🧠 Sam Altman’s Merge Labs aims to connect human brains to AI, no surgery required

OpenAI founder Sam Altman is backing Merge Labs, a new brain-computer interface startup that wants to link human thought directly to machines but without the skull implants used by Elon Musk’s Neuralink.

🖱 Merge’s researchers are exploring a non-invasive BCI that could use genetic modifications and ultrasound instead of physical electrodes.
🖱 The idea: modify neurons so they produce proteins that react to ultrasound, allowing a scanning cap to read brain activity and send it to a computer.
🖱 This method could dramatically speed up adoption by removing the need for surgery, though safety testing remains a major hurdle.
🖱 Beyond medical applications like restoring vision, Altman envisions everyday integrations with AI systems from thought-based interfaces to real-time digital communication.

If Merge succeeds, brain-computer links could shift from sci-fi to mass market but the ethical questions will only grow louder than the ultrasound waves powering them.

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🧠 Apple turns to Google’s 1.2T-parameter Gemini model to reinvent Siri

Apple is reportedly finalizing a nearly $1 billion-per-year deal with Google to power the next generation of Siri using the Gemini 1.2 trillion-parameter model, one of the largest AI systems ever deployed in consumer devices.

🖱 The move marks Apple’s biggest AI partnership yet, signaling a shift from building in-house models to licensing Google’s foundation AI for deep reasoning and contextual understanding.
🖱 Gemini would enable Siri to handle complex, multi-step requests and maintain long-term memory across Apple’s ecosystem, from iPhone to Vision Pro.
🖱 The integration could make Siri competitive again after years of lagging behind ChatGPT, Claude, and Alexa in conversational depth.
🖱 Still, the partnership raises questions about privacy, data sharing, and Apple’s dependence on a direct rival’s infrastructure.

Apple’s smartest assistant yet may be powered by Google’s brain, a rare alignment that blurs the line between Silicon Valley’s fiercest competitors.

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🚀 B2B GTM in 2025 was chaos 2026 will be scale or die

A new report from Kyle Poyar’s Growth Unhinged surveyed 195 SaaS and AI startup leaders ($1–10M ARR) and found one thing clear: 2025 was the year of GTM experimentation. 2026 will be the year of ruthless focus.

🖱 23% of startups relied on inbound, 19% on outbound, and 18% on account-based GTM — but effectiveness depended entirely on deal size. Under $5K? Product-led + founder-led outbound. Over $25K? Personalized, account-based outreach wins.
🖱 Startups now juggle an average of 10+ marketing channels, spreading teams thin and burning cash. The highest ROI came from LinkedIn (66%), SEO (53%), and warm outbound (48%), while TikTok, PR, and Reddit barely registered.
🖱 The best-performing playbooks leaned on intent-based outbound, AI-assisted search, and closed events even small private dinners that could close six-figure deals.
🖱 91% of companies used ChatGPT, but over half saw no ROI. Tools like Clay, HubSpot, and Lovable are emerging as GTM automation favorites for 2026.

Next year’s mantra: cut the noise, double down on 2–3 high-intent channels, and apply AI only where it multiplies returns not where it adds complexity.

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💰 AI startups just broke every funding record in venture history

According to PitchBook, investors are pouring more capital into AI startups than in any previous tech boom from crypto to social media to mobile combined.

🖱 In the first half of 2025 alone, AI companies raised $104 billion in the U.S., capturing nearly two-thirds of all VC funding.
🖱 Globally, 57.9% of venture dollars went to AI and machine-learning startups, the highest concentration ever recorded.
🖱 Over the past three years, investors have funneled $330 billion into roughly 26,000 AI ventures, betting that the next trillion-dollar outcome will emerge here.
🖱 Yet many of these startups still lack clear revenue models or defensible moats, raising fears of a “post-AI bubble” shakeout.

If this momentum continues, 2025 will be remembered not just as the peak of the AI hype cycle, but as the year capital itself became artificially intelligent.

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🚗 Tesla shareholders back Elon Musk’s record $1 trillion pay plan

Tesla investors have approved a massive compensation package for CEO Elon Musk potentially worth up to $1 trillion in stock if the company hits a series of ambitious growth milestones.

🖱 Over 75% of voting shareholders supported the new plan, which ties Musk’s payout entirely to Tesla’s performance.
🖱 The package spans 12 tranches linked to operational goals, adjusted profits, and a market-cap target of $8.5 trillion, nearly six times Tesla’s current value.
🖱 Musk receives no base salary or cash bonus; all rewards depend on reaching these benchmarks.
🖱 The approval follows a court decision voiding his earlier $56 billion plan over governance concerns.

The vote marks one of the boldest compensation gambles in corporate history, a bet that Tesla’s valuation can soar to unprecedented heights. If Musk delivers, shareholders gain enormously; if not, the package will stand as a cautionary tale of ambition outpacing reality.

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🔥 Sam Altman snaps at investor Brad Gerstner over OpenAI spending question

Things got tense on the BG2 podcast when Altimeter’s Brad Gerstner pressed Sam Altman on how OpenAI could justify $1.4 trillion in spending commitments with “just” $13 billion in annual revenue all while Microsoft CEO Satya Nadella sat beside him.

🖱 Gerstner asked how OpenAI could afford such massive compute and infrastructure bets relative to its earnings.
🖱 Altman sharply pushed back: “We’re doing well more revenue than that… Brad, if you want to sell your shares, I’ll find you a buyer.”
🖱 He went on to defend OpenAI’s aggressive growth plan saying the company is betting on steep revenue growth, new consumer devices, and AI systems that “automate science.”
🖱 Nadella stepped in to cool the moment, noting that OpenAI consistently exceeds its forecasts, though the tension visibly lingered.

Gerstner later downplayed the clash on X, writing that they “laughed about it afterwards.” But the exchange revealed real strain between investor caution and founder conviction and raised a sharper question: is this confidence, frustration, or a sign of how high the stakes have become for OpenAI’s next phase?

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⚙️ Google’s new Ironwood chip takes aim at Nvidia’s AI dominance

Google has unveiled Ironwood, a powerful new tensor processor built specifically for training and running massive AI models and it’s already being compared to Nvidia’s top-tier chips. Delivering 4.6 petaflops of compute, Ironwood rivals the Nvidia B200 and GB200 in raw performance.

🖱 Ironwood is purpose-built for large-scale model workloads, optimized for Google Cloud’s AI infrastructure to accelerate both training and inference.
🖱 Anthropic has reportedly ordered 1 million Ironwood chips to power future versions of Claude marking one of the largest single AI hardware deals to date.
🖱 Sales will begin in the coming weeks, positioning Google as a direct supplier of cutting-edge silicon to major AI labs.
🖱 The launch underscores Google’s strategy to compete not just in AI software, but in the entire compute stack, from chips to models to cloud deployment.

With Ironwood, Google isn’t just catching up to Nvidia,it’s rewriting the balance of power in the global AI hardware race.

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📊 Crypto Venture Weekly: November 3–7, 2025

$630M was raised across 16 projects this week, led by Ripple’s $500M strategic round at a $40B valuation. At the same time, BitMine, Evernorth, Strategy, Future Holdings, and Strive announced $564M in new digital asset treasury allocations across BTC, ETH, and XRP.

Here’s what the top 8 are building 👇

🖱 Ripple ($500M, Citadel, Fortress, Pantera, Brevan Howard, Marshall Wace)
Enterprise blockchain for cross-border payments, expanding institutional liquidity and settlement.

🖱 fomo ($17M, Benchmark, Coinbase Ventures, Archetype)
Consumer-focused crypto trading app simplifying onchain investing.

🖱 Donut Labs ($15M, Hack VC, Bitkraft, Inception, Makers Fund)
Agentic crypto browser enabling autonomous, AI-driven Web3 experiences.

🖱 Liquid ($7.6M, Paradigm, General Catalyst)
Perpetuals DEX aggregator improving price discovery and execution across trading venues.

🖱 Arx Research ($6.1M, Castle Island, 1kx, Placeholder)
Building handheld point-of-sale devices for everyday digital asset payments.

🖱 Harmonic ($6M, Paradigm, Solana ecosystem backers)
Open block-building system optimizing validator revenue and throughput on Solana.

🖱 Sprinter ($5.2M, Robot Ventures, Uniswap, A.Capital)
Crosschain “solving-as-a-service” infrastructure for interoperability and computation.

🖱 Zynk ($5M, Hivemind, Coinbase, Alliance DAO)
Settlement infrastructure enabling instant global payments with onchain finality.

Investor attention this week centered on infrastructure, AI convergence, and real-world payments, with Ripple’s expansion marking another step toward institutional blockchain adoption and decentralized finance at scale.

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💰 Meta’s billion-dollar problem: profits from scam ads

Meta is quietly earning a fortune from fraudulent advertising, according to internal documents revealed by Reuters. Roughly 10% of Meta’s 2024 revenue; about $16 billion, came from scam or banned ads, flooding its platforms with billions of deceptive promotions each day.

🖱 Meta’s systems displayed up to 15 billion “high-risk” scam ads daily, often easier to post than on Google.
🖱 Even repeat offenders weren’t removed: some advertisers amassed 500+ violations and stayed active because they spent heavily.
🖱 A so-called “penalty bid” system charged suspected scammers higher rates, so Meta still profited from them instead of banning them.
🖱 Regulators are circling: the SEC and UK authorities are probing Meta after reports that over half of payment-fraud losses trace back to its platforms.

Meta aims to shrink scam-ad revenue to 6% by 2026 but cutting that stream could hurt profits in the short term.

In chasing ad dollars, Meta may have optimized for growth at the expense of integrity and regulators are starting to notice.

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☀️ Google to launch solar-powered space data centers with TPUs by 2027

Just a day after the first Nvidia H100 reached orbit, Google has announced Project Suncatcher, an ambitious plan to deploy space-based data centers powered entirely by solar energy.

🖱 Each orbital unit will operate at 650 km altitude, collecting uninterrupted sunlight unaffected by weather, time of day, or seasons.
🖱 The vacuum environment enables natural cooling, potentially cutting maintenance costs by up to 40%.
🖱 Google’s custom TPUs have passed radiation-resistance tests, performing 15× better than the mission’s minimum standard.
🖱 In early 2027, Google plans to launch two satellites carrying four TPUs each to validate full system performance before scaling further.

Competitors are racing too, startup Starcloud claims it will have a complete orbital data center operational by the same year.

If Suncatcher succeeds, it could redefine cloud infrastructure, where compute power finally leaves Earth’s atmosphere.

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🚐 Waymo hits the gas: robotaxis roll into Detroit, Vegas & San Diego

Alphabet-owned Waymo is expanding its driverless taxi network into three new U.S. cities, Detroit, Las Vegas, and San Diego,marking its fastest rollout yet. The move comes as Waymo targets 1 million rides per week by 2026, up from roughly 250,000 earlier this year.

🖱 Each city serves a strategic testbed: Detroit’s snow, Las Vegas’s traffic, and San Diego’s suburban sprawl push the system’s weather and mapping limits.
🖱 The fleet will include both Jaguar I-Pace EVs and Zeekr RT prototypes, blending legacy auto hardware with Waymo’s in-house AI stack.
🖱 Rollout starts with mapping and safety-driver phases before public service launches, following Waymo’s gradual expansion model.
🖱 The company now operates or tests in eight major metros, outpacing rivals like Cruise and Zoox after recent regulatory and safety setbacks in the industry.

Waymo’s expansion signals a shift from pilot projects to scaled commercial ops and a bet that diversified geography, not just tech leadership, will define who owns the future of autonomous mobility.

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💥 The big (fake?) AI short: how media sold Burry’s phantom bet

Michael Burry didn’t actually short Nvidia or Palantir for “$900 million” but that didn’t stop headlines from saying he did. His latest 13F filing became clickbait gold for financial media hungry for an AI bubble narrative.

🖱 Outlets from FT to Bloomberg framed Burry’s positions as a massive anti-AI bet, claiming Scion Asset Management shorted 80% of its portfolio.
🖱 In reality, 13F forms only show long positions, we can’t see whether he hedged, shorted, or offset those puts elsewhere.
🖱 The filings don’t reveal strike prices or expirations, meaning the actual value of the “$900M” put exposure could range from millions to nearly zero.
🖱 The ambiguity made it perfect clickbait: “Burry vs. the AI boom” is a better story than “data unclear.”

By turning nuance into narrative, the media converted paperwork into panic, proof that in the attention economy, the scariest number wins.

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🗣️Yann LeCun: “LLMs aren’t a bubble, the AGI hype is.”

Meta’s chief scientist Yann LeCun pushed back against claims that large language models are an investment bubble. In his view, the money flowing into AI isn’t misplaced but the expectations around AGI are.

🖱 LeCun argues that LLMs already have lasting, practical value and will remain useful across industries for years.
🖱 The real “bubble,” he says, is the belief that scaling current models alone will reach human-level intelligence.
🖱 True progress, in his view, requires scientific breakthroughs not just more data, parameters, or compute power.
🖱 “We’re missing something important,” LeCun warned, suggesting that the current deep learning paradigm needs fundamental innovation.

LeCun’s message lands as a reality check for the AI boom: LLMs may be profitable but not magical.

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💰 SoftBank cashes in Nvidia stake to fuel $6B+ AI push

SoftBank has sold its $5.8B stake in Nvidia, using the windfall to supercharge new bets across AI infrastructure, robotics, and data centers part of a sweeping shift from model investments to the physical backbone of AI.

🖱 The profit surge follows soaring valuations in AI portfolios held by Vision Fund and Arm, lifting quarterly results above expectations.
🖱 Proceeds are being redirected into AI data centers with OpenAI, robotics manufacturing in the U.S., and semiconductor ventures aligned with Japan’s tech strategy.
🖱 The sale also trims exposure to overheated GPU markets, signaling SoftBank’s pivot toward long-term ecosystem control instead of short-term chip gains.
🖱 Analysts note this marks SoftBank’s most decisive reallocation since its Alibaba exit, effectively turning Masayoshi Son’s empire into an AI-first holding company.

SoftBank’s Nvidia exit shows that in the next AI wave, the real power may lie not in who trains the biggest model but in who owns the compute, infrastructure, and robotics that make it possible.

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⚡️ Yann LeCun to leave Meta and build his own “world model” startup

According to the Financial Times, Meta’s chief AI scientist Yann LeCun is preparing to leave the company and is already in early talks with investors for a new venture.

🖱 The project will reportedly focus on “world models” AI systems that learn from video and spatial data to understand the real world, an idea LeCun has championed for years.
🖱 His exit follows internal tensions at Meta, where LeCun now reports to Alexander Wang, the 27-year-old former Scale AI CEO, a structure some say undercut his autonomy.
🖱 Meta has not commented, and LeCun himself remains silent, but the move would mark the end of an 11-year run leading FAIR and shaping Meta’s AI research vision.

A symbolic moment: the pioneer who helped invent deep learning now leaves the corporate labs to chase a new foundation for machine intelligence, one rooted not in text, but in reality.

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💰 Anthropic may turn profitable years before OpenAI

A new WSJ analysis shows Anthropic could reach break-even by 2028, while OpenAI faces deep losses projected at $74B that same year despite record growth.

🖱 Anthropic’s steady climb: Its revenue and costs are rising in sync, keeping spending disciplined and scalability intact.
🖱 Corporate-heavy model: Roughly 80% of Anthropic’s revenue comes from enterprise clients, giving it predictable, recurring income.
🖱 OpenAI’s spending spree: Plans to pour $1.4T into infrastructure over the next eight years, delaying profitability past 2030.
🖱 Strategic contrast: While OpenAI bets on massive consumer reach and platform dominance, Anthropic is quietly building a leaner, cash-efficient B2B engine.

In the race to monetize intelligence, Anthropic might prove that profitability not scale, is the smarter kind of power.

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📊 Top 10 most active VC investors in post-seed AI startups

PitchBook’s latest AI & ML report ranked the most active investors backing startups beyond the seed stage (2022–June 2024). While big names dominate, a few lesser-known firms stand out for their quiet consistency.

🧾 Top 10 by number of AI/ML deals:

🖱 Gaingels: 199 deals
🖱 Alumni Ventures: 122
🖱 Sequoia Capital: 113
🖱 Andreessen Horowitz: 94
🖱 Soma Capital: 91
🖱 Tiger Global: 83
🖱 Mana Ventures: 74
🖱 FJ Labs: 72
🖱 Khosla Ventures: 71
🖱 Bossa Invest: 69

🌍 Hidden gems in the ranking:

🖱 Mana Ventures (California): Early investor in Unity, with a portfolio spanning SpaceX, Groq, Canva, Figure, and Epic Games. The firm leans toward deep tech and frontier innovation.

🖱 Bossa Invest (Brazil): A micro VC powerhouse with 1,500+ LATAM investments and over 100 exits. Their Airtable-based portfolio shows at least three unicorns: Enjoy, Swvl, and Amprius.

While the giants still write the biggest checks, regional and specialized funds like Mana and Bossa are quietly becoming power brokers in the AI ecosystem.

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💼 The 70/20/10 rule, how McKinsey’s innovation model shapes startup portfolios

The 70/20/10 rule, or McKinsey’s innovation portfolio, offers a clear framework for distributing capital and risk across startups at different maturity levels, balancing stability, growth, and moonshots.

🖱 70% — Core: Proven startups with clear business models and profit. Focused on stable income and risk minimization.
🖱 20% — Adjacent: Startups expanding current business directions. Moderate risk, solid growth potential, and diversification.
🖱 10% — Transformational: Radical, high-risk innovations, AI, biotech, quantum tech, with a chance for x50 returns.

Applied to a $1M portfolio, this means: $700K in late-stage, profitable companies (x2–3 returns), $200K in early-stage, adjacent startups (x5–10), and $100K in pre-seed, breakthrough bets (x50+ potential).

The logic is simple: core holdings fund your stability, adjacent bets fuel growth, and transformational ideas create the upside. Venture success isn’t luck, it’s a system where reliable startups sustain bold innovation.

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💰 Robert Kiyosaki says he’s $1.2B in debt and calls it his best financial move

The Rich Dad Poor Dad author revealed he currently holds $1.2 billion in debt, but insists he’s not worried at all.

🖱 Kiyosaki explained that the debt is “good debt”, borrowed money used to acquire cash-flowing assets like real estate and businesses, not liabilities.
🖱 His philosophy: the rich use debt to get richer, while the poor and middle class use it to buy things that lose value.
🖱 He emphasizes that the key is who controls the debt, if your assets pay for it, you own the system; if your labor pays for it, the system owns you.

His takeaway: don’t fear debt, learn to make it work for you.

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