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Huawei just updated the Ascend 960 & 970 roadmap. $NVDA's competition pressure is increasing from both $AVGO and Huawei continuously as we've anticipated. → 960DT rollout pulled forward from 4Q27 to 1Q27. (Seems like they are finally getting the HBC-like, tailored-memory approach closer to high-volume production.) → 960PR by 3Q27 with 8 PFLOPS FP4—double the 960DT’s FP4 compute. (Closing in on NVDA’s Blackwell Ultra, which tops out at 15 PFLOPS dense FP4. Rubin moves that target to 35 PFLOPS dense FP4.) → 970 by 2028 with 14 PFLOPS FP4, up from the previous 8 PFLOPS target. Overall, this looks consistent with our Tau Scaling thesis: a higher compute ceiling, more tailored chip designs, and a faster path to production. Not every specification has doubled, but a near-doubling of the 970’s planned FP4 compute alongside an accelerated 960 rollout is a meaningful change. The bigger story, in our view, is Huawei’s CIDM model. Optimizing the entire vertical—not just the logic die—creates more room to change the compute design and shorten the development cycle. Huawei’s disclosed co-optimization already spans devices, circuits, chips and systems. This is the kind of vertical integration Elon Musk is targeting with Tesla and SpaceX’s Terafab. But Huawei is already putting its ecosystem version to work. (Would be quite funny if the Chinese ecosystem gets accelerator-based EUV into production lithography first, too.) Against NVDA, our thesis is that the single-chip gap narrows from roughly three years toward two—and potentially less as the roadmap accelerates. From a PPAC perspective, Tau Scaling could bring the logic die within a year—or less—while memory remains further behind. Bandwidth, not simply capacity, looks like the harder bottleneck. But Huawei may be able to lean into memory capacity and co-design the LLM and serving strategy around that constraint, rather than simply copy something optimized for NVDA’s hardware balance. System-level performance still needs to be proven at the cluster sizes Huawei is targeting. But we would not be surprised if it works better than expected. Even before the full UnifiedBus vision is realized, we think Huawei could gain system-level performance leadership on some large-model workloads sooner than many expect.
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Convequity’s AI Bubble Barometer (ABBX) — Week 17 | 11 September 2026 Our weekly framework tracks valuations, returns, capex, financing risk and forward infrastructure economics across hyperscalers, neo-clouds and model labs. This week’s print is also an Oracle week. $ORCL reported FQ1 FY27 on 10 September: revenue and cloud growth accelerated, disclosed backlog (RPO) jumped to $664B, cash capex ran hot, FCF stayed negative but better than feared, and a $20B ATM equity sale was completed. Capacity delivery was the operational headline — 850 MW and >300k GPUs in the quarter. The ABBX read is below. As of 10 Sep session close (US ET), 13 selected companies (hyperscalers + neo-clouds): -EV / IC = 5.80x (↓ 0.06x from 5.86x on 4 Sep Friday close; ↓ 1.87x from 7.67x on 15 May series start) - All-in EV / IC (incl. private OpenAI & Anthropic) = 6.52x - ROIC / WACC = 3.07x in Q2'26 vs. 3.73x in Q1’26 - ROIC – WACC spread = +23.1% EV/IC tells you what the market is paying for every dollar of invested capital. ROIC/WACC tells you whether those dollars are still earning more than they cost. A classic bubble is when EV/IC keeps expanding while ROIC/WACC is rolling over. That is still not the setup. Returns remain more than 3× the cost of capital while EV/IC for the public builder set sits near 5.8x — well below the midsummer 7× prints and the 15 May series start. The multiple compression looks more financing-driven than fundamental: the return spread is still wide. Oracle is the live test of that financing channel — and on ABBX it sits halfway between the diversified hyperscalers and the neo-clouds. Start with backlog. ORCL’s $664B RPO is now in the same league as $MSFT (~$678B) and ahead of $GOOGL (~$520B) and $AMZN (~$496B). Neo-clouds are a different scale entirely: $CRWV ~$104B, $NBIS ~$40B, $IREN ~$17B. On disclosed backlog, Oracle is already peer to Microsoft — hyperscaler-scale contracted AI demand. The constraint is not finding counterparties; it is delivering power, GPUs and campuses against a backlog that already exceeds its AI enterprise value (EV/RPO ~0.7x on the company row). Valuation paints the halfway case. On Forward Valuation, Oracle’s AI EV is roughly mid-hundreds of billions against ~$163B/year of plan-implied revenue (~12.8 GW internal × ABBX blend (colo vs. GPU rental vs. mode/app derived revenue) — about 2.8x EV / plan-implied revenue and ~4.3x EV / implied gross profit. That sits well below Microsoft, Amazon and Google (roughly high-single-digit to low-double-digit EV/plan on the same framework) and Meta (~4.5x), but well above CoreWeave and IREN (sub-1x to low-1x on plan revenue). EV/IC tells a similar story: Oracle is cheaper than the big four diversified names and closer to the neo-cloud pack on capital multiples, while its backlog and quarterly capex now look hyperscaler-sized. The market is pricing Oracle more like a leveraged AI builder than like Azure/AWS/GCP — even though its contracted demand book looks like theirs. Capacity mix explains part of the gap. In AI Infrastructure Capacity Plans, Microsoft, Amazon, Meta and Google all show some external planned GW — capacity rented from specialists such as CoreWeave, Nebius and IREN. Oracle shows none. Its disclosed plan is treated as internal OCI build. Oracle is the landlord in this graph, not a tenant: it sells AI cloud to frontier labs and enterprises, and does not need a large rented-GPU line to hit the plan. That is control and a cleaner path to own-stack margin. It is also concentration. Build cost, leases and delivery sit on Oracle, not on a diversified supplier set. The Implied Revenue & Valuation Multiples table shows the same shape in the numbers. Oracle’s implied ABBX USD per planned GW is about USD 13 billion, below Microsoft at about USD 17 billion and well below Meta and Google in the mid-20s. That is not a claim that OCI is worse infrastructure. It is a revenue-density assumption. Oracle’s plan is weighted to large-contract AI infrastructure sold to hyperscale counterparties, closer to wholesale cloud economics. Meta and Google’s higher implied USD per GW embeds denser take on their own AI and platform stacks. CoreWeave can look high on a thin equity base. Oracle is the opposite: a huge RPO and more conservative revenue per gigawatt of disclosed plan. Financing is where Oracle looks more neo-cloud than Microsoft. Our Expanded Debt & Capex, split by disclosure vintage: From FQ1 FY27 (31 Aug, condensed balance sheet in the earnings release): borrowings $125.3B, cash $36.4B, IC $156.2B, plus operating-lease ROU/liability lines on the face of the BS. Still on the May / Q4 FY2026 full note lock (not in the FQ1 PR): on-books stack ~$167B (~1.3× borrowings), uncommenced DC leases ~$260B, purchase obligations ~$13B, all-in burden ~$441B (~3.4× borrowings). That roughly 3.4× all-in multiple sits near Amazon (about 4×) and above CoreWeave (about 2.5×) and IREN (about 2.9×), but far below Microsoft (about 17×), Google (about 10×) and Meta (about 9×). Those names carry large off-balance-sheet lease books relative to thinner headline debt. Oracle’s uncommenced lease line is still huge; the multiple looks moderate only because borrowings themselves are already large. The setup is notes debt, a completed ATM equity raise, heavy cash capex, negative FCF, and a large May commitment book that the next 10-Q still needs to update. Management says new contracts need no further equity raise. ABBX treats that as a claim to monitor, not a free pass. Oracle’s Q1'27 cash capex was about USD 28.5 billion — in the same league as Meta, below Microsoft, still below Amazon and Google. Against that sits about USD 664 billion of contracted future work, more than twenty times one quarter’s build spend, with management guiding that roughly half should become revenue inside three years. That is the highest backlog-to-build ratio in the group, and it is why demand on paper looks full. It does not mean this year’s cash bill is already paid. RPO is multi-year revenue, not cash this quarter. Capex is a repeating outflow — FY27 guidance is about USD 90–95 billion, or about USD 70 billion net after customer prepays — and Oracle has also signed about USD 260 billion of data-center leases (as of May 2026) that are not yet on the balance sheet and start as campuses come online. Rent is due once those leases begin, even if customer cash is later. That is why the stock can de-rate on funding even with a full book. The high ratio argues for utilisation if delivery lands. Free cash flow and the lease stack are the separate question of who funds the build before the backlog turns into cash. Other hyperscalers can look weaker on the same ratio because more of their spend serves their own ads and cloud stacks, which never appear as Oracle-style external backlog. Delivery is catching up with that book. Oracle has put about 850 MW and more than 300,000 GPUs into service, with GPU utilisation at 97.9% and renewals at a premium — capacity is being used when it lands. At the Abilene, Texas campus, six of eight buildings are live, and customer acceptance of new clusters has shortened to about 24 hours: delivered capacity is signed and usable in a day, not weeks. The earlier caution still holds: lab tests of how many tokens a cluster actually produces, and real traffic samples through gateways that serve open-weight models, are still thin. Campus delivery looking full is not the same as those checks. Direction still matters. Across the complex, Friday’s snapshot still looks like a build-out, not a hangover. For the 13 names, aggregate AI enterprise value is about USD 10.7 trillion. AI EV over disclosed backlog is in the mid-single digits. AI EV over plan-implied revenue is in the high-single digits on the broader cuts. Planned capacity is still well above 100 GW. Those multiples look attractive if the book converts: a few years of contracted work, and under 10 times the revenue the build plan implies — not a 20-times software multiple on a hope. Financing risk is the other side of the same picture. One debate is a high EV versus invested capital. Another is a high EV versus off-balance-sheet commitments. Oracle now forces both into one name: hyperscaler-scale backlog, a neo-cloud-like equity valuation, and a commitment stack you can inspect in ABBX. Verdict: no broad AI bubble signal. Week 17 of our ongoing weekly series. Updates every Friday. Full interactive dashboard (Fundamentals, Forward Valuation, Expanded Debt & Capex, Capacity) available at Convequity. #AIBubbleBarometer #AIBubble
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Kirin 9050 Pro is the first Tau Scaling chip. The bigger story is Ascend. Huawei just put LogicFolding in the Mate XT 2. First commercial SoC on the Tau (τ) law He Tingbo laid out in May / updated in July. Not a surprise if you read that paper. Still a real jump: DUV + SAQP + logic stacking took density from 155 → 238 MTr/mm² on the same SMIC N+3 node. +55% in one gen. Power went down, not up. That’s N3-class equivalent density. Headline number matches Intel 18A HD (238) and sits next to Samsung SF2 (~231). TSMC N2 HD is still ahead (~313). Caveat: 238 is Huawei’s LogicFolding equivalent, not a planar N3 shrink. Same device node as 9030 Pro. Some analysts put the industry-formula read closer to ~175. Mobile scoreboard vs last-gen trifold / 9030 Pro: • Device +42% • CPU ST +24% / MT +52% • P-core 3.1 GHz (was 2.75) • CPU / GPU / NPU iso-perf power −41% / −58% / −66% • GPU render +142% • On-device 30B-total-param MoE Multi-core still trails $QCOM’s latest. The gap is no longer multi-generation. Next Tau Kirin can catch, maybe pass, a leading-edge mobile SoC. The packaging tell is sharper than the phone benches. 1.4–1.5μm hybrid-bond pitch. ~50M vertical interconnects. Western HVM hybrid bonding is still ~6μm (TSMC SoIC) to ~9μm (Intel Foveros Direct). If 1.4μm holds, that is SOTA vs Western leading-edge bonding — not the Geekbench print. Phone catch-up is the demo. AI silicon is the payoff. Apply the same fold to Ascend and the process-density gap vs $NVDA peers compresses from the lithography side over the next few quarters. Remaining bottlenecks: domestic DUV tool volume, HBM, packaging, software. I wouldn’t assume they outproduce ASML on DUV. I also wouldn’t fade the direction. Mature DUV + stacking + almost-unlimited power is enough to put China in the token-factory fight vs the US. Tables below. 🧵
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GPT-6 Astra: scaling still works. Algo is the larger remaining gap. Astra shows both scaling laws and algorithms are still progressing. It is reportedly distilled from a 10T+ teacher named Bel. Maybe with $NVDA Vera Rubin and the next compute generation coming online, 10T+ becomes the new standard and performance keeps compounding. That means continued — even accelerating — demand for AI infra. The more interesting signal is algo. IMO we are still just touching the surface of how algorithms can improve. There is still a large gap between SOTA perf/watt and the human brain. GPT-6’s recurrent depth is the part that stuck with me. CoT / test-time reasoning lets the model write intermediate steps out as tokens, then produce a better final answer than a direct reply. Recurrent depth is different. It lets the model think longer and harder in hidden space before any word is emitted. Not just write the thinking out. Think harder on each next bit of the word. I am perplexed this arrived so late in GPT-6. Stable Diffusion introduced and spread this kind of latent iterative method in image generation ~5 years ago. The main reason it came so late: hidden thinking is harder to safety-check and monitor than explicit CoT. That constraint is real. It is also more human-like. People do not narrate every step. Plenty of global-max algorithms are still unimplemented for similar reasons. Models only get stronger from here.
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Cybersecurity isn’t uniformly exposed to model labs. Host agents (telemetry), inline enforcement, and shift-right ops — humans, live environment, adversaries — stay relatively hard to absorb for the intermediate future. Shift-left and the checkable end (vuln scanning, validators) are already getting hit hardest by stronger codegen and coding agents. Relatively safer (host agents / inline / shift-right): $PANW, $CRWD, $FTNT, $NTSK More exposed (shift-left / scan / checkable): $TENB, $QLYS, $RPD.
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Open weight model to Nvidia ls kerosene lamp to Standard Oil. Free access to models = more demand for underlying infra. What's better is $NVDA can make this Lamp tailored and optimized for Nvidia stack such that users have to pick: Free model + $NVDA stack or Expensive model + Non-NVDA stack. The question is more about if $NVDA can deliver frontier open weight models or not. I am still a bit skeptical about it because it takes an elite dedicated team to do that and $META learned that hard lesson. And it also requires visionary mgt directly leading the elite team which $GOOG and $MSFT failed, while $SPCX realized it and back up again.
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Pondering how safe $PANW and other cyber incumbents really are if model labs keep expanding. Split security into two buckets. Bucket one: scanning and out-of-band work. Code and package vulns, cloud config checks via APIs, posture reports, “is this alert real?” These jobs are relatively checkable. Coding agents and Mythos-class models can do more of them over time — often with no endpoint agent or firewall in the path. That is where AppSec and vuln tools look exposed. Bucket two: live sensors and inline enforcement. See the process. See the packet. Allow or deny. Contain the machine. AI cannot do that from the outside by calling an API. Something still has to sit in the estate. That is much harder for a lab to recreate. So the interesting question is not “is cyber safe?” It is: how much of the franchise is bucket one, and how much is bucket two? PANW lives mostly in bucket two — plus deep SOC context. That looks safer than a pure scanner. Not untouchable. Just a different risk.
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Exploring SaaS survival in the agentic era — $PANW vs $DDOG. Both sit on critical telemetry. Both expose MCP. On paper they look alike. They aren’t. Model labs have a real reason to build their own observability collectors. Coding agents write and ship code; to get better they need production feedback. Own the host telemetry, close the loop. That same incentive is much weaker for cybersecurity endpoint agents. Finding beacons and writing better code are different jobs. Building a Falcon- or Cortex-class sensor estate is years of trust, labeled attacks, and compliance — not a natural side quest for a model lab. Then there’s the screen. Datadog doesn’t own where developers live. They already live in Cursor / Claude Code / Copilot. So those agents pull Datadog in over MCP. The data plane stays sticky; the UI gets shared. Palo Alto usually does own where the SOC lives. The shift starts in the security console — cases, playbooks, response. In a PANW shop, Cortex is the main workplace. Claude can sit on top in a multi-vendor stack. That’s the risk but not the base case. Same idea — agents need the pipes. What’s different is who tries to own the collector, and who already owns the desk. Thoughts welcome. Full Part 3 write-up for Convequity subscribers is on its way.
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Snyk is a clean postmortem for what happens when a security tool lives inside the coding agent’s loop. The product was mostly scan-and-warn. Find the issue, comment on the PR, suggest a fix. Blocking the merge usually sat in GitHub, not in Snyk. Bigger platforms smothered it. $PANW, $CRWD, and Wiz pulled AppSec into the bundle the CISO was already buying. GitHub was the main developer surface and put scanning where the code already lived. Then coding agents arrived and delivered the final blow. A lot of that scanning became something the agent could just do. Growth held up for a short while after the COVID/cloud tailwind. Then it decelerated hard. This is the same lens we use in Convequity’s SaaS Agentic Survival Evaluation Framework. The PANW, CRWD, and FTNT reviews go up on Convequity in a few days.
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The AI build is moves more aggressively off the balance sheet Convequity’s debt scan across $MSFT $AMZN $GOOGL and $META: what these companies borrow in public is rising slowly. What they have promised to spend later — leases not yet started, equipment orders, contracted compute — is rising much faster. In 2Q26 that gap is the widest in four quarters. Off-book commitments are running toward +316% year-on-year. On-book debt is barely moving. If you only watch headline borrowings, you miss the cycle. MSFT is the clearest case. On-book debt +15% YoY. Off-book commitments +138%. That is why its all-in financing load is about 17x reported borrowings. Microsoft is under pressure from two sides: shareholders who want more AI spend, and shareholders who want less capex and faster returns. The compromise is to slow the capex it puts on its own books and lock in capacity from $IREN, $NBIS and other neo-clouds instead. How that works: IREN or NBIS borrows, builds the site and owns the chips. Microsoft signs a multi-year contract to pay for the capacity. Microsoft does not put the building on its balance sheet. It takes a future bill — rent and service payments that show up later as operating cost. Asset risk moves to the neo-cloud. Payment risk stays with Microsoft. GOOGL looks similar on a multiple and is doing something different. All-in commitments are 10.2x borrowings: $100bn of headline debt, $121bn including leases, $902bn off-book. $811bn of that off-book pile is purchase orders for its own stack — TPUs and the kit around them — not rented neo-cloud sites. On-book +190% YoY. Off-book +836%. That multiple is Google buying the factory, not renting it. META is closer to Google than to Microsoft: on-book +127%, off-book +680%. Some of Meta’s orders are reserved compute from CoreWeave and Nebius, which Meta will expense as it uses the capacity. Most of the pile is still Meta committing to build and buy for itself. AMZN is the most balanced of the four (+59% on-book / +80% off-book). Both lines are rising together. If the cycle breaks, Microsoft is less stuck with buildings and chips it owns. It is more stuck with bills it already signed. Google is more exposed because more of the capital is already spoken for on its own account. If compute gets scarcer, the extra megawatts IREN and NBIS have not yet sold can go to whoever pays more. Microsoft then has to wait or pay up. Because it owns less of the physical stack, it has less spare capacity it can simply switch on — which means higher compute costs and tighter margins. Powerful cycle. Not a broad bubble. The heat is in the promises, not the 10-Q debt line. Full AI Bubble Barometer available at Convequity.
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Cerebras is a taxi. NVIDIA runs the buses. Almost every confusion about $CBRS begins with reviewing the wrong vehicle. We just published Part 1 of our Cerebras deep dive: → Why wafer-scale SRAM excels at low-batch decode → Why $NVDA becomes the cheaper token factory at high concurrency → Why Cerebras was built for training—but found its future in inference → Why the two architectures are increasingly complementary, not substitutes Our conclusion: Cerebras is mostly a buy over the next 2–3 years—but demand for premium-speed inference will decide the case. The full high-level report is live. 👇 https://t.co/awUwxPUSHI
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The $3T “hidden AI debt” number is not the story WSJ just put a headline on the shadow capex we’ve been tracking for a while. Purchase commitments + leases that have not started. Most of it never hits the balance sheet. The $3T is real. The interpretation is not. Add the explicit debt-like items on the books of $GOOGL $MSFT $META $AMZN and you get ~1.69x. Widen the definition to implicit / off-book instruments and it can balloon to 8.36x — approaching that $3T figure. That is the chart. It is not the economics. 1. Most of this is contract value, not a hard legal obligation to pay. A lot of it is pay-as-you-go. The commitments they actually have to fund already sit on the balance sheet. 2. The implicit load is shared. It is spread across financial players — private credit and equity — who are underwriting the build. Their IRRs are not insane. Current ROIC on this spend is still high. This is not a bubble from here. 3. AI ROIC can compress later. I would not be surprised. Near-term it is more likely to stay elevated or even rise. Compute is still short. Efficiency gains are raising, not lowering, the value of each incremental GPU-hour. 4. What the hyperscalers are actually doing: paying a bit more, accepting a slightly lower margin, and sharing a slice of the economics with investors who want the risk. That is risk offload, not hidden leverage. The cleaner setup is still the player that can keep building more capacity internally without leaning as hard on this structure. SpaceX is the extreme version of that. It also means more profit pass-through to the specialized GPU clouds sitting in the middle of this: $IREN $NBIS $CRWV and others. The $3T is a real number. Treating it as imminent balance-sheet stress is the wrong read. Pictures below are snapshots from our AI Bubble Barometer.
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Agentic AI is changing the question for every SaaS name. The old one was “how big is the market?” The new one: when agents do the work, does the vendor get bypassed, survive as plumbing, or own the stack? We built a scorecard for that split — and ran the first three deep dives: • $PLTR — Route 3 ownership bet (ontology / multi−domain ops). Structure elite; valuation still has to earn it • $DDOG — Route 2 plumbing agents need. Host telemetry is hard to absorb casually; coding agents + first-party collect is the watch item. Multiple roughly fair • $NET — purest agent-volume rails. Unabsorbable network footprint; physics caps the model layer; ~40x prices in a lot Condensed public version below. Full Part 2 + series for Convequity subscribers.
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I do feel like people have cognitive dissonance here: Jeff Dean isn't only about the model application layer — he is the one who masterminded TPU, GOOG's incredible global distributed systems, and making efficient hardware + software actually work. The story isn't simply that models don't matter anymore for GOOG, that GOOG is all in on just AI infra as a more neutral player at the model layer, avoiding heavy investment that could get quickly depreciated by Chinese open source models because infra is the better business. It is simply that GOOG's current model effort is turning south at an accelerated speed. And they know this area is strategically important — otherwise Pichai wouldn't have fought so hard to keep Jeff and others from leaving. You can also excuse GOOG's strategic failure — not foreseeing this year's huge demand, not reserving enough TPU in-house for both model serving and training, and instead selling the capacity to Anthropic — as a smart move to profit from infra and shield away from model competition. It is just that GOOG's internal divisions don't collaborate with each other: the infra team sold out TPU to Anthropic this year in a negotiation done last year, while the model serving and training team never saw the demand coming and never worked that closely with the infra team. By the sheer fact that there are still people sugar-coating this story, I feel less confident about GOOG — although I am still quite bullish on its TPU and infra business. BTW, Anthropic is looking for building its ASIC with $AVGO directly, and also building and running its infra as well. Already they have pivoted away from directly renting GPU from GCP to hosting its TPU and building its kernel to juice up perf by 50% to 2X, and they will continue this optimization route just happening slower than OpenAI does.
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Jeff Dean’s departure is a seminal moment for $GOOG I’ve been watching this closely and the picture keeps getting clearer — and more concerning. Google is still treated as the safe consensus AI bet. Full-stack ecosystem, consumer reach, TPU muscle. But the competitive status of its frontier models looks increasingly dubious. The one-way talent flow tells the story. Google has seen massive outflows to OpenAI and Anthropic (reports put the numbers in the high hundreds to over a thousand in recent periods) while inbound from those labs remains tiny. This is not normal churn. It signals a structural talent problem that shows no sign of being fixed anytime soon. Jeff Dean leaving with Sanjay Ghemawat, Quoc Le and Oriol Vinyals, while Demis Hassabis steps back from day-to-day leadership, is the clearest signal yet that the research edge has peaked. Top talent does not want to waste cycles inside a large, conservative organization when the progress window is this narrow. We’ve flagged Google’s cultural and leadership issues in our recent rebalancing work. This just cross-validates it hard. What’s especially striking is the internal dynamic. All signs point to Koray Kavukcuoglu having been central to the environment that produced these outcomes — yet he has now risen to Chief AI Architect and the unchallenged operational AI leader, reporting directly to Pichai. The research org is fraying rapidly while the executive who oversaw much of that period moves up the ranks. That is a strange incentive structure. Large orgs struggle to deliver breakthrough AI. Talent density, bold vision and nimble execution matter more than scale. Meta rebuilt around an independent lab. SpaceX is doing the same with xAI. Google may eventually follow, but the current setup still seems unwilling to fully accept the reality. That said, the TPU and infra side continues to execute well with far less leakage. The competitive edge is shifting toward infrastructure, chips and consumer distribution — not frontier model leadership. This remains the biggest news of the week for a reason.
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Nvidia’s $2bn Marvell investment is not a simple endorsement $MRVL is one of the few real DSP alternatives to Broadcom. Nvidia lacked that lever — now it has influence. Marvell has also struggled in custom AI silicon and networking (Trainium 3 was a clear disappointment). The path of least resistance becomes designing compute that attaches to NVLink rather than building independent high-speed I/O. On scale-up, the field is largely NVLink vs Broadcom’s Ethernet camp. UALink was the alternative; Marvell was the key switch name. Once Marvell is aligned with Nvidia, UALink’s prospects as a true independent standard weaken. Three effects at once: 1) DSP influence, 2) custom ASICs pulled toward NVLink, and 3) a potential NVLink competitor blunted. We stayed on the sidelines. Not every Nvidia partnership is automatically bullish.
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Why we exited $FIG AI coding agents and prompt-to-UI tools are changing the design workflow faster than most expected. Platforms such as v0, Lovable and Claude Design can already take a team from idea to a working interface without a traditional design file in the middle. Figma has added its own generative features, yet these have largely been layered on via external model APIs rather than a full re-architecture around AI-native workflows. Newer entrants were built for this environment from the start. In public interviews the founding team has consistently struck a defensive posture — essentially arguing the existing franchise is secure — rather than showing the urgency of a company prepared to disrupt its own model. That mindset is rarely a positive signal when a platform shift is underway. After several quarters of waiting for a more compelling response, we exited the position.
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Why we exited $CRSP and kept $TEM AlphaFold solved the backend of the problem — predicting a protein’s 3D structure from its sequence. The harder frontend problem remains: knowing which protein or pathway is actually worth targeting. Most complex diseases are still poorly understood at the causal level. That distinction matters. The next real breakthroughs look more likely to come from AI-native groups that treat large-scale compute as the primary engine, in the same way OpenAI and Anthropic approached language before the ChatGPT moment. Public investors may only get clean access later via secondaries or IPOs. CRISPR sits on the earlier-generation side of that divide. Tempus is closer to the compute-and-data native side — building models while assembling a genuine clinical data moat. That is why one was exited and the other retained.
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Himax and the parallel optical bet $HIMX sits in two places in the optical stack: micro-lens arrays that route light into fiber, and a more speculative but potentially transformative position in microLED-based CPO. The microLED idea inverts the industry’s core assumption. Instead of fewer, ever-faster InP laser channels, it uses hundreds or thousands of simple low-speed lanes (4–10G each). High-speed SerDes and DSP largely disappear, energy drops below 1 pJ/bit, and the continuous-wave InP laser is eliminated entirely — replaced by GaN microLEDs from the display supply chain. Reliability improves and failures become graceful rather than catastrophic. Reach is limited to a few meters and the fiber-bundle ecosystem is still immature, so the technology is confined to short-reach scale-up. It remains pre-revenue and more debated than it was a few months ago. Yet the process know-how from Himax’s high-density display drivers maps unusually well onto this architecture. The stock is up ~55% YTD but down ~18% QTD, and currently trades around 31x EV/FCF and 10x EV/GP. For anyone who still sees a meaningful path for CPO — and views the microLED route as high-upside optionality rather than pure speculation — the current levels look like a reasonable place to build or add. We go deeper on the microLED architecture and its place in the broader optical stack in Part 4 of our Photonics series at Convequity.
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$TSEM + $SOI: positioned on both sides of the biggest shift in AI datacenter interconnect — and Google's new TPUv8i just supercharged the story. Leg 1 — Smarter optics, much bigger chips. Next-gen optical links ("coherent-lite") encode data far more densely than today's IMDD links. But the sophistication has a physical cost: the photonic chip needs roughly 2–2.5x more silicon area per port, because it packs in multiple modulators, splitters, and detectors where today's simpler links need one of each. Factor in that bigger dies yield worse, and you need ~3–4x the wafer starts per port. More area × more volume × worse yield = several-fold growth in SiPh wafer demand over the cycle. Leg 2 — The switch itself goes photonic. TPUv8i connects its chips through an optical circuit switch (OCS) built from silicon photonics — a genuinely new thing. $GOOGL's old scale-out OCS used MEMS mirrors: zero SiPh content. The new one puts a very large photonic die (300–450mm², heading toward 500–800mm² — near the reticle limit) in every pod. That's a brand-new ~10–20% layer of demand on top of the transceiver story, and it scales faster than linearly as switch radix grows from 64×64 to 128×128. The elegant part: Leg 2 forces Leg 1. The optical switch loses 5–10dB of light — 70–90% of the signal is gone by the time it exits. Today's IMDD transceivers can't read a signal that weak; coherent ones can. So every transceiver plugged into the switch fabric has no choice but to go coherent — and coherent means the 2–2.5x bigger chips from Leg 1. Google's switch decision doesn't just create its own SiPh demand; it automatically triggers the transceiver upgrade too. Tower has manufacturing flows for both legs: high-speed modulator + Ge flows for transceiver PICs, AND mature passive flows for the huge fabric dies. Soitec supplies the photonics-SOI wafers upstream either way — it wins regardless of which foundry takes the socket. One more kicker: Celero, the Alphabet-backed startup tipped for Google's coherent DSP socket, has no fab of its own — its companion photonic chip has to land somewhere, and an open specialty foundry is the obvious somewhere. Risks: coherent modulators migrating to InP/TFLN would leak the PIC leg out of silicon photonics; Marvell verticalizing into its own photonics partially bypasses the open-foundry path.