIDIA-level.

Meta’s defection stung, and NVIDIA responded the way only a trillion-dollar titan does with a smile sharp enough to draw blood.

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🧠 Key takeaways from Ilya Sutskever’s new interview with Dwarkesh Patel 🖱 The scaling era is ending: Ilya argues that “just add more compute” no longer produces major breakthroughs, the next leaps require new scientific discoveries, not bigger clusters. 🖱 Generalization is the real bottleneck: Current models look impressive on benchmarks, but much of that progress is misleading. Models overfit to evals, especially after RL, while researchers unintentionally reinforce the loop by creating similar benchmarks and training setups. 🖱 He knows what’s missing but won’t say: Ilya hints that today’s models lack a core ingredient. While he keeps the details private, the interview suggests SSI is exploring Continual Learning and dynamic reward systems as possible directions. 🖱 AGI timeline: wide uncertainty: Could be 5 years, could be 20. But when it arrives, Ilya believes the safest way for humans to stay useful and stay in control is to merge with AI (a subtle wink toward Neuralink-style interfaces).

A rare interview where Sutskever openly says the frontier has shifted: brute-force scaling is no longer enough.

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🔔 Robinhood’s CEO just raised $120M to build “error-free AI" Harmonic, the AI startup co-founded by Robinhood CEO Vlad Tenev, secured $120M at a $1.45B valuation to tackle one problem everyone in AI feels: models still make too many mistakes. 🖱 Harmonic’s core mission is to reduce hallucinations and make AI outputs consistently accurate. 🖱 The company is building an “accuracy layer” that sits on top of existing models to catch and correct errors. 🖱 Investors see reliability as the next frontier, driving major interest in companies focused on precision. 🖱 The product targets enterprises that need trustworthy AI for finance, operations, and regulated sectors. 🖱 Tenev’s involvement signals a push toward safety, guardrails, and real-world dependability not just speed.

Harmonic is betting that the winners in the AI race won’t be the flashiest models, but the ones users can actually trust.

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📱 Altman says OpenAI’s new device will feel “more peaceful and calm” than the iPhone Sam Altman offered new hints about OpenAI’s upcoming hardware, describing it as a calmer, less stressful alternative to today’s smartphones. Speaking with TechCrunch, he emphasized that the goal is to rethink personal computing around attention, not distraction. 🖱 The device won’t mimic a phone, Altman says the world doesn’t need another rectangle with apps. 🖱 OpenAI is targeting a “peaceful, ambient” interaction style where the AI handles tasks proactively. 🖱 Jony Ive’s design influence is clear: the hardware is meant to feel physical, elegant, and minimal. 🖱 Altman hinted the device may prioritize voice, sensors, and context over screens. 🖱 He claims the product represents a shift toward “calm technology” computing that fades into the background.

OpenAI wants to build the first AI-native gadget that replaces, rather than competes with, the smartphone’s attention economy.

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📊 TAM / SAM / SOM: the three circles of hell for every pitch A simple, no-nonsense breakdown of how to calculate TAM / SAM / SOM without embarrassing yourself in front of investors: TAM — Total Addressable Market The whole universe. The market if you captured 100% of global demand. How to calculate: Top-down: — Use credible market reports — Select the segment tied to your category — Users × average spend Bottom-up (preferred): — Count how many users could use the product — Multiply by your real ARPU/ACV Formula: TAM = potential customers × annual revenue per customer Mistakes: — “global tech market $4T” nonsense — Random blogs with made-up numbers SAM — Serviceable Available Market The part of the market you can actually serve with your geography, niche, channels, model. How to calculate: Cut off everything out of reach: — Geography — Audience definition — Distribution limits — Regulation — Data/API/partner constraints Formula: SAM = reachable customers × your price Example: TAM = all online stores worldwide SAM = online stores in CIS that can plug in your product SOM — Serviceable Obtainable Market The realistic slice you can win in the next 12–36 months. Not “in theory.” With your team, budget, funnel, and sales speed. How to calculate: Start with SAM → apply a believable share (0.5–5%). Justify with your funnel: — Leads you can bring — Conversion logic — Retention Formula: SOM = realistic share × SAM Rule: If you claim more than 1–3% early on, nobody believes you. Bottom-up example (B2B SaaS for cafés): — 60,000 cafés in Russia → TAM = 60,000 × $800 = $48M — 15,000 cafés with 3M+ RUB turnover → SAM = 15,000 × $800 = $12M — You capture 2% in 2 years → 300 clients → SOM = 300 × $800 = $240k/year The trio in short: TAM — the idea’s theoretical ceiling SAM — the business’s real opportunity SOM — the team’s reality check

Real growth starts the moment the fantasies stop.

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⚡️ Google’s rebound shows Big Tech’s AI cycle is far from settled Google has quietly rebuilt market confidence after being prematurely written off in the early ChatGPT era and its stock momentum now reflects a much broader shift inside the company. 🖱 The launch of Gemini 3 and Google’s new TPU generation signal that the company is once again competitive at the frontier of AI performance. 🖱 Record search revenues show the core business is not only intact but accelerating despite years of disruption narratives. 🖱 Waymo’s expansion and improving utilization highlight Google’s lead in autonomous driving as rivals scale back. 🖱 Major Google Cloud AI deals with both startups and enterprises are pushing the division toward stronger margins. 🖱 YouTube’s dominance on Smart TV cements Google’s grip on the fastest-growing video consumption channel.

The message to markets: Google wasn’t falling behind, it was regrouping, and now the fundamentals are compounding again.

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📊 2025 cheat sheet: how much startups raise and how much equity they give up Here’s the quick snapshot of what “normal” looks like for U.S. fundraising in 2025, heavily skewed by SF pricing and big AI rounds. 🖱 Pre-seed: $500K–$2M rounds, typically 15–25% dilution. 🖱 Seed: $2M–$6M (AI teams often higher), around 18–22% dilution. 🖱 Series A: $8M–$20M, usually 15–20% sold. 🖱 Series B: $20M–$60M, about 10–15% dilution. 🖱 Series C: $40M–$150M, generally 5–10% sold as price per point jumps.

In San Francisco, these numbers hold; elsewhere in the U.S., expect lower valuations and slightly higher dilution for the same traction.

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