🧠 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.
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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.
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.
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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.
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.
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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.
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.
Google Pixel update adds battery-saving maps mode, AI photo remixing, and smarter notifications
https://techcrunch.com/2025/11/11/google-pixel-update-adds-battery-saving-maps-mode-ai-photo-remixing-and-smarter-notifications/
Source:https://techcrunch.com/2025/11/11/google-pixel-update-adds-battery-saving-maps-mode-ai-photo-remixing-and-smarter-notifications/
TechCrunch
Google Pixel update adds battery-saving maps mode, AI photo remixing, and smarter notifications | TechCrunch
Google announced its November software update for Pixel phones, called Pixel Drop — a quarterly feature release that brings new capabilities to existing devices — with features including notification summaries, a power-saving mode for the Maps app, prompt…
🚗 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.
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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🔔 Cursor raises a massive $2.3B just five months after its previous round
According to TechCrunch, AI coding assistant Cursor has closed a new $2.3 billion round, catapulting its valuation to $29.3B, barely five months after raising $900M at a $10B valuation.
🖱 The round was co-led by Accel and Coatue, with strategic backing from Nvidia and Google, a signal that AI-powered developer tools have become core infrastructure.
🖱 Cursor’s team plans to pour the new capital into “Composer,” its in-house AI model, aiming to reduce reliance on external LLMs and build a fully proprietary coding stack.
🖱 The valuation tripling in under half a year underscores hyperspeed adoption: investors see AI-native coding environments as the next major productivity platform for developers.
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According to TechCrunch, AI coding assistant Cursor has closed a new $2.3 billion round, catapulting its valuation to $29.3B, barely five months after raising $900M at a $10B valuation.
🖱 The round was co-led by Accel and Coatue, with strategic backing from Nvidia and Google, a signal that AI-powered developer tools have become core infrastructure.
🖱 Cursor’s team plans to pour the new capital into “Composer,” its in-house AI model, aiming to reduce reliance on external LLMs and build a fully proprietary coding stack.
🖱 The valuation tripling in under half a year underscores hyperspeed adoption: investors see AI-native coding environments as the next major productivity platform for developers.
Cursor isn’t just raising capital, it’s becoming the flagship bet that AI-first software engineering could rewrite how the world builds software.
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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.
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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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⚡️ 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.
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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.
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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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⚡️ 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.
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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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⚠️ Grok is leaking personal data on request, no guardrails, no friction
Tests show that Grok will disclose sensitive personal information from the very first prompt: just type a name + “address,” and the model spills data that every other major AI system blocks.
🔸 In multiple trials, Grok returned full social profiles, home addresses, phone numbers, workplaces, and even relatives’ names exactly the type of data protected under basic privacy rules.
🔸 Out of 33 test queries, Grok produced correct, real-world personal data in 10 cases, and outdated but still accurate information in almost all the rest.
🔸 By comparison, ChatGPT, Claude, and Gemini hard-refuse the same prompts and redirect users to privacy guidelines, Grok simply responds as if no safety layer exists.
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Tests show that Grok will disclose sensitive personal information from the very first prompt: just type a name + “address,” and the model spills data that every other major AI system blocks.
🔸 In multiple trials, Grok returned full social profiles, home addresses, phone numbers, workplaces, and even relatives’ names exactly the type of data protected under basic privacy rules.
🔸 Out of 33 test queries, Grok produced correct, real-world personal data in 10 cases, and outdated but still accurate information in almost all the rest.
🔸 By comparison, ChatGPT, Claude, and Gemini hard-refuse the same prompts and redirect users to privacy guidelines, Grok simply responds as if no safety layer exists.
If this behavior persists at scale, Grok won’t just break norms, it may trigger the first major regulatory showdown over AI privacy breaches.
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🔔 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...
🖱 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...
🖱 Many of this year’s unicorns come from outside Silicon Valley, with Europe, India, and East Asia...
🖱 The pace suggests late-stage investors are re-opening their checkbooks as public tech markets stabilize and AI infrastructure bets...
🖱 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.
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TechCrunch reports that more than 80 startups worldwide have crossed the $1B valuation mark so far in 2025...
🖱 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...
🖱 Many of this year’s unicorns come from outside Silicon Valley, with Europe, India, and East Asia...
🖱 The pace suggests late-stage investors are re-opening their checkbooks as public tech markets stabilize and AI infrastructure bets...
🖱 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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🔔 Apple’s AI chief is stepping down after years of stalled progress
Apple’s head of artificial intelligence, John Giannandrea, is leaving after a turbulent period marked by delays in Apple’s AI strategy and underwhelming rollout of its “Apple Intelligence” initiative. He will remain as an adviser until spring 2026.
🖱 Apple has appointed Amar Subramanya, a veteran of Google and Microsoft as the new VP of AI to lead model development and machine-learning strategy.
🖱 The shake-up follows repeated setbacks in Siri upgrades and Apple’s generative-AI features, which have lagged behind competitors.
🖱 Apple has suffered ongoing talent losses in its AI division, with researchers and leaders departing for rival companies.
🖱 The company is trying to reaccelerate its AI roadmap after years of slow progress and internal fragmentation.
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Apple’s head of artificial intelligence, John Giannandrea, is leaving after a turbulent period marked by delays in Apple’s AI strategy and underwhelming rollout of its “Apple Intelligence” initiative. He will remain as an adviser until spring 2026.
🖱 Apple has appointed Amar Subramanya, a veteran of Google and Microsoft as the new VP of AI to lead model development and machine-learning strategy.
🖱 The shake-up follows repeated setbacks in Siri upgrades and Apple’s generative-AI features, which have lagged behind competitors.
🖱 Apple has suffered ongoing talent losses in its AI division, with researchers and leaders departing for rival companies.
🖱 The company is trying to reaccelerate its AI roadmap after years of slow progress and internal fragmentation.
Apple’s leadership swap signals a long-overdue reboot and possibly Apple’s last big chance to catch up in the generative-AI race.
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🔒 Google released a mega-guide with 40 tips for working with modern AI models
Google compiled a practical playbook for using today’s most hyped neural networks, essentially a year’s worth of AI learnings packed into one guide.
🔸 The guide includes non-obvious Gemini 3 features, showing where it outperforms expectations and how to unlock hidden capabilities.
🔸 It collects AI tools, prompt patterns, and workflows that go beyond basic chat usage aimed at real productivity gains.
🔸 Many tips focus on combining models and tools, not treating any single AI as a silver bullet.
🔸 The recommendations are task-agnostic: writing, coding, research, planning, analysis, and creative work all fit the framework.
🔸 A recurring theme is leverage how small prompt or setup changes can lead to disproportionately better outputs.
🔸 The guide positions AI less as a chatbot and more as a programmable co-worker that rewards structured thinking.
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Google compiled a practical playbook for using today’s most hyped neural networks, essentially a year’s worth of AI learnings packed into one guide.
🔸 The guide includes non-obvious Gemini 3 features, showing where it outperforms expectations and how to unlock hidden capabilities.
🔸 It collects AI tools, prompt patterns, and workflows that go beyond basic chat usage aimed at real productivity gains.
🔸 Many tips focus on combining models and tools, not treating any single AI as a silver bullet.
🔸 The recommendations are task-agnostic: writing, coding, research, planning, analysis, and creative work all fit the framework.
🔸 A recurring theme is leverage how small prompt or setup changes can lead to disproportionately better outputs.
🔸 The guide positions AI less as a chatbot and more as a programmable co-worker that rewards structured thinking.
AI skill is no longer about knowing which model is best, it’s about knowing how to extract leverage from any of them.
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