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- 🤑Alphabet's $900B Chip Opportunity
🤑Alphabet's $900B Chip Opportunity
PLUS: BlackRock Expects Turbulent AI | Trump HHS Embraces AI
Reading time: 5 minutes
🗞️In this edition
Alphabet's TPU chips could generate $900B business
Sponsored: Remio - AI powered personal knowledge hub
BlackRock expects AI to dominate 2026 with volatility
HHS embraces AI with "try-first" culture under RFK Jr.
In other AI news –
Meta scales back metaverse unit while pushing Ar glasses forward
Meta tests AI powered support assistant across Facebook and Instagram
Snowflake users get access to Claude and custom AI agents
4 must-try AI tools
Hey there,
Alphabet's stock surged 31% in Q4 as investors bet its TPU chips could become a $900 billion business, potentially worth more than Google Cloud itself. BlackRock's CIO warned AI markets face a "rocky ride" despite upward trends, citing near-record hedge fund leverage that amplifies selloffs. And the Trump administration's HHS rolled out a "try-first" AI strategy with ChatGPT for all employees, raising questions about patient data protection on sensitive health records.
We're committed to keeping this the sharpest AI newsletter in your inbox. No fluff, no hype. Just the moves that'll matter when you look back six months from now.
Let's get into it.
What's happening:
Alphabet investors are growing confident the company's semiconductors could represent a significant driver of future revenue.
The success of Alphabet's tensor processing unit chips is the primary reason for the stock's 31% Q4 rally, the tenth best performance in the S & P 500. TPUs were always seen as a major strength internally, accelerating growth for cloud business. But there's rising optimism Alphabet could start selling chips to third parties, creating a new revenue stream ultimately worth almost trillion dollars.
"If companies want to diversify away from Nvidia, TPUs are a good way to do it," said Gil Luria, head of technology research at DA Davidson. "Chip business could ultimately be worth more than Google Cloud." Should Alphabet get serious about selling TPUs, Luria estimates they could capture 20% of the AI market over a few years, making it roughly a $900B business.
TPUs are application-specific integrated circuit chips, custom designed for particular use, in this case to accelerate machine learning workloads. That makes them less flexible than Nvidia semiconductors but also cheaper, a real benefit when investors question AI spending.
Morgan Stanley analysts expect about 5M TPUs bought in 2027, up roughly 67% from previous estimates, and 7M in 2028, 120% above prior estimates.
Every 500,000 TPU chips sold to third-party data centres could add about $13B to Alphabet's 2027 revenue and 40 cents to earnings per share. Alphabet expected to post roughly $447B revenue in 2027, so adding $13B would boost sales almost 3%.
Why this is important:
The $900B potential chip business is larger than Google Cloud's current trajectory and positions Alphabet as a credible Nvidia alternative.
TPUs being cheaper than Nvidia while optimized for specific workloads addresses investor concerns about AI spending efficiency.
20% market share estimate within a few years is aggressive but based on actual customer adoption (Anthropic, potential Meta deal).
Our personal take on it at OpenTools:
The $900B chip business estimate is wildly optimistic.
Capturing 20% of the AI chip market when Nvidia has 80-95% share and proven ecosystem requires Alphabet aggressively selling TPUs externally. They haven't committed to that strategy yet.
Every 500K TPUs adding $13B revenue sounds impressive until you realize that's a small fraction of Alphabet's $447B projected 2027 revenue. A 3% boost isn't nothing but isn't transformative either.
Alphabet has the advantage of vertical integration: Gemini optimized for TPUs, Google Cloud selling TPU access, and potential third-party sales. But monetizing that advantage through direct chip sales is different from using internally.
This is investors' front-running potential strategy that Alphabet hasn't fully committed to. Optimism is warranted but $900B valuation is premature.
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What's happening:
BlackRock, the world's largest asset manager, expects AI to dominate markets in 2026 but anticipates turbulent ride for investors as speculative trading and leverage raise risks of repeating November's sharp selloff.
Helen Jewell, BlackRock's CIO of fundamental equities EMEA, said AI-linked investment returns would stay on an upward trend but warned: "Do I think that there is likely to be a rocky ride as we go there. Also yes," citing crowding and leverage as key reasons for fluctuations.
Doubts about AI groups overspending on data centers caused the biggest US stock market pullback in months in November. Hedge funds are trading with near-record leverage, raising risks for fast selloffs if asset price declines force liquidation to meet lenders' requirements.
Jewell said BlackRock is adding positions in European energy and power infrastructure groups like Siemens Energy as the AI boom and data center rush boost demand for turbines, grid technology, and clean energy.
Why this is important:
World's largest asset manager warning about "rocky ride" while staying invested signals expectation of continued volatility.
Near-record hedge fund leverage means market selloffs amplify quickly as forced liquidation cascades. November pullback demonstrated this dynamic.
BlackRock adding European energy infrastructure positions while warning about AI volatility shows they're positioning for AI infrastructure buildout beneficiaries, not just software/chip companies.
Our personal take on it at OpenTools:
BlackRock's dual message is hedged positioning: AI uptrend continues but volatility ahead.
"Incredible capital spends by companies with incredible cash" is validation of infrastructure buildout thesis. Apple, Microsoft, Google, Meta, Amazon have balance sheets to sustain spending even if returns lag.
But "crowding and leverage" warning is a key caveat. When everyone's long same trade using borrowed money, any catalyst triggers cascade selling. November demonstrated this.
Near-record hedge fund leverage is most concerning detail. Leverage amplifies returns on way up and losses on way down. Small decline becomes large liquidation when margin calls hit.
Adding European energy infrastructure (Siemens Energy) is smart derivative play on AI. Don't buy AI companies directly, buy companies supplying power and grid infrastructure for data centers. Lower multiple, less volatility, essential for AI buildout.
This is institutional investor playbook: stay exposed to AI uptrend but diversify into infrastructure beneficiaries and hedge volatility risk. That's more conservative than pure AI exposure but acknowledges risks Jewell outlined.
What's happening:
HHS outlined a strategy Thursday to expand AI use, building on the Trump administration's embrace of the technology while raising questions about health information protection.
The 20-page plan focuses on making work more efficient and coordinating AI adoption across divisions. It also promotes AI innovation in patient health data analysis and drug development.
Deputy HHS Secretary Jim O'Neill wrote: "For too long, our Department has been bogged down by bureaucracy and busy-work. It is time to tear down these barriers to progress."
The strategy calls for a "try-first" culture to help staff become more productive. HHS made ChatGPT available to every employee earlier this year.
Five key pillars include governance structure managing risk, AI resources across departments, empowering employees to use AI tools, funding programs setting standards for research, and incorporating AI in public health and patient care.
HHS divisions are working on AI "to deliver personalized, context-aware health guidance to patients by securely accessing and interpreting their medical records in real time."
HHS had 271 active or planned AI implementations in 2024, projecting 70% increase in 2025.
Why this is important:
"Try-first" culture with ChatGPT access for all HHS employees means experimenting with AI on sensitive health data before establishing comprehensive safeguards.
Real-time access to medical records for "personalized, context-aware guidance" is patient data mining without clear privacy protections detailed in strategy.
HHS previously handed Medicaid recipients' health data to ICE, showing a history of pushing legal boundaries on sensitive data sharing.
70% increase in AI implementations (271 to 460+) in one year is rapid scaling without demonstrated governance capacity.
Our personal take on it at OpenTools:
"Tear down barriers to progress" is concerning framing for health data handling.
O'Neill's language about removing "bureaucracy and busy-work" conflates administrative efficiency with patient data protection. Those are different concerns requiring different standards.
Real-time access to medical records for AI-generated guidance sounds beneficial but lacks detail on data retention, model training, third-party access, and patient consent mechanisms.
Trump repealing Biden's AI executive order removed guardrails HHS now operates without. The strategy promises "gold standard science" and risk assessments but doesn't specify enforcement mechanisms or consequences for violations.
This could modernize HHS operations as suggested, but speed prioritized over safety creates preventable risks with irreversible consequences for patient privacy.
Meta Weighs Cuts to Its Metaverse Unit – Meta plans to direct its investments to focus on wearables like its augmented reality glasses but does not plan to abandon building the metaverse.
Meta centralizes Facebook and Instagram support, tests AI support assistant – The new AI assistant being tested is designed to offer more personalized help with things like account recovery, managing your profile, or updating your settings.
Anthropic signs $200M deal to bring its LLMs to Snowflake’s customers – Snowflake said its customers will be able to tap Claude models, including Claude Opus 4.5, to run multimodal data analysis. Customers will also be able to use the models to build their own custom agents.
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