reply @Thomus0x eh, Rosenblatt June 30 note identified AMD as the likely first $AAOI CPO customer. I wouldn't quite say they aren't remotely close.
$LITE / $COHR / $SIVE just look like your top 3 right now in terms of CPO laser visibility per the recent Morgan Stanley note.
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$INTC and $AMD to sign CPU LTAs with Chinese customers for AI DCs (Reuters).
- Prices of some CPU products have risen more than 40% in China since the start of the year from sources.
- Month-on-month increases topping 10% for some products
CPUs were already a bottleneck, following CPU ratios due to AI inference...
But the broader trend of LTAs seems to be appearing from:
- Memory, with $MU, Samsung, $SNDK, and SK Hynix signing DRAM/NAND LTAs.
- Photonics, with $LITE, $COHR signing EML LTAs. And recent Trendforce reports that $AMD and hyperscalers are now pursuing CW LTAs.
And I'm sure there's many more from MLCCs to all the way to substrates.
+1 for the bottleneck investors... hard to be a "bubble that pops" if you have take or pay demand spanning multiple years.
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quote: Tesla shareholder update underway right now! https://x.com/tesla/status/2080021924072366329 | Q2 Shareholder Update → ir.tesla.com
Highlights
– Cybercab began production at Gigafactory Texas
– Tesla Semi remains on track for volume production this year at our new factory in Nevada
– Making continued progress with battery pack capacity expansion (the main limiting factor to near-term vehicle production volume increase)
– Megafactory Texas is nearing completion (start of production planned for this year)
– More customers are now opting to subscribe to FSD at the time of vehicle purchase!
– Robotaxi rollout continued in the US. Now live in 7 major metros
– Construction of Optimus at Fremont Factory began after decommissioning the Model S & X lines. Planned production later this year
From here, there remains much hard work as we aim to revolutionize transportation, energy and productivity through our leading real-world AI. Scaling will be non-linear and we are focused on long-term value creation.
We’ve never been more optimistic about the future.
Automotive
– Record deliveries in several markets: South Korea, Australia, Colombia, Japan, Taiwan, Thailand, Portugal, the Philippines, Chile, Slovenia & Lithuania
– We launched the Model YL in the US in July and have seen a positive response from customers
Energy generation and storage
– Record energy storage deployments in EMEA, supported by record deployments from Megafactory Shanghai, which continues to ramp production
– On track to begin production of Megapack 3 & Megablock this year at our new Megafactory Texas
– Powerwall 3P (three-phase) is now available in Germany and is designed to meet the power needs of German homes with a single unit
Robotics
– Installing the first-generation lines for Optimus at Fremont Factory, where we expect to start production soon
– The initial Optimus builds will be used in our Optimus Academy for training data collection and further functionality development
– Additionally, we continued site development at Gigafactory Texas with building construction now in full swing
AI Training Compute
– More than doubled our onsite compute in Texas (in terms of MW of compute) during the first half of 2026
– Cortex 2 supports the development of both vehicle and humanoid robot autonomy software & will ramp further over the rest of the year
Battery
– Ramping new battery & material factories, including vehicle pack capacity in Berlin, cathode material production and lithium refining in Texas and LFP cells in Nevada for our energy storage products
– Increasing production of 4680 cells to support ramping Cybercab & Tesla Semi plus increased production of Model Y
Other Supporting Infrastructure
– Added over 2,400 net new Supercharging stalls, growing the network by 17% year-over-year
AI Software
– Started rolling out FSD v14 lite to early-access customers in the US & South Korea with AI3 hardware
This software build distills the driving behavior from AI4’s v14 series into both the camera & compute configuration of AI3, bringing destination options & speed profiles. It also addresses challenging driving scenarios with improved proactive & reactive responsiveness
AI Inference Compute
– Making progress on construction & equipment procurement for our semiconductor fab in Austin
Automotive and Other Software
– Rolled out Summer Release:
Self-Driving stats are now available in the mobile app
Grok can make phone calls, search & play music, and adjust climate controls, among other things
Automatic Navigation expands beyond home & work to support any destination based on personal habits and schedule
Robotaxi
– Started production of Cybercab, our purpose-built autonomous EV designed to be the workhorse of our Robotaxi fleet
– Began offering employee rides in Cybercabs on our GFTX campus in July
– Preparing for expansion of our Robotaxi service to additional US metros: testing, permitting & first responder training
– Expanded unsupervised rides to the entire Austin metro area & launched unsupervised rides in Miami, Orlando & Tampa in July
FSD Supervised
– Record net new subscriptions in Q2
– Record FSD attach rates in North America, with over half of new deliveries including FSD subscriptions
– Received additional approvals in Lithuania, Estonia, Denmark & Belgium, with customers in these countries driving over 50 million kilometers (31 million miles) on FSD as of July
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$GOOGL How much are they earning from token sales?
An estimate:
Per the call, GOOGL AI model APIs are processing ~22B tokens per minute (+38% q/q; ~270% annualized), with 500 cloud customers >1 trillion in LTM and 2k enterprises >100B in LTM. Existing customers are exceeding their commitments by >50%.
Rough sizing attempt with Claude on this:
Assuming 22B tokens per minute (implies 11.6 quadrillion per year run-rate), and pricing between $0.10 and $2.00 per million, would imply revenue between $1.2B - $23B (likely on lower half range, considering this skews to cheap flashlite/free tier models AND includes Google's own internal consumption, which is included in token count but NOT in Cloud revs).
Also probably makes more sense to only assume ~70% of these tokens have any monetization.
This analysis shows how powerful the opportunity is as 1) token sales continue to grow high 30s% and 2) they are able to capture more pricing (more off free/lite tiers).
AI tokens sales are likely only 5-15% (low-end) of annualized Cloud revs.
h/t Griffin MacMaster
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quote: $GOOGL now has 950M monthly Gemini users and processes 22B API tokens/min.
Compared to 750M back in February.
Absurd growth adding 200 million more users in a few months. Kinda explains why they ran out of compute, then cut allocations to $META and others.
Very unlikely AI capex will slow down given. | $GOOGL reported earnings today:
EPS: $9.11 actual vs $2.91 expected - retail reactions to EPS blowout is kinda just accounting noise, since $SPCX, Anthropic, and others were likely large contributors.
Revenue: $119.7B actual vs. $116.98B expected
Google Cloud Revenue: $24.77B +82% Y/Y, vs. 63-65% expected ($22.2-$22.6B) - this is a very large beat and most material part so far.
Cloud Operating Income: ~$8.81B, implying 35.6% margin vs. 32.9% Q1.
Capex: $44.92B vs ~$44.15B (basically in line).
So initial read through is Google Cloud accelerating growth with expanding margins is genuinely bullish for AI demand.
Their former $180-$190B capex guidance is already extremely large, and as long as we get around these numbers in the earnings call + forward projections...
Should be good to go for $LITE and the other optics/networking trade. (eg. last earnings, they said DC and networking would be ~40% of capex spend).
Hyperscaler earnings transcripts are probably the most important thing to pay attention to with the AI capex trade.
And that should be in 3 minutes.
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$GOOGL reported earnings today:
EPS: $9.11 actual vs $2.91 expected - retail reactions to EPS blowout is kinda just accounting noise, since $SPCX, Anthropic, and others were likely large contributors.
Revenue: $119.7B actual vs. $116.98B expected
Google Cloud Revenue: $24.77B +82% Y/Y, vs. 63-65% expected ($22.2-$22.6B) - this is a very large beat and most material part so far.
Cloud Operating Income: ~$8.81B, implying 35.6% margin vs. 32.9% Q1.
Capex: $44.92B vs ~$44.15B (basically in line).
So initial read through is Google Cloud accelerating growth with expanding margins is genuinely bullish for AI demand.
Their former $180-$190B capex guidance is already extremely large, and as long as we get around these numbers in the earnings call + forward projections...
Should be good to go for $LITE and the other optics/networking trade. (eg. last earnings, they said DC and networking would be ~40% of capex spend).
Hyperscaler earnings transcripts are probably the most important thing to pay attention to with the AI capex trade.
And that should be in 3 minutes.
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quote: Swarms x @GeminiApp
Gemini 3.6 Flash and the latest Gemini models are now available on Swarms Cloud, making it easy to build production-ready single-agent and multi-agent workflows.
This guide covers API setup, single-agent execution, and multi-agent orchestration with complete Python and cURL examples so you can start building with Gemini in minutes.
Learn more below ⬇️ | We’re rolling out three new models to make AI agents faster, smarter, and cheaper at scale:
🔵 Gemini 3.6 Flash: It uses fewer tokens than 3.5 Flash to deliver higher quality work at the exact same cost.
🔵 Gemini 3.5 Flash-Lite: A fast, cost-effective option for everyday tasks like processing documents and agentic search.
🔵 Gemini 3.5 Flash Cyber: A cybersecurity model built to find and patch critical software vulnerabilities.
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quote: Swarms x @GeminiApp
Gemini 3.6 Flash and the latest Gemini models are now available on Swarms Cloud, making it easy to build production-ready single-agent and multi-agent workflows.
This guide covers API setup, single-agent execution, and multi-agent orchestration with complete Python and cURL examples so you can start building with Gemini in minutes.
Learn more below ⬇️ | We’re rolling out three new models to make AI agents faster, smarter, and cheaper at scale:
🔵 Gemini 3.6 Flash: It uses fewer tokens than 3.5 Flash to deliver higher quality work at the exact same cost.
🔵 Gemini 3.5 Flash-Lite: A fast, cost-effective option for everyday tasks like processing documents and agentic search.
🔵 Gemini 3.5 Flash Cyber: A cybersecurity model built to find and patch critical software vulnerabilities.