💰 OpenAI is reportedly raising again and the number is eye-watering

OpenAI is preparing a new funding round of up to $100 billion, with a target valuation of roughly $830 billion.

🖱 Just a few months ago, in October, OpenAI was valued at around $500B. Earlier in 2025, estimates were closer to $300B making this a dramatic step-up in a very short time.

🖱Sources cited by the WSJ say the company’s cash reserves are thinning, largely due to the soaring costs of training and running new frontier models.

🖱 While OpenAI has secured sizable cloud credit deals, insiders suggest that inference costs are still mostly paid in cash, not credits.

🖱 To ship new models, the company has reportedly had to piece together funding wherever possible, underscoring how capital-intensive frontier AI has become.

This round underlines a new reality: even the most powerful AI company in the world needs ever-larger pools of capital just to stay at the frontier.

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🧠 “Big Short” investor Michael Burry drops another market warning, this time about AI, Nvidia, and China

Legendary contrarian investor Michael Burry, famous for profiting from the 2008 housing crash is sounding the alarm again, this time about the AI sector and U.S. competitiveness.

🖱 In recent posts on X (formerly Twitter), Burry argued that America’s reliance on Nvidia’s ever-more power-hungry AI chips could be a structural disadvantage, especially compared with China’s rapidly expanding energy infrastructure.

🖱 He says the prevailing narrative that simply scaling up GPU power and electricity use wins the AI race ignores the reality that China may outpace the U.S. in electricity generation capacity, giving it a strategic edge for large-scale AI compute.

🖱 Burry suggests the U.S. should pivot toward energy-efficient, AI-specific silicon (like ASICs) rather than doubling down on brute-force GPUs, a view that challenges the current industry playbook.

🖱 Behind the messaging is Burry’s broader critique of today’s AI boom: he believes Nvidia has too much influence, that AI stocks may be overvalued, and that the market is setting itself up for disappointment.

Burry’s warning isn’t just about Nvidia, it’s a strategic AI race warning that touches on energy, infrastructure, and where innovation bets should be placed.

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✅ The Pentagon adopts Grok as a classified AI system

The US Department of Defense has signed a contract with xAI to deploy a specialized version of Grok across the Pentagon, marking one of the most significant government adoptions of a frontier AI model to date.

🖱 The system, branded “Grok for Government”, will be rolled out to roughly 3 million military personnel and civilian employees across the Department of Defense.

🖱 This is not the consumer version of Grok, but a customized model designed specifically for defense, intelligence, and operational use cases.

🖱 Grok for Government is certified at Impact Level 5 (IL5), meaning it is approved to work with confidential and classified data inside secure DoD environments.

🖱 The platform is expected to support tasks such as analysis, decision support, and intelligence workflows rather than general-purpose chat or content generation.

🖱 A key and controversial detail: Grok will reportedly provide access to online data from X, though official descriptions remain vague.

🖱 In practice, this likely enables operational intelligence use cases analyzing information flows, detecting disinformation, geolocating events, and measuring audience sentiment in near real time.

🖱 The integration effectively turns X into a live OSINT layer feeding directly into US defense and intelligence systems via an AI interface.

🖱 Strategically, the deal signals a shift in how governments view AI models not as productivity tools, but as core intelligence infrastructure.

This isn’t just the Pentagon using an LLM, it’s the formal fusion of AI, social media, and classified environments, where control of the information layer becomes a strategic asset rather than a side channel.

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⚡️ Data centers are catching up with offices, AI is driving construction

Spending on data centers in the US has almost matched and may already have surpassed spending on office construction. August 2025 data shows the gap narrowing fast, driven by one factor: AI’s exploding demand for computing power.

🖱 In the US, data center investment has surged as AI workloads scale, while office construction continues to stagnate. If the crossover hasn’t happened yet, it likely will very soon.

🖱 In Russia, there’s no public data on spending, but capacity figures tell a similar though slower story. According to Kommersant citing iKS-Consulting, commercial data center capacity nearly doubled over five years: from 43,000 racks in 2019 to 81,000 in 2024.

🖱 Growth is now decelerating. In 2024, the market added 10,900 racks (below the expected 12,000). For 2025, analysts forecast only 4,600 new racks.

🖱 Why the slowdown? Analysts point to reduced foreign investment after 2022, high key interest rates pushing up borrowing costs, more expensive equipment, and difficulties paying for imported supplies.

iKS-Consulting says most data center projects planned or under construction are being postponed to 2026–2027, turning rapid expansion into a temporary pause rather than a full stop.

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💵 The dollar is still dominant but its grip is slowly loosening

The US dollar remains the world’s main reserve currency, but its share of global reserves continues a long, gradual decline, a trend that’s more about diversification than collapse.

🖱 As of 2025, the dollar accounts for roughly 57% of global foreign-exchange reserves. That’s down from more than 70% in the early 2000s, but still far ahead of any competitor.

🖱 The euro remains the clear No. 2 reserve currency at around 20%, followed by the Japanese yen and British pound, each in the mid-single digits. No alternative currency is even close to challenging the dollar’s scale.

🖱 Why the share is falling: central banks are slowly diversifying reserves, geopolitical risks have increased, and valuation effects (exchange rates moving against the dollar) also play a role not just active selling.

🖱 What hasn’t changed: most global trade, debt issuance, commodities, and financial infrastructure are still dollar-centric. Even countries trying to “de-dollarize” tend to reduce exposure at the margin, not abandon the dollar entirely.

The dollar is losing share, not status, diversification is happening, but there is still no realistic replacement for the dollar as the backbone of the global financial system.

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🚫 The US is shutting the door on new foreign-made drones and DJI is the biggest casualty

The Trump administration’s ban on foreign-made drones officially kicks in this week, cutting off approval for new models from companies like DJI and reshaping the US drone market overnight.

🖱 The FCC has added foreign-made drones and key components to its national security “Covered List,” meaning new models can no longer receive authorization for sale or import in the US.

🖱 For consumers: existing DJI drones are not banned. If you already own one, you can still fly it. The ban targets new models and future approvals, not devices already in circulation.

🖱 DJI is the clear loser. As the dominant global drone maker, it effectively loses access to the US consumer and commercial market for future products unless exemptions are granted.

🖱 The rationale: US officials argue foreign drones pose national security and data-collection risks, especially for critical infrastructure, public safety, and government use.

🖱 The broader goal: reduce reliance on Chinese hardware and force a shift toward US-made or “trusted” allied drones, even if they’re currently more expensive or less advanced.

This isn’t the end of DJI drones in America, but it is the end of business as usual. The ban blocks the future, not the past, and signals a long-term decoupling of hardware supply chains in sensitive tech.

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🧠 Nvidia is set for its biggest AI chip move yet with Groq

Nvidia has agreed to buy AI chip startup Groq for about $20 billion in cash, a deal that if completed would be the largest in its history and a major expansion of its AI hardware footprint.

🖱 Groq specializes in high-performance AI accelerator chips (Language Processing Units, or LPUs) that are designed to run inference tasks the real-time work of responding to AI model queries faster and more efficiently than many traditional GPUs.

🖱 The acquisition reportedly excludes Groq’s early-stage cloud business, which will continue separately, and comes only months after Groq raised $750 million at roughly a $6.9 billion valuation.

🖱 Nvidia plans to integrate Groq’s technology into its AI stack, strengthening its capabilities across both training (with its GPUs) and inference workloads, marking a strategic push deeper into end-to-end AI hardware.

🖱 Groq’s founders and key executives are expected to join Nvidia, helping transfer expertise even as Groq continues some independent operations under new leadership.

This $20 billion move underscores Nvidia’s dominance in AI silicon and reflects a broader race among major tech players to control both training and inference compute at scale.

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✅ Early-stage investing cheat sheet (Carta-style ranges)

These are guidelines, not rules. Most relevant for San Francisco & New York. Non-AI software and non-hot markets typically price lower.

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🔔 OpenAI’s push toward ads inside ChatGPT starts to take shape

OpenAI is quietly laying the groundwork for an advertising business inside ChatGPT, signaling a potential shift away from relying only on subscriptions and API revenue.

🖱 Internal teams have discussed native ad formats, including sponsored answers or recommendations that appear directly inside ChatGPT conversations, rather than traditional banner ads.

🖱 Early mockups reportedly explore context-aware ads, tied to what users are asking for, such as travel, shopping, or local services.

🖱 OpenAI is said to be building ad infrastructure internally, suggesting long-term intent rather than short-term experiments.

🖱 Leadership has previously downplayed ads, making this a notable change as compute costs rise and ChatGPT’s user base scales into the hundreds of millions.

🖱 The biggest risk remains user trust, especially if ads blend too closely with answers, raising questions about neutrality and transparency.

If ads arrive, ChatGPT wouldn’t just be an AI product, it would become a new distribution channel, putting OpenAI on a collision course with search and social advertising giants.

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🧠 Netflix’s rise from DVD startup to global streaming powerhouse

Netflix began in the late 1990s as a DVD-by-mail rental service, a business that looked odd compared to dominant players like Blockbuster, which had thousands of physical stores and seemed untouchable. Netflix avoided brick-and-mortar costs, focused on online ordering, eliminated late fees, and introduced a subscription model with free trial periods, moves that were unconventional at the time but made the service more user-friendly.

🖱 Disruption over imitation: Netflix’s early model didn’t chase the latest movies or mimic Blockbuster’s rental strategy. Instead, it reimagined convenience and pricing for customers, which helped it build a loyal base even as traditional video rental revenues collapsed.

🖱 Blockbuster missed its pivot: Around 2000, Netflix offered to partner with Blockbuster, but Blockbuster’s leadership rejected the opportunity, underestimating online DVD subscriptions. Blockbuster later launched initiatives to counter Netflix, but those moves blew through cash and failed to stop the disruption, ultimately leading to bankruptcy.

🖱 From mail to streaming: Netflix successfully transitioned from mailing discs to internet streaming, anticipating where customer demand and technology were headed. That pivot helped it become a dominant global OTT platform with original content and tens of millions of subscribers.

🖱 Disruption mindset: Netflix’s journey illustrates how a startup can upend an established industry by challenging entrenched business models and leveraging new delivery methods, classic disruption theory in action.

Netflix didn’t just grow, it reshaped the market by betting early on online convenience and subscription pricing. Blockbuster’s failure to adapt highlights how innovation can overturn dominant players when customer habits and technology shift.

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64GB of RAM in the USA cost more than a semi-automatic AR-15 rifle

Amazing timeline…

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📉 OpenAI loses half of its enterprise LLM market share in two years

Menlo’s latest annual AI market report points to a sharp power shift in the enterprise LLM API market, as OpenAI’s early dominance continues to erode.

🖱 Since 2023, OpenAI’s share of the enterprise LLM API market has fallen from roughly 50% to 27%, a dramatic decline over just two years.

🖱 Anthropic is now the clear leader, controlling about 40% of the market nearly triple its share in 2023 driven by strong enterprise adoption of Claude.

🖱 Google sits in third place with ~21%, benefiting from tight integration with its cloud, data, and enterprise distribution channels.

🖱 The market is increasingly multi-vendor, as enterprises hedge risk, avoid lock-in, and route workloads to different models based on cost, safety, or context length.

🖱 OpenAI remains a major player, but no longer the default choice, as competition on reliability, governance, and enterprise fit intensifies.

The LLM API market has shifted from winner-takes-all to fragmented infrastructure and OpenAI is now competing on equal footing, not from a monopoly position.

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🇨🇳 AI exam for Apple in China

China is formalizing one of the world’s strictest AI approval regimes and Apple Intelligence is now directly in its scope.

🖱 According to The Wall Street Journal, every chatbot entering the Chinese market must pass a 2,000-question government exam designed to surface responses on prohibited topics.

🖱 To pass, a model must refuse to answer at least 95% of politically or socially sensitive prompts. The question bank is updated monthly, forcing continuous re-certification.

🖱 Enforcement is massive: in just three months, regulators removed 960,000 units of “illegal” AI content and shut down 3,500 AI products.

🖱 AI is now officially framed as a national security risk, on par with earthquakes and epidemics. Xi Jinping has called it an “unprecedented risk,” while a senior aide likened unregulated AI to “driving on a highway without brakes.”

🖱 This directly impacts Apple Intelligence. Outside China, Siri escalates complex queries to ChatGPT, and some workloads rely on Gemini on Apple’s servers but in China, Apple is effectively forced to use a local partner.

🖱 That partner is Alibaba, with Apple relying on the Qwen3 model to comply with local regulations.

🖱 A notable side effect: researchers at the Carnegie Endowment find Chinese models score better on some safety metrics (less violence, pornography, and self-harm), but are easier to jailbreak using English prompts.

China isn’t just regulating AI, it’s stress-testing whether global AI platforms can exist as fully localized, state-compatible systems without breaking their core product experience.

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🚕 Closing a door in a Waymo robotaxi isn’t free

Waymo’s driverless taxi rollout across the US hides a growing operational cost: human intervention for edge cases autonomy still can’t handle.

🖱 According to The Washington Post, Waymo relies on an on-demand network of human “rescue workers” to assist robotaxis when they become immobilized.

🖱 One of the most common failures is mundane: if a passenger exits without fully closing the door, the vehicle refuses to move and must be manually assisted.

🖱 These helpers are dispatched via Honk, a roadside-assistance marketplace founded in 2014 that operates like Uber for car help and has raised $33 million.

🖱 Rescue workers report being paid $22–24 just to close a door on a stalled Waymo vehicle.

🖱 More complex interventions pay more: $60–80 to tow a robotaxi that ran out of battery or failed to reach a charging station.

🖱 The process is inefficient at times workers say they’re often given imprecise locations, spending up to an hour searching city streets for the vehicle.

Waymo removed the driver, not the humans autonomy still depends on a hidden workforce handling the last, expensive edge cases.

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📈 Elon Musk predicts double-digit GDP growth and triple-digit AI upside

Elon Musk says global GDP could grow 10%+ in the next 12–18 months, and that if applied AI becomes a proxy for economic output, 100%+ annual growth could be possible within five years.

🖱 Musk argues applied AI is no longer theoretical, it is directly improving productivity in software, manufacturing, logistics, and science.

🖱 If AI output scales faster than labor or capital constraints, traditional GDP models may understate real economic expansion.

🖱 Musk frames AI as a general productivity multiplier, similar to electricity or the internet but with faster compounding.

🖱 Critics note that GDP growth is constrained by energy, regulation, and real-world deployment speed, even if AI capability advances rapidly.

🖱 In response, a public challenge emerged: name at least one Musk prediction or promise that actually came true.

🖱 Common answers include reusable rockets (SpaceX), private human spaceflight, EVs outperforming ICE cars, and global satellite internet via Starlink.

Musk’s forecasts remain extreme but history shows he has repeatedly been early rather than wrong, which is why markets still pay attention.

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📊 India startup funding hits $11B in 2025 as investors grow more selective

India’s startup ecosystem raised nearly $11 billion in 2025, but the pace and structure of deals shifted significantly compared with previous years signaling a maturing market and more cautious investor behavior.

🖱 Total funding slid modestly relative to 2024, while the number of funding rounds fell sharply (~39%), underscoring greater selectivity among investors.

🖱 Seed-stage funding dropped around 30%, reflecting pullback on early, high-risk bets, while late-stage funding also cooled amid stricter scrutiny on scale and profitability.

🖱 Early-stage funding grew (~7%), indicating investor interest in startups with strong product–market fit, revenue visibility, and unit economics.

🖱 AI funding in India grew modestly (~4%) and was mostly directed at application-led startups, contrasting sharply with the U.S. AI boom where funding surged far more and was dominated by late-stage rounds.

🖱 Activity became more concentrated: a smaller group of lead investors drove most rounds, and domestic capital played a larger role as some global investors pulled back.

🖱 Government initiatives including a $1.15 B Fund of Funds and a ₹1 trillion R&D & innovation scheme helped catalyze funding, especially for deep tech sectors like energy transition, quantum, robotics, biotech, and space.

India’s startup funding in 2025 remained robust in absolute terms, but the ecosystem is shifting toward more disciplined, quality-focused capital deployment, with emphasis on early-stage performance, pragmatic AI bets, and deeper involvement from domestic investors and public sector support.

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📉 Inflated startup valuations are setting up painful corrections and AI looks next

A Crunchbase analysis warns that hype-driven valuations, not fundamentals, are once again shaping startup funding with AI showing many of the same warning signs seen in past bubbles.

🖱 In previous cycles, startups raised at extreme revenue multiples without solid unit economics, leaving them vulnerable once markets tightened.

🖱 When follow-on rounds failed to materialize at higher prices, many companies were forced into down rounds, shutdowns, or fire-sale acquisitions.

🖱 Inflated valuations distorted decision-making, encouraging aggressive burn rates under the assumption that capital would always be available.

🖱 The article argues that early-stage AI startups are now raising on narrative and assumed future dominance, often before product–market fit or durable revenue.

🖱 As hype fades, only AI companies with strong margins, real customers, and disciplined spending are likely to survive the correction.

🖱 Founders are urged to prioritize runway, fundamentals, and realistic pricing over headline valuations.

The lesson from past cycles is clear: valuation is not success and in AI, today’s inflated prices could become tomorrow’s biggest liability.

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🔔 OpenAI hires a Head of Preparedness as AI risks shift from theory to reality

OpenAI is introducing a new senior role, Head of Preparedness, focused not on mitigating model flaws, but on anticipating what happens after powerful AI systems are released into the world.

🖱 Unlike traditional safety roles, this position is about modeling second- and third-order effects that can’t be reliably tested in labs or benchmarks.

🖱 The mandate includes identifying non-obvious danger zones: long-term societal risks, emergent misuse patterns, and failure modes that only appear at scale.

🖱 Sam Altman says 2025 brought the first serious warning signal: measurable negative impacts on mental health, including reports linking ChatGPT use to suicides.

🖱 At the same time, strong coding capabilities elevate risks in cybercrime, automation of attacks, and lowering the barrier for sophisticated exploitation.

🖱 The role exists because model capabilities are now high enough that downstream consequences matter as much as model alignment itself.

🖱 Compensation is up to $555k including bonuses, serious money, though modest by frontier-AI executive standards.

AI labs are beginning to treat post-deployment societal impact as a core strategic risk, the question is whether this becomes real governance or polished reassurance.

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📉 Nvidia finalizes $5B strategic stake in Intel, a major shakeup in the chip world

Nvidia has completed a $5 billion purchase of Intel shares under a private placement agreement first announced in September 2025, buying about 214.7 million shares at $23.28 each.

🖱 The deal was executed after U.S. antitrust clearance and is viewed as a significant financial lifeline for Intel, which has struggled with expensive production expansions and recent strategic setbacks.

🖱 Nvidia’s shares dipped modestly in pre-market trading after the disclosure, while Intel’s stock was relatively unchanged.

🖱 The investment gives Nvidia a notable minority position in Intel and marks one of the most unusual equity moves between two major U.S. chipmakers in years.

🖱 Analysts see it as a vote of confidence in Intel’s turnaround and a way for Intel to raise cash without debt, even as it expands its foundry and AI-focused chip efforts.

🖱 Beyond finance, the agreement signals closer strategic ties between the companies going into 2026 especially in AI infrastructure and advanced processor development.

A $5 billion vote of confidence from Nvidia could reshape competitive dynamics in AI chips, bolster Intel’s balance sheet, and deepen collaboration at a pivotal moment for the semiconductor industry.

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⚠️Nvidia quietly exits the cloud business and steps back from competing with AWS

Nvidia has effectively abandoned plans to run its own cloud service, folding DGX Cloud back into an internal-only operation and redeploying most of the team into R&D.

🖱 DGX Cloud technically still exists as an org chart item, but it now serves only Nvidia’s internal compute needs, not external customers.

🖱 Demand for the service was minimal, which appears to be the real reason for the shutdown, the market showed little interest in Nvidia as a cloud provider.

🖱 The move signals a clear retreat from competing with hyperscalers, especially AWS, rather than a strategic pause.

🖱 Notably, AWS previously refused to participate in the DGX Cloud program, drawing a hard line against Nvidia encroaching on its core business.

🖱 By shutting DGX Cloud down, Jensen Huang publicly reassures Nvidia’s biggest customers that it will remain a supplier, not a rival.

🖱 Resources shifting to R&D reinforces Nvidia’s core strategy: sell picks and shovels for AI, not run the gold mine itself.

Nvidia chose ecosystem dominance over platform ambition and decided that angering AWS, Azure, and Google was far riskier than abandoning its own cloud dreams.

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🔔Meta buys Manus to jump-start its AI agent strategy

Meta has acquired Manus, the Singapore-based AI startup, in a deal reportedly valued at ~US$2B, betting on agentic AI that can do work, not just answer questions.

🖱 Manus builds general-purpose AI agents that autonomously execute tasks, research, document analysis, hiring workflows, coding, and business automation.

🖱 Unlike most AI labs, Manus already had real revenue: over US$100M ARR within months of launch, proving demand beyond demos.

🖱 The startup moved its HQ to Singapore in 2025, distancing itself from China exposure and making it acquisition-friendly for US Big Tech.

🖱 Meta plans to keep Manus as a standalone product while integrating its agent tech into Meta AI, WhatsApp, Instagram, and enterprise tools.

🖱 Strategically, this helps Meta close the gap with OpenAI, Microsoft, and Google, who are all racing to own the “AI that actually works for you” layer.

🖱 The deal signals Meta’s shift from pure model competition to applied, monetizable AI systems that justify its massive AI capex.

🖱 Buying Manus is faster than building: Meta gets product-market fit, paying customers, and a team that already cracked agent execution.

Meta isn’t just chasing smarter models anymore, it’s buying its way into the operational AI era, where agents replace workflows, not chats.

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