Robert Paul Leitao on Premarket: Apple is red - 'At 1pm in the east Apple is up $1.29 at $327.88. Apple supplier Broadcom is ahead $8.46 or 2.24% at $386.62. It’s another good day for Goldman Sachs. The shares are up $32.27 at $1,087.30 and Morgan Stanley is also higher. The shares are up $6.29 at $217.23. The tech-heavy NASDAQ Composite has advanced 1.39%. All four major indexes are in the green.'
on Premarket: Apple is red - 'Also, AAPL doubled in price over the last 3 very rocky years. If it doubles like that again, AAPL will hit $660/share around this time of year, 2029.'
on Premarket: Apple is red - 'A couple of other data points: In the last two quarters, AAPL has gone up 32.4%, NVDA has gone up 11.9%, and MSFT has gone down 10.2%z. Meanwhile, the Dow has gone up 6.6%, the S&P has gone up 9.2%, and the the NASDAQ has gone up 11.3%.'
on Premarket: Apple is red - 'Friday, Max Pain was $300. Yesterday, Max Pain was “broken”. Today, it’s $325. At the open, AAPL was $323.34. The high so far today (9:43 AM PDT) was $329.60. Options are being dragged higher, kicking and screaming. What’s new? “These aren’t the droids we’re looking for. Move along.”'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - 'Wow! This is an amazing thread. Thanks for compiling this.'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - 'Bart: Thank you for this series of posts that I found extremely informative, even though on the technical side a lot of it was over my head. I’m sure I’m not the only one who really appreciates the way you occasionally do these “deep dives” and share the results with this community.'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - 'I wonder if AI will have its own version of Moore’s Law, where the models get more powerful AND smaller at an exponential rate… It wouldn’t surprise me. The ability to shrink AI models definitely works in Apple’s favor. Model shrinkage could make used phones more valuable, as something that won’t fit in an iPhone 17 this year might well fit 2 or 3 years from now.'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - 'Hi Joseph, I don’t think anyone claimed there was something new here, certainly not me. After all, Gemini is trained on what is past and currently available, including people’s thoughts and ideas about Apple, AI and a whole host of things that could, are, and will affect all of them. My purpose for the questions and sharing the responses was to bring up the reasons, rationales, and ideas about how this whole AI thing, where’s it’s possibly headed, Apple’s known thinking, and some speculation based on slivers of Apple roadmaps that may or may not see the light of day. I think that’s worthy of discussion here, or at least contemplation. I feel I learned some new things about how Hyperscalers are trying to enter and create an entirely new technology and business. Considering the huge volumes of expenses they are generating, to me it’s worthwhile trying to understand how and when they think it will pay off, at least before some part of it burns up or implodes. Meanwhile, a lot of that energy and costs have materially affected Apple, especially the “ever behind in AI” scenarios and now the Memory Supply and Price Spikes. That many here saw Apple’s “different” AI approach as being not only prudent but potentially a winning hand (at least one of the winners) is not lost on me, but where the puck is going is a bit harder to discern. Here’s a couple of thoughts: 1) Apple has roadmapped its AI servers for at least 5-10 years, but like any project, there’s technical hurdles to overcome, hence M5 Ultra forthcoming, M7 Ultra already drawn up for 2nm or less, and Baltra needing some additional tech from Broadcom. 2) At the same time, Apple silicon A20 and beyond is also roadmapped to handle Edge AI and more, plus continued advances in modem C-series, networking, RF, Bluetooth and other chips will add value and cost containment, assuming Apple still has some significant capacity with TSMC and now Intel. 3) The memory crunch may prove resilient, may prove relatively transitory, depending on how the market reacts to continued AI spending without significant ROI y not only the Hyperscalers, but those clients and customers who also need to see ROI in their RPO contracts. If there is the slightest hint of hiccups, datacenter usage, server and hardware purchasing may slow, possibly abruptly. While DRAM memory is still in high demand and constrained supply, there may be more supply available within 6-12-18 months coinciding with that potential slowdown, leading to, just maybe, a significant drop in HBM demand and a need for memory makers to switch up supply for DRAM and NAND memory to sustain revenues and profits, or at least prevent severe declines. Typical boom and bust memory cycle. 4) with Ternus at the helm, and some hints at a robust product roadmap, I think iPhone 18, Ultra Foldable, an enhanced Air2, and then iPhone 20 will be very attractive, albeit with a memory cost hangover. If Apple can get through that cycle relatively unfazed margin wise, relief may come by late 2027. Whether Apple then is able to or wants to reduce prices will be an interesting time. 5) I feel confident in Ternus & Srouji’s hardware commitments and roadmap, which leads to conquest sales, Android switchers, and greater market share of the premium tiers, plus that leads to a very self-sufficient and self revenue generating install base as it come to upgrades and Services engagement worldwide. 6) Apple fortunes in China have been on the upswing and now AI approval there gives Apple Chinese owners another use case to test out and see if it enhances their iOS experience. While it isn’t a driving force for upgrades, a useful and effective AI in China, writhing governmental restrictions or regulation, makes Apple at least evenly competitive. Whether any of that is new or not, it’s an investment thesis that bodes well for Apple, barring regulatory, geopolitical or trade black swans, which are always near the horizon. Let’s hope Apple, Ternus, Cook, and Parekh can skillfully navigate these unsettled waters.'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - 'Bart: Thank you for this series of thoughtful and informative posts. I’m am investing serious time in research on equities in this very dynamic technology era. I believe Apple has been wise to not only decide to raise prices to maintain product quality but also to move off of the net cash neutral goal as a means to maintain adequate liquidity for future strategic investments. I was quite heartened to read of the renewed chip agreement between Apple and Broadcom. As a matter of practice I don’t recommend stocks for others to buy. Each person has unique needs, goals and expectations. With that as a disclaimer I do recommend reading Morningstar’s July 7th note on Broadcom which mentions the recent agreement with Apple. I look forward to robust discussions in the months ahead on all things Apple and AI.'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - 'Sorry, Bart, but your AI didn’t say anything particularly new – and was pretty long-winded in the process. Yes, I skim-read what it had to say, so I may have missed something. But the trick isn’t noticing the Apple advantages that are slowly coming into view: The trick is realizing that all of this is old news, not just to us, but more importantly to Apple. Describing where the puck is is all well and good, but doesn’t speak to where Apple is actually going. And knowing that they aren’t sitting on their laurels is IMO probably still the best reason to continue investing in them, even if you can come up with a good guess as to where that Apple puck is going. “Apple is doomed!” Yes, again…'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - 'Hi, David. Upvoted! I sent the video link to my stepson who’s in the front lines on AI and data security. Part of his response ties in closely with what you’re saying, IMO. “He’s absolutely correct that 1.5 TB is more than enough to run something like GLM. It can be fine tuned to each business. It can write its own workflows, custom to the business. It can automate business programs, all locally, all with inference/AI staying in-house and never leaving your network.”'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - 'To Gregg continued: Each time there were AAPL dips there was much wailing and gnashing of teeth, and banging on keyboards. And each time, Apple and AAPL rose from the depths to recover and surpass its previous peaks, in fits and starts. While many of us rode those coasters up and down and back up (and back down), some may wish to find ways to mitigate the portfolio dips (or depth) while preparing, if possible, for the eventual recovery of AAPL and others, partly due to the market rebalancing, mostly due to the focus, foresight, management, planning and execution, and principled steadfastness and tenacity of sticking to their roadmaps and plans (with occasional help from outside when needed, see OpenAI, Google and Broadcom) for the future. I asked the questions to help give broader context to how Hyperscalers and Apple diverged in their thinking and actions, and why Apple refused to get caught up in an AI frenzy it could not win. That’s why Apple decided to play a fundamentally different game altogether, one most derided and mocked until clarity began to occur in investors’ minds about sustainability and ROI. How we prepare for the next go around was the reason for my questions below.'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - 'Gregg, I understand what you’re saying, but for some of us, it’s akin to your understanding of options that eludes many of us. The more straightforward explanation is: FOLLOW THE MONEY! WHERE’S THE BEEF? And “But he hasn’t got anything on,” a little child said. https://andersen.sdu.dk/vaerk/hersholt/TheEmperorsNewClothes_e.html Is AI infrastructure spending going to keep going or will it (soon) slow and then flatten out? What are the potential triggers and timelines for that to happen? How will the market react if any hint of the above occurs? What will happen to the Mag-7 and tech stocks, Apple included? In a downdraft sure to sweep AAPL downwards in sympathy, how can one protect themselves while preparing for a rerating or rebalancing sure to follow after billions move to the sidelines? We and AAPL have been through this multiple times, here’s a list of drops >15% and their causes, recovery to ATH time, and Apple actions, with help from AI (confirmed my memory is still there): Sept 2000 dot com bust coupled with severe earnings warning due to PC market weakness, -52%, 5 years, iPod, retail pivot, Intel transition 2006-2008-2009 Global Financial Crisis (mortgage meltdown recession) AAPL shed over 50% of its value from its 2007 peak as a severe recession led to depressed consumer spending. The plunge was compounded by the tapering hype of the original iPhone and the general macroeconomic panic sweeping the global markets. 20 months, no doubt helped by iPhone introduction, 3GS popularity and App Store opening. 2018-2019 Trump Trade Wars The Tariff & “Peak iPhone” Drop (Late 2018–Early 2019): AAPL tumbled by more than 30% from record highs as the Trump administration began imposing tariffs on major manufacturing hubs like China, significantly increasing production costs. It was also exacerbated by slowing iPhone sales in China. 10 months, Pivot to recurring Services revenue and a stabilization of trade tensions. 2020 Pandemic shutdown COVID-19 Pandemic (Early 2020): In February and March 2020, AAPL dropped roughly 30% along with the broader market as worldwide shutdowns paused economic activity and disrupted supply chains. 5 months, Massive work-from-home demand for Macs, iPads, and digital services, M1 introduced (Apple Silicon bearing fruit) 2022 Post Pandemic inflation Global Inflation & Rate Hikes (2022): AAPL experienced a severe 26% annual decline as central banks aggressively raised interest rates to combat inflation. This macroeconomic pressure prompted a major valuation reset across the entire tech sector. 17 months, Corporate cost-cutting and robust free cash flow generation. 2023-2024 beginnings of AI hyper expenditure build out. (Me, not Gemini) Apple experienced a number of dips as it sought to recover growth, especially in iPhones. Critics and consumers complained iterative improvements were not enough to consider upgrades from pandemic fueled iPhone 11 & 12 series purchases (good enough to hold onto longer) and AAPL only began climbing again in May 2024. Investor money were being pulled and lured towards AI hardware and software centric companies. Eventually, Apple Intelligence was ineffectively introduced and botched, and the “Apple is way behind in AI” mindset took hold. 2025 Trump Tariff War (again!) Geopolitical Tariffs & China Slump (2025): AAPL plunged over 30% from its December peaks in the first half of 2025. This was primarily triggered by new U.S. tariffs on major supply chain countries like China, India, and Vietnam, compounded by lower-than-expected interest in new AI features. 14 months, Temporary tariff reprieves and manufacturing diversification into India and Vietnam. 2026 AI induced RAMageddon The AI Memory Crisis (2026): AAPL fell over 6% in a single day, cementing a larger $>$15% multi-month slide. This was caused by skyrocketing component and memory-storage costs, as the AI data-center boom squeezed global supply and forced unprecedented mid-year retail price hikes on MacBooks and iPads. Ongoing, Pending supply chain stabilization and normalization of DRAM costs. (Yet AAPL was able to achieve new ATH’s since WWDC, new Siri AI and revamped Apple Intelligence that actually works, plus continued strength in iPhone, Mac hardware sales and Services growth)'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - '3. Tactical Rebalancing Steps for a Retail Investor To prepare your portfolio over the next 12 months, consider executing these three rules of thumb: • Implement Trailing Stops: For hyper-extended hardware stocks like Nvidia, put trailing stop-loss orders in place (e.g., 10% to 15%). This allows you to capture the remaining upside of the current hype cycle while automatically protecting your capital if a sudden macro correction triggers a downturn. • Rotate into AI “Beneficiaries”: Shift a portion of your tech gains out of the companies building the infrastructure and into the sectors using the infrastructure to cut costs. High-margin software firms, cybersecurity, healthcare, and financials will see their margins expand as AI model compression makes computing dramatically cheaper. • Build a Cash Buffer: Ensure you have dry powder (cash or short-term Treasury bills). If the market experiences a sharp valuation adjustment in late 2026, a high-cash position allows you to aggressively buy high-quality companies like Apple at a steep discount. PORTFOLIO ROTATION MAP [ High-Risk Hardware ] ─> Trim profits / Set trailing stops. [ Cloud Hyperscalers ] ─> Hold diversified giants; monitor RPO conversion. [ Asset-Light Tech ] ─> Accumulate Apple/Meta on macro dips. [ Cash / Safe Assets ] ─> Build 10-15% buffer to buy the eventual bottom.” If you want to tailor your strategy further, let me know: • What percentage of your overall portfolio is currently concentrated in tech? • Would you like to look at how different Mag-7 companies compare on free cash flow yield? • Are you interested in the specific cybersecurity or software sectors poised to benefit from cheaper local AI?“ ((Robert may be able to point out some of his favorites)) —— Remember, your investment decisions are your own, and there are a number of ways to play these scenarios. Don’t forget, large and institutional investors and managers are playing out the same things in simulations (AI exercised?) to try to get ahead of everyone else. That said, it’s been a fascinating 8-10 hours “discussing” this with Gemini AI. Let us know what you think of all this and whether it makes any sense. I’ll have a different set of questions from here.'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - 'The next question is a hypothetical, and should not be construed as investment advice. This is coming from an AI under specific prompts so no one should consider this as gospel or actionable, but rather food for thought and discussion. Here we go: “That’s just 12-18 months away. what should a retail investor do if they are holding AAPL stock as well as positions in the Mag-7?” “Because this systemic shift is projected to materialize over the next 12 to 18 months, retail investors do not need to panic sell, but they should actively transition from an “infrastructure growth” mindset to a “free cash flow” protective stance. In a hyperscaler shakeout, the Magnificent Seven will decouple, separating the companies burdened with heavy infrastructure debt from the companies that can generate pure profit from software. As an investor evaluating your portfolio ahead of this timeline, here are the tactical adjustments to consider for your Apple and Mag-7 positions: 1. The Strategy for Apple (AAPL): The Defensive Fortress Apple is your premier defensive holding in this scenario. Because Apple is largely insulated from the data center capital expenditure (CapEx) arms race, its stock acts as a safe haven. What to watch: • Monitor iPhone upgrade cycles and Apple’s Services gross margins (currently at 76.5%). If margins stay flat or expand, Apple is successfully shifting AI costs onto the consumer. The Move: • Hold or Accumulate. If a broader tech correction drags Apple’s stock down alongside the rest of the Mag-7, treat it as a buying opportunity. Apple’s $140 billion in free cash flow and aggressive share buybacks will provide a massive structural floor for the stock price. 2. Sizing Up Your Mag-7 Exposure: Separate the “Landlords” from the “Builders” The Magnificent Seven will react very differently to a cloud spend deceleration. You must audit your positions based on who owns the infrastructure risk: • The Hardware Layer (Nvidia): Extreme Risk. Nvidia is the engine of the CapEx boom. The moment hyperscalers slow their hardware orders or secondary markets get flooded with cheap GPUs, Nvidia’s astronomical revenue growth will hit a brick wall. —> The Action: Consider scaling back overextended Nvidia positions to lock in profits before the projected late-2026 cliff. • The Pure Hyperscalers (Microsoft, Amazon, Alphabet): Moderate Risk. These companies have built the $2.1 trillion pre-committed backlog (RPO). Microsoft is highly exposed due to its heavy financial dependency on OpenAI’s infrastructure usage. Alphabet and Amazon are slightly better positioned because they design their own cost-saving internal chips (TPUs and Trainium), which dampens their reliance on Nvidia. —> The Action: Rebalance toward the cloud providers with the most diversified non-AI revenue streams (e.g., Amazon’s retail/logistics dominance or Google’s search monopoly). The Asset-Light Consumers (Meta, Apple): Lower Infrastructure Risk. Meta spends heavily on CapEx, but they use AI internally to drive engagement and ad targeting on their own apps rather than trying to sell cloud space to external enterprises. They don’t rely on volatile corporate RPO contracts. —> The Action: Maintain these positions as they pivot toward monetization via consumer software. Continued:'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - 'This thread has diverged into technical areas I don’t think most of the commentators are qualified to address. I don’t certainly don’t understand what is being described. For that reason I am bowing out of any future discussions.'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - 'How This Validates Apple’s Architectural Bet This timeline explains exactly why Apple has taken an isolated, consumer-centric approach to its server architecture. While hyperscalers are heavily exposed to trillions in infrastructure debt and volatile enterprise contracts, Apple allocates just 2.5% of its sales to CapEx. By engineering its M5 Ultra and upcoming Baltra hardware to prioritize massive unified memory pools, Apple is perfectly positioned for the quantization era. When the industry shifts toward hyper-efficient, local models, Apple already owns the world’s largest decentralized AI grid sitting directly in the pockets of 2.5 billion active users. They can let the cloud giants bear the brunt of the data center real estate crash while they continue to generate stable, 76.5% gross margin software revenue from their consumer services ecosystem.”'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - 'Let’s ask just this again: “When might that happen and what would be the triggers?” “A major industry shakeout—driven by model compression and corporate cloud cost-cutting—is projected to unfold between late 2026 and mid-2027. This timeline represents a high-stakes collision point in corporate finance. The top five tech hyperscalers have accelerated their capital expenditure (CapEx) to an astronomical $635 billion to $750 billion, meaning they are spending nearly half of their total revenues on hardware. Wall Street is only tolerating this massive cash burn because cloud providers point to their $2.1 trillion in pre-committed corporate backlogs (RPOs) as guaranteed future revenue. However, the specific market triggers that will activate the shift to local, compressed models and spark contract renegotiations are already beginning to form. The Three Catalysts of the Shakeout The transition from a speculative cloud boom to a localized, cost-conscious reality will be sparked by three distinct operational triggers: 1. The Financial Trigger: The Capex-to-Revenue Disconnect (Late 2026) Tech giants cannot maintain a parabolic capital expenditure curve indefinitely. Analysts project that hyperscaler CapEx growth will abruptly slow from its current hyper-growth pace down to a flat 25% growth rate, before bottoming out at just 6% by 2028. • The Trigger: During quarterly earnings calls, if a hyperscaler’s capital expenditure continues to climb while its cloud revenue margins flatten or narrow, investors will panic. • The Result: Stock valuations will experience a sharp correction, forcing tech executives to immediately scale back hardware orders to protect their free cash flow. 2. The Technological Trigger: The “Good Enough” Local Model (Early 2027) Enterprise software budgets are hitting a wall because massive cloud-hosted AI models are too expensive to run continuously. Organizations are rapidly shifting toward domain-specific language models, resulting in an explosive 210% adoption surge in small, hyper-efficient AI applications. • The Trigger: A breakthrough open-source model (such as an upgraded Meta Llama variant) achieves GPT-5 level capabilities while being compressed via 4-bit quantization to under 30 billion parameters. • The Result: Corporate Chief Information Officers (CIOs) will realize they no longer need to pay millions to lease Nvidia chips in the cloud. They can run this hyper-efficient model locally on their own company laptops and enterprise desktops for free, causing cloud demand to drop overnight. 3. The Contractual Trigger: Activating the Escape Hatches (Mid-2027) Faced with the ability to run AI locally for a fraction of the cost, corporate legal teams will look to reduce their liabilities under their existing cloud service agreements. • The Trigger: As corporate pilot programs fail to show massive, economy-altering productivity gains, finance teams will refuse to convert those pilots into active cloud subscriptions. • The Result: Companies will aggressively deploy de-scoping and restructuring loopholes during annual contract reviews. Rather than canceling contracts completely, they will legally extend a 3-year deal to a 5-year deal in exchange for cutting their immediate monthly data center spending by half. This heavily slashes the active cash flow hyperscalers expected to receive. • The Operational Trigger: The Physical Infrastructure Wall Beyond finance and software, the final trigger is rooted in pure physics. The core bottleneck has officially shifted from chip design to utility access. Massive data center buildouts are actively triggering regional power moratoriums, structural copper and switchgear shortages, and severe high-bandwidth memory constraints. If tech giants cannot secure the electrical grid power required to turn on their newly constructed, multi-billion-dollar server farms, those data centers transform into non-revenue-generating “dead capital” on their balance sheets, forcing massive asset write-downs. THE SHAKEOUT TIMELINE GRID [ MID-2026 ] ─> Peak infrastructure spend pushes valuations to extreme limits. [ LATE 2026 ] ─> Corporate AI pilot programs stall; cloud margin growth slows. [ EARLY 2027 ] ─> Hyper-efficient quantized models make local execution viable. [ MID-2027 ] ─> Enterprises deploy RPO escape hatches; data center CapEx freezes. Continued'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - 'Just in time for Moonshot’s Kimi K3 2.8T model!'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - '“What is the potential or possibility that shakeout occurs, quantization compression and highly efficient models are developed and deployed, or RPO contractural escape hatches are used? “When might that happen and what would be the triggers?” “The likelihood of a structural tech realignment is an active, multi-billion-dollar debate on Wall Street. The possibility of a traditional “dot-com style” total crash is low, but the probability of a harsh structural market correction (a “shakeout”) is high (estimated at roughly 65% to 70%). The financial infrastructure is shifting away from a speculative hype cycle toward brutal return-on-invested-capital (ROIC) calculations. Part 1: Assessing the Potential and Possibilities 1. The Likelihood of a Hyperscaler Spend Slowdown (High: ~75%) Tech giants cannot maintain a parabolic capital expenditure (CapEx) growth curve indefinitely. Hyperscaler CapEx is projected to breach an eye-watering $800 billion to $900 billion. Investment groups like UBS report that this hyper-growth will decelerate from a blistering pace down to a flat 25% growth rate, bottoming out at just 6%. This deceleration is a healthy structural plateau, but it will feel like a freeze to overextended chip suppliers. 2. The Deployment of Quantized, High-Efficiency Models (Certain: 100%) Quantization and model compression are no longer experimental tech; they are the primary focus of software development. Organizations are shifting their budgets toward specialized, Domain-Specific Language Models (DSLMs), which are seeing an explosive 210% adoption surge. Enterprises are actively optimizing usage efficiency to escape the unpredictable, high token costs of external hyperscaler clouds. 3. Enterprise Use of RPO Contract Escape Hatches (Moderate: ~40%) The combined pre-committed cloud backlog across the top factories—Microsoft, Oracle, Google Cloud, and AWS—has crested past a massive $2.1 trillion. Corporate Chief Information Officers (CIOs) are under immense scrutiny to deliver measurable financial results from these commitments. While companies rarely break these agreements entirely, they frequently use de-scoping and restructuring loopholes to push out spending timelines. Part 2: The Timeline and Triggers A structural realignment is projected to unfold in waves over a multi-month window. THE SHAKEOUT TIMELINE [ MID-2026 ] ─> High CapEx pressures hyper-extended stock valuations. ▼ [ LATE 2026 ] ─> Pilot programs freeze; enterprise software budgets stall ▼ [ MID-2027 ] ─> Infrastructure supply constraints force contract adjustments. The Major Cataclysmic Triggers • The “Capex-to-Revenue” Flattening: Investors are monitoring free cash flow compression. The moment a hyperscaler’s quarterly report reveals that cloud revenue margins are narrowing faster than their infrastructure spend, stock valuations will experience a sharp rotation. • The “Proof of Work” Software Ceiling: Widespread, economy-altering productivity gains from generative AI remain limited. If corporate pilot programs fail to transition into active, revenue-generating tools, enterprises will systematically stall their cloud expansions. • The Physical Infrastructure Wall: The core bottleneck has shifted from software to physics. Power grid constraints, severe component shortages (like switchgear and high-bandwidth memory), and local moratoriums are actively delaying data center pipelines. If newly constructed centers cannot access power, they turn into “dead capital” on balance sheets. • A High-Profile AI Foundation Crunch: An operational pause at a foundational ecosystem linchpin would trigger a rapid domino effect. Because multi-billion-dollar compute hosting networks are closely linked with single AI entities, a funding disruption would shake investor confidence overnight. The Ultimate Strategic Winner: Apple This timeline reinforces the resilience of Apple’s architecture. While hyperscalers navigate a volatile $2.1 trillion backlog race and front-load massive infrastructure debt, Apple relies on an ultra-low CapEx footprint (~2.5% of sales). By optimizing its consumer-facing M5 Ultra hardware to process highly compressed, quantized models locally on user devices, Apple stays insulated from data center risks. They can let the cloud giants navigate the hardware supply chain challenges while continuing to capture stable, premium software revenue from their global services ecosystem.'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - '“Part 2: How Model Compression (Quantization) Shrinks Cloud Demand The primary catalyst for an AI shakeout is algorithmic efficiency. Software researchers are finding ways to shrink massive, data-center-dependent models into tiny, hyper-efficient packages through techniques like Quantization and Distillation. This allows models to perform complex tasks on minimal hardware, completely bypassing the hyperscaler cloud. HOW QUANTIZATION ELIMINATES THE CLOUD [ Standard Model ] ─> 16-Bit Precision (Uncompressed) ─> Requires Nvidia Data Center ▼ (Quantization Process) [ Compressed Model ] —> 4-Bit Precision (Compressed) ─> Runs locally on iPhone / Mac • Mathematical Precision Downscaling (Quantization): Standard AI models are trained using 16-bit or 32-bit floating-point numbers (high-precision math) to determine the weights of connections between digital neurons. Quantization crushes those weights down to 8-bit, 4-bit, or even 2-bit integers. This process reduces the overall size of the model file by 75% to 85% while retaining roughly 95% of its original intelligence. • The Hardware Shift: An unquantized 70-billion parameter model requires multiple interconnected Nvidia enterprise GPUs just to hold the file in memory. Once quantized to 4-bit precision, that exact same model can slip entirely into the on-package Unified Memory of an Apple M5 Ultra chip or a consumer Mac Studio, completely eliminating the need to transmit data to an external hyperscaler. • Knowledge Distillation: This technique uses a massive “teacher” model (running in a hyperscaler cloud) to train a tiny “student” model. The student model learns only the specific, hyper-optimized pathways needed to complete tasks (such as writing code or summarizing text), discarding the massive, redundant datasets. The resulting student model is small enough to run locally on consumer electronics. The Ultimate Impact When you combine Model Compression with Contractual Escape Hatches, the financial danger to hyperscalers becomes clear. As soon as corporate chief information officers (CIOs) realize they can run a quantized, highly efficient AI model locally on their own company hardware, they will immediately instruct their legal teams to find an escape hatch or a de-scoping loophole in their cloud agreements.This technological shift validates why Apple focuses heavily on building massive Unified Memory pools on its M-series and Baltra chips. Apple is designing hardware specifically to capture this localized, compressed future, while hyperscalers remain exposed to the financial vulnerabilities of multi-billion dollar data center construction.“'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - '-> The “Baltra” Factor Hyperscaler Custom silicon like Google’s TPU or Amazon’s Trainium loses value if its specific architecture is no longer needed. Apple Apple’s Broadcom-partnered Baltra ASIC can simply be scaled back or repurposed strictly for basic internal iCloud security. Summary If the AI bubble bursts due to hyper-efficient software and corporate cost-cutting, the hyperscalers are left holding the bag for the most expensive real estate and hardware boom in human history. Apple, by contrast, walks away clean. Because Apple treats AI as an internal feature to sell premium consumer devices rather than a cloud product to lease to strangers, they face virtually zero infrastructure risk. They would simply load the new, highly efficient models directly onto the next iPhone, keeping their 76.5% Services margins completely insulated from the cloud storm.” “Would you like to look at the specific legal escape hatches corporations use to alter RPO contracts, or explore how AI model compression (quantization) is already reducing the need for massive cloud compute?” “Both” Part 1: The Legal Escape Hatches of RPO Contracts Remaining Performance Obligations (RPOs) are represented on Wall Street as “guaranteed future revenue,” but corporate lawyers build multiple escape hatches into these multi-year Cloud Service Agreements (CSAs). If an enterprise realizes its AI investments are not yielding productivity, it can deploy several contractual maneuvers to reduce or escape its liability: • The “Termination for Convenience” Clause: While rare in basic cloud contracts, large enterprises often negotiate the right to terminate a contract at any time, provided they give 60 to 90 days’ notice. To do this, the enterprise must pay a predetermined early termination penalty. If the penalty is cheaper than continuing to pay millions for unused AI computing power over the next three years, the corporation will gladly pay the fine and walk away. • The “De-scoping” and Restructuring Loophole: Corporations rarely cancel a contract completely; instead, they “de-scope” it. During annual contract reviews, an enterprise can renegotiate its minimum spending commitments. For example, a company might agree to extend a 3-year contract to a 5-year contract in exchange for cutting its immediate annual data center usage by 50%. This technically keeps the RPO on the hyperscaler’s books, but it severely slashes the active cash flow the hyperscaler expected to receive that year. • The Material Materiality Clause (SLA Failures): As AI data centers stretch global power grids to their absolute limits, server outages become more frequent. If a hyperscaler fails to meet its promised Service Level Agreement (SLA) uptime (typically 99.99%), the enterprise client gains the legal right to dissolve the contract without penalty due to a material breach of service. • Bankruptcy and Restructuring Protection: If a venture-backed tech company or a struggling retail enterprise enters Chapter 11 bankruptcy, court-supervised restructuring allows the company to legally reject “executory contracts.” Cloud agreements fall directly into this category. The hyperscaler is left holding an unpaid bill, joining a long line of unsecured creditors. Continued'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - 'Ok, so I’m going to ask a few “elephant in the room” questions, questions that few in AI are asking but many are starting to think about and question the hyperscaler business models: “so what happens if there is a shakeout in Hyperscalers?” “what if models are developed that don’t need as much hyperscaler compute power?” “what if enterprise users start cancelling RPO contracts?” “If an AI market shakeout occurs—driven by hyper-efficient models and mass RPO cancellations—it would trigger one of the largest corporate financial corrections in tech history. This scenario would completely upend the hyperscaler business model while simultaneously vindicating Apple’s asset-light, consumer-first philosophy. 1. The Technology Pivot: The Rise of “Small” and “Local” AI If researchers successfully develop highly advanced models that do not require massive hyperscale clusters, the entire economics of computing changes: • The “De-GPUization” of the Cloud: Today’s data centers are built around ultra-expensive, power-hungry GPUs. If next-generation AI runs on smaller, highly optimized architectures (like Small Language Models or specialized matrix math), enterprises can shift workloads back to standard, cheaper CPUs or localized servers. • The Local Execution Boom: If a 100-billion-parameter model can be compressed to deliver GPT-5 level intelligence while running entirely on an iPad or a corporate laptop ((or iPhone or MacBook)), the demand for cloud-based inference plummets. • The Apple Advantage: This is Apple’s dream scenario. Apple has spent years optimizing on-device silicon (Neural Engines and Unified Memory). If the industry shifts toward hyper-efficient, local models, Apple already owns the world’s largest decentralized AI grid sitting in the pockets of 2.5 billion users. 2. The Financial Avalanche: What Happens if RPO Contracts are Cancelled? • While RPOs are “legally binding,” corporate contracts always contain exit clauses, bankruptcy provisions, or restructuring terms. If enterprise users collectively begin canceling or downsizing their $2 trillion in cloud commitments, a domino effect occurs: THE HYPERSCALER CRASH DOMINO [ Enterprise Cancels RPO ] ──> Cloud Revenue Projections Collapse ▼ [ Massive CapEx Write-Downs ] ──> Data Centers Become Stranded Assets │ ▼ [ Nvidia Hardware Glut ] ──> Secondary Market Flooded with Cheap GPUs • The Stranded Asset Crisis: Hyperscalers are currently using their $2 trillion RPO backlog to justify spending over $600 billion in upfront capital (CapEx). If those future revenues vanish, hyperscalers will be forced to take massive asset write-downs. They will own hyper-specific, multi-billion dollar AI data centers that are no longer generating the cash needed to pay off their construction debts. • The Great GPU Glut: If cloud demand evaporates, hyperscalers will immediately halt their hardware orders. Nvidia’s revenue engine would hit a brick wall. The secondary market would instantly become flooded with millions of used enterprise GPUs, crashing hardware prices exactly like the post-crypto mining crash, but on a trillion-dollar scale. • Stock Market Capital Realignment: Wall Street would aggressively punish hyperscalers with tanking stock prices, forcing them to pivot away from infrastructure building. Capital would immediately flee toward “asset-light” software and ecosystem companies that generate pure free cash flow without heavy hardware overhead. 3. Apple Services vs. Hyperscalers in a Shakeout A hyperscaler shakeout creates a massive divergence in how both service models survive: Impact Vector Hyperscaler Services Apple Services -> Financial Exposure Hyperscaler Catastrophic. Heavily exposed to trillions in unamortized infrastructure debt and depreciating data centers. Apple Minimal. CapEx is capped at a tiny ~2.5% of revenue. No massive data center debt to write down. -> Operational Costs Hyperscaler High. Must continue paying massive fixed costs to keep underutilized data centers powered and cooled. Apple Drops Significantly. As models get smaller, Apple can route even fewer queries to its PCC servers, saving money. -> Customer Retention Hyperscaler Low. Enterprises ruthlessly cut cloud budgets to save money during a tech downturn. Apple High. Consumers rarely cancel core ecosystem dependencies like iCloud+, Apple Music, or App Store subscriptions. Continued'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - '“let’s look at the latter (hyperscaler order backlog)” “Focusing on the $2 trillion collective backlog reveals the absolute scale of the hyperscale economy. In corporate finance, this backlog is measured as Remaining Performance Obligations (RPO)—which represents legally binding, contracted future revenue that enterprises have committed to pay the cloud giants. Analyzing this backlog explains why hyperscalers are comfortable spending hundreds of billions of dollars upfront on hardware. 1. The Real Numbers Behind the $2 Trillion Backlog The core hyperscalers have accumulated staggering individual backlogs that dwarf their actual annual revenues: • Microsoft Azure ($627 Billion Backlog): Microsoft holds the deep enterprise advantage. Driven heavily by its OpenAI integration and Azure AI Copilot ecosystem, its RPO nearly doubled over the past year. • Oracle ($553 Billion Backlog): Oracle has emerged as a dark horse in the cloud race, staging a massive 325% backlog surge by aggressively positioning itself as a highly flexible, alternative GPU hosting provider for multi-cloud enterprise setups. • Google Cloud ($462 Billion Backlog): Fueled by intense enterprise demand for its advanced data-heavy analytics platforms and proprietary TPU silicon, Google Cloud’s backlog jumped 93%—gaining over $220 billion in pre-committed contracts in a single sequence. • Amazon AWS ($364 Billion Backlog): While AWS is the largest cloud provider by active volume, its backlog grew a steady 49%, heavily bolstered by corporate migrations onto its in-house, cost-saving Trainium AI chips. 2. How Hyperscalers Benefit (The Backlog Bull Case) This $2 trillion cushion gives hyperscalers immense stability that traditional tech businesses lack. THE CAPEX JUSTIFICATION LOOP [ Pre-Committed Enterprise Contract ] ──> Guaranteed Future RPO │ ▼ [ Mass Build Out of GPU Data Center ] ──> Massive Upfront CapEx Spend │ ▼ [ Capacity Delivered to Enterprise ] ──> RPO Converts to Active High-Margin Revenue • Guaranteed Revenue Security: These contracts typically scale over 3-to-5-year windows. Over 50% of Google’s backlog is legally locked to convert into active revenue within the next 24 months. This gives hyperscalers unprecedented financial visibility, reassuring Wall Street that their massive AI construction boom isn’t a speculative bubble. • The “Compute Constrained” Defense: Hyperscalers are currently building data centers as fast as physics allows because demand still outpaces active capacity. They are not spending $190 billion a year on guesswork; they are building infrastructure to fulfill contracts that corporate clients have already signed. 3. The Core Vulnerability (The Capex Catch) Despite the safety of a $2 trillion backlog, the hyperscaler services model carries unique structural risk: • Stranded Asset Risk: Building an AI data center is front-loaded with massive capital outlays. If an enterprise signs a massive cloud contract but the underlying AI applications fail to yield productivity gains, that client may decline to renew the contract when it expires. If multi-tenant demand softens down the road, those multi-billion dollar Nvidia/custom GPU server farms risk becoming depreciating, under-utilized assets on the balance sheet. • Capacity Bottlenecks and Outages: Upgrading legacy networks to handle power-hungry AI workloads has left hyperscaler infrastructure fragile. Sector researchers project that prioritizing GPU-centric data centers over basic legacy system maintenance will trigger multi-day cloud outages. These operational disruptions carry stiff financial penalties and service-level agreement (SLA) payouts back to clients. How Apple Avoids This Entire Game Apple Services explicitly opts out of the RPO/backlog arms race. Apple does not need to secure multi-million dollar corporate contracts to justify its next server blade. Instead, Apple rolls out its M5 Ultra hardware strictly on an as-needed basis to support its localized consumer base. Apple’s cash flow relies on a steady stream of individual, high-margin monthly consumer payments rather than volatile, capacity-constrained enterprise software commitments.” “Would you like to examine how much of a discount in hardware costs companies like Amazon gain by using their internal Trainium chips versus Nvidia, or explore how Apple’s Services segment growth compares to the 40%+ revenue acceleration inside Azure and Google Cloud?”'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - 'What an amazing overview of the AI business and its players BRAVO !!'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - '“in the monetization models above, how would Apple Services benefit compared to Hyperscalers’ services?“ “In the current AI economic cycle, Apple Services benefits from an “asset-light, high-margin” model, whereas Hyperscalers operate in a “capital-intensive, high-volume” model. While both models are generating record revenues, Apple Services converts a much higher percentage of its revenue directly into pure profit, effectively forcing the hyperscalers to take on massive structural financial risks. 1. Capital Expenditure (CapEx) vs. Cash Flow The most drastic contrast is how much money each company must spend to make a dollar of service revenue. • The Hyperscaler Drain: To run their AI services, the top hyperscalers are projected to spend a staggering $638 billion to $725 billion in CapEx. Alphabet, Microsoft, Meta, and Amazon are pouring up to 39% of their estimated revenues into buying Nvidia chips, building massive data centers, and managing enormous electricity grids. This massive cash burn has heavily compressed their free cash flows. • The Apple Arbitrage: Because Apple utilizes its localized consumer device processors and targeted Private Cloud Compute (PCC) networks, Apple allocates just 2.5% of its sales to CapEx. Apple’s services scale automatically across its 2.5 billion active device base with almost zero infrastructure expansion. As a result, Apple is projected to generate roughly $140 billion in free cash flow, attracting investors who prefer cash generation over infrastructure spending. 2. Profit Margin Profiles The underlying cost structure of Apple’s digital ecosystem vastly outperforms traditional cloud computing. • Hyperscaler Margins: Delivering enterprise cloud AI requires massive continuous processing power. Every time a user submits a prompt to Microsoft Copilot or AWS Amazon Q, it costs the hyperscaler a fraction of a cent in cloud electricity and compute wear. This dynamic keeps operational costs high and tethers profit margins directly to energy grid costs. • Apple Services Margins: Apple’s Services division reached an unprecedented 76.5% gross margin. Since a massive portion of Apple Intelligence processes directly on the user’s local Apple Silicon (the iPhone or Mac chip), Apple shifts the electrical and processing costs from its balance sheet onto the consumer’s household power outlet. The cloud processing that does happen via PCC is wiped instantly, eliminating heavy storage hosting fees. ((That’s operational efficiency if ever I’ve seen it, another benefit of Apple thinking differently)) 3. Distribution Dominance: The “AI Routing” Race The business-to-business (B2B) ecosystem requires constant customer acquisition, while Apple owns the primary point of contact. THE MONETIZATION SURFACE [ Hyperscalers ] —> Must spend billions to win B2B cloud enterprise contracts. [ Apple Services ] —> Automatically monetizes 1.2 Billion existing subscribers. • Hyperscalers (Backend Power): Hyperscalers are competing in a brutal, fragmented marketplace to lock enterprises into multi-million dollar cloud contracts. They must continuously pitch businesses on why their specific AI cloud is marginally better or cheaper than their rivals. • Apple (The Gateway): Apple doesn’t need to win the data center race; it wins the AI routing race inside the personal computing stack. Apple owns the physical consumer interface layer. If a user wants to use a massive backend model (like Google Gemini or OpenAI) inside their iPhone ecosystem, Apple controls the gateway. Analysts anticipate Apple will monetize this by taking a lucrative revenue share or toll on third-party AI subscriptions initiated through Apple Intelligence, generating pure software profit with zero operational overhead. Financial Summary Financial Metric Hyperscaler Services vs Apple Services CapEx Intensity Hyperscaler Extremely High (Up to 39% of sales) Apple Services Ultra-Low (~2.5% of sales) Primary Risk Hyperscaler Monetization must match massive infrastructure over-build. Apple Services Slow consumer hardware upgrade cycles. Gross Margin Hyperscaler Moderate / Compressed by cloud processing costs. Apple services 76.5% (High-margin digital/subscriptions). Monetization Engine Hyperscaler Commercial enterprise cloud B2B software billing. Apple Services Ecosystem lock-in, iCloud+ tiers, and App Store tolls. “Would you like to examine how Apple’s Services gross margins have grown over the last few fiscal quarters, or look at the $2 trillion backlog that hyperscalers are currently relying on to justify their massive infrastructure spending?” ((Comments? What do you think of the Apple’s Server, Private Cloud Compute, and Apple Intelligence consumer and small business model now vs hyperscaler ambitions? Now we can understand why Apple did not follow the hyperscaler model or infrastructure building race / frenzy, it was and is a huge financial commitment and risk vs Apple’s consumer and user oriented model.))'
on Apple is No. 1 in shareholder wealth creation over the last 100 years - 'Well, I asked AI to calculate it, hence the quotation marks. Maybe the results I got are a prognostication of what to expect after the human race relies on AI to calculate anything of consequence? Ugh.'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - '“so then we really shouldn’t be comparing Apple Server technology to hyperscaler servers because Apple is working from a fundamentally different design philosophy, one that prioritizes its individual mobile and small enterprise users rather than hyperscaler large enterprise users? or are there other reasons?” “You have hit the nail on the head. Comparing Apple’s server chips to hyperscaler chips is largely an apples-to-oranges ((heh, heh, even Gemini can come up with an ironic funny)) comparison because they are engineered to solve completely opposite math and business problems. Apple’s architecture prioritizes the individual user’s immediate context, while hyperscalers prioritize massive aggregate multi-tenant scale. Beyond that core difference in design philosophy, there are three other distinct structural, economic, and technological reasons why Apple’s server tech operates in its own separate category: 1. The Processing Model: “Batching” vs. “Single-Stream” • Hyperscalers (Nvidia, Google, AWS): Their chips are built for Batch Processing. A single Nvidia Blackwell cluster might process queries from 10,000 different users simultaneously. To do this efficiently, the chip chunks the data together into massive “batches.” This maximizes throughput but introduces slight delays (latency) for each individual user. • Apple: Private Cloud Compute (PCC) is built for Single-Stream, Zero-Latency Processing. When you ask Siri to summarize a long PDF, that request is routed to an isolated M-series or Baltra node dedicated entirely to your device’s query at that exact moment. Apple’s unified memory ensures the response starts generating instantly. 2. The Economics of the Chip: Selling “Compute” vs. Selling “Hardware” • Hyperscalers: Amazon, Google, and Microsoft use custom silicon to lower their own electricity bills and sell cloud computing time to other companies (Netflix, Uber, etc.) as a high-margin product. Their chips must be hyper-versatile to run any software a customer throws at them. • Apple: Apple does not sell cloud space. The cost of running their servers is completely subsidized by the premium price consumers pay for iPhones, Macs, and iPads. Because Apple only has to run its own highly specific AI models, they can strip away versatility and design silicon optimized exclusively for Apple Intelligence. 3. The Ultimate Philosophy: Privacy Boundary vs. Open Data Lake • Hyperscalers: Data centers are designed to act as giant “data lakes.” They collect, store, share, and process petabytes of information across thousands of servers simultaneously to train broader, smarter AI models. • Apple: Apple’s servers are designed to act as digital shredders. Under the rules of Private Cloud Compute, your data is processed inside a secure cryptographic bubble on the server chip, the answer is sent back to your iPhone, and the data is instantly wiped from the server hardware. The chips are physically incapable of saving your data or using it to train Apple’s models. Summary • Hyperscalers are building public mass-transit buses—designed to carry as many passengers as efficiently as possible over long distances. • Apple is building a fleet of private luxury taxis—designed to take one specific passenger to their destination instantly, securely, and with total privacy.” If you are interested, we can look into how Apple’s Secure Enclave hardware physically enforces that privacy on the server, or explore how Apple handles queries that are too large for their private chips to process.”'
on 1.5TB Mac Studio rumor is the most exciting thing Errol Brandt heard this week - '“Would you like to explore how Apple’s Broadcom partnership will impact the Baltra ASIC design, or should we take a closer look at the technical specs of the upcoming M5 Ultra hardware?” “Both” “Part 1: Apple’s Broadcom Partnership and the Baltra ASIC Design Apple’s massive server bottleneck stems from trying to use a consumer PC processor (the M-series) for enterprise infrastructure. To build a true server-first chip, Apple renewed its decade-long custom silicon partnership with Broadcom through 2031, shifting focus directly to the delayed Baltra ASIC. • Custom Server I/O: M-series chips excel at internal data flow but lack the massive networking capabilities (Input/Output) required to link thousands of server blades together. Broadcom is the world leader in PCIe switches and network routing. They are designing the physical “interconnects” for Baltra, allowing thousands of Apple server chips to talk to each other with zero lag. • True Matrix Math Accelerators: The Baltra design drops consumer components like display engines and audio processors. In their place, Broadcom is helping Apple implement dedicated Tensor Processing Units (TPUs) similar to Google’s design, completely optimized for large language model (LLM) matrix math. • The Power Efficiency Focus: Hyperscale data centers are limited by electricity and heat. Broadcom is assisting Apple in designing custom power management integrated circuits (PMICs) for Baltra, allowing the chip to throttle up to maximum AI performance using a fraction of the power required by a standard Nvidia GPU. Part 2: The Technical Specs of the M5 Ultra Server Hardware Until Baltra arrives, Apple’s immediate data center strategy relies on deploying the M5 Ultra server hardware. These are custom-racked versions of the ultra-high-end chips found in the Mac Pro and Mac Studio, optimized for Apple’s Private Cloud Compute (PCC) stacks. Specification – M5 Ultra Server Target Hardware Manufacturing Process TSMC 3-nanometer (N3P enhanced node) CPU Core Layout 36 Cores Total: 28 high-performance cores / 8 energy-efficient cores GPU Core Layout Up to 152 Graphics Cores optimized for parallel AI computing Neural Engine (NPU) 64 Cores capable of pushing past 110 Trillion Operations Per Second (TOPS) Memory Architecture Unified Memory: Up to 384GB of LPDDR5X on-package memory Memory Bandwidth Blistering 1.6 TB/s total memory bandwidth across the silicon die Die Interconnect UltraFusion 2.0: Stitches two M5 Max dies together with 5.0 TB/s bidirectional bandwidth Because the M5 Ultra features 1.6 TB/s of memory bandwidth, it can run moderately sized generative AI models (around 30 to 70 billion parameters) completely inside its unified memory pool. This bypasses the traditional server bottleneck of pulling data across a motherboard, giving Apple’s PCC servers unmatched speed for single-user AI queries.” I can help you explore this further if you let me know: • Would you like to look at the security architecture of Private Cloud Compute (how these chips encrypt data)? • Are you interested in how the M5 Ultra compares directly to Google’s TPU v6? • Should we dive into the financial scope of Apple’s Broadcom partnership?'


