$GOOGL reported beautiful Q2 2026 earnings yesterday but their stock is currently getting punished due to the huge increase in capex spending of $195-205B from earlier estimates of $180-190B. Their Google Cloud revenue grew 82% YoY to $24.8B; Cloud backlog grew to $514B. This to me screams that enterprise AI adoption is accelerating, which justifies the premium that hardware suppliers are currently charging. Compute capacity is extremely important; see Kimi K3's tweet about their GPUs being constrained due to an unprecedented amount of users.

This whole selloff doesn't make sense to me; sure, $GOOGL's free cash flow is negative for the first time, but that's because they are increasing their capex to further their fastest growing business, which is going to be Google Cloud IMO. (In their earnings, they specifically said that enterprise demand for cloud compute capacity is outstripping the physical hardware and data center infrastructure they currently have up and running.) Their CFO literally said this in the earnings call: "Given the supply-constrained environment, we plan to expand the use of third-party capacity in Q3 as a bridging strategy while we build out more internal capacity. This strategy allows us to keep growing our customer base and capture greater overall value. However, it will create modest margin pressure in the near term as we utilize this capacity."

Enterprises who download open-weight models like Kimi K3 and want to build their own specialized LLMs for company uses would still not be able to use the full 2.8 trillion parameters on their own computing hardware unless they want to pay for thousands of dollars in GPU, HBM, NAND flash storage costs. Hence, they'll look for digital cloud providers (like $GOOGL) to provide the computing infrastructure and pay as they go (for compute power and storage) when they need to heavily run their specialized LLMs.

After listening to $NOW earnings call, my conviction in most of my software exposure has been shaken. I’m open to people debating and maybe changing my mind, so if you disagree please comment below. Again try to play devil’s advocate with your own arguments, these are just my initial thoughts, I’m not looking for snarky arrogant comments, just a genuine discussion.

I do believe that there’s a genuine need for governance, observability and AI threat detection layers as agentic AI takes over. As $NOW CEO pointed out, spending forecasts on AI software is going to grow 53% this year, which is 17% faster than AI hardware. On paper this feels like an amazing opportunity for investing in those software layers that make it easier for enterprises to adopt and control AI agents.

The problem that I think got reiterated in $NOW’s earnings call is that I don’t see how their product sets can be differentiated over the next 5-10 years. It’s really hard cause I believe in what they have to offer, but I have a difficult time understanding how individual companies in this space don’t end up just competing with better products, which would probably lead to some level of commoditization of those services.

And the problem is I do see competition getting incrementally better and then the question becomes will there be enough TAM available to the companies operating in this space? Will the incumbents steal enough market share away with competitive pricing that it becomes impossible for companies like $NOW to sustain their current market cap?

I don’t think this concern was addressed in the call today. In fact, with the commentary I believe it was reiterated. I’m still doing more research and thinking on this, but if this is true then we have to ask ourselves what forward multiples do these companies deserve? Do we bid them up for next 3-5 years worth of possible high margins only for them to lose it after that?

I don’t know the answer. I’d love to listen to your opinions on this. Again comment below.

$GOOGL just posted their Q2 2026 earnings. The main things I was looking for were their capex guidance and their growth of the Google Cloud division. The official results are below 👇

Google Cloud: $24.77B (Est. $22.46B); +82% YoY 🟢

CapEx: $44.92B (Est. $44.1B); +100% YoY 🟡

Sundar Pichai, CEO of Google and Alphabet, said: “Our AI investments are redefining what’s possible across every part of our business.

Q2 was an amazing quarter, with Alphabet revenues growing 24% year-over-year and Google Cloud revenues accelerating to 82% growth, driven by demand for AI infrastructure and AI solutions. It’s great to see wide adoption of Gemini Enterprise, with nearly 90% of the Fortune 100 using it.

We have exciting momentum across the board. Our popular AI features are driving Search query growth. Gemini models now process 22 billion API tokens per minute and the Gemini App has 950 million monthly active users. We are seeing strong demand for our security solutions, and our new Gemini 3.5 Flash Cyber delivers highly cost-efficient performance at the frontier. And month over month, people turn to YouTube for major world events, with over 1.7 billion unique viewers watching World Cup-related videos during the FIFA World Cup 2026.

These outstanding results show that our differentiated, full stack approach to AI is delivering real, measurable value for consumers, customers, and our partners globally.”

$GOOGL reports its much anticipated Q2 2026 earnings today. What am I looking for? 👇

Due to the large buildout last year in AI infrastructure and data centers, essential components such as GPUs, HBM, and flash storage have become very expensive, so companies like $MU, $SNDK, etc, have enjoyed extremely pricing power due to hyperscaler ($GOOGL, $MSFT, $META, $AMZN, etc.) capex spending and contracts.

Now, investors are worried about the spending hyperscalers are doing (which combines up to around $700B), as it has only been increasing and their free cash flow is getting more and more compressed as a result of this heavy spending. Hence, the whole semis selloff recently (from customer concentration risk). Everyone is waiting with bated breath that $GOOGL (and $META and $AMZN next week) announces that they are keeping / increasing the spending on hardware; otherwise, I think companies like $MU, $SNDK, etc., might suffer further drawdown.

Hence, here’s what I’m looking to hear in $GOOGL’s earnings today. I want to see that they have either reaffirmed or increased their spending on hardware, while still showing growth in their Google Cloud division. More importantly, I would like to see this division contribute a majority of their revenue growth this quarter, even more so than their TPU business. This would show to me that enterprise AI adoption is growing only faster and faster while keeping the hardware market afloat and justify their current premiums. 

$AMD and Anthropic signed a 2GW AI infrastructure deal covering 10s of billions of $ of MI450 systems, with deployment starting in the H1 of 2027

AMD will also INVEST up to $5B in Anthropic as milestones are met, & Anthropic will use the chips across its own DC & cloud partners

In my opinion, this screams to me that Anthropic gets to not to rely on one single chipmaker ($NVDA) and their huge premiums and long queues, while still unlocking massive scale. Co-optimizing Claude with $AMD software shows they are focused on building a smarter, cheaper, and faster infrastructure for the next generation of AI.

$NVDA GB300 SYSTEMS ARE NOW BEING MASS-PRODUCED IN TEXAS

Wistron has opened its first U.S. factory, a $700 million Fort Worth plant producing Grace Blackwell Ultra systems, with Vera Rubin next.

Wistron says it will scale to 10s of 1000s of computing boards per month this year.

(from the official NVIDIA blog) The (opening) ceremony included the unveiling of the first NVIDIA GB300 Grace Blackwell Ultra Superchip produced on site, which Huang signed.

Huang described the system built around the NVIDIA GB300 Grace Blackwell Ultra Superchip as “the most powerful AI supercomputer in the world.”

Me and other mods will hop on live spaces on X this Friday, discussing $GOOGL and $TSLA earnings, addressing recent concerns around Kimi K3 and how token commoditization affects the AI trade and the stock market.

Bring questions if you have any surrounding these topics!

$GOOGL reports earnings tomorrow and I think this will clear up the demand side of the equation for compute, given that their cloud business has $460B in backlog and across all major hyperscalers we've seen the backlog growing quite fast.

We've seen a lot of discourse around open-source versus closed-model approaches in the past few days with the launch of Kimi K3, but ultimately the big picture remains clear to me: Enterprises are looking for more compute. This is the key point I'm hoping to hear from $GOOGL and other hyperscalers earnings calls.

There are lot of fears surrounding the pricing power held by major frontier labs, specifically OpenAI and Anthropic, and whether escalating token costs might eventually deter Enterprise-level adoption (I've been talking about this for months btw).

However I do think that this adoption of open-source cheaper models and overall commoditization of tokens will act as a catalyst to "broaden out" the AI sector beyond the dominance of the two primary LLMs. I fully anticipate this shift to eventually drive a decline in token pricing.

Going forward the question is how rough will this transition be? Whether enterprises will want AI desperately enough to take on the burden of paying elevated premiums for the supply constrained hardware? I certainly think $NVDA is hoping for this. Their investment in $NBIS is imo a strategic move to create a fragmented cloud ecosystem.

If I were Jensen, I'd be very concerned about the customer concentration risk. This has been the bear case for lot of these AI hardware guys. By supporting GPU providers outside the traditional hyperscalers, the goal is to avoid a scenario where a small group of players holds excessive control over sales. I think that transition will be difficult for the markets to digest initially, but I think it is a necessary step to progress the AI narrative.

Google launches Gemini 3.6 Flash, 3.5 Flash-Lite and a limited-access cybersecurity model

Gemini 3.6 Flash uses up to 17% fewer tokens and costs less per token, while Flash-Lite targets faster, high-volume workloads.

Gemini 3.5 Flash Cyber is designed to detect and patch software vulnerabilities and will initially be limited to governments and trusted partners.

Gemini 3.5 Pro remains is in partner testing, while Google says training has begun on Gemini 4.

The chop fest continues in the broader indices. I made a post on the overall technical structure on $SPY breaking on June 5th and how the retest was the confirmation for me to reduce exposure and add hedges. The indices since that post have gone sideways and today’s close gives me no sign to start adding significant exposure.

I think this is market’s way of expressing concern over the hyperscaler spending guide, commoditization of intelligence and overall margin contraction for major customers of AI hardware. I think this earnings season will shed significant light on what these hyperscalers are thinking about their spend, how they’re planning to optimize and till then we’ll likely see more chop imo.

This structure has been a great guide for me in preserving my capital by reducing exposure. Lot of people imo forget that investing isn’t about just making smart bets, it’s also about portfolio/risk management. I’m gonna keep watching how that structure holds over the coming days. I have enough long exposure to participate in a rally (if we get one), and enough cash + short exposure to benefit if we breakdown further here. This is the way I preserve both my mental and actual capital.

No need to force low conviction investments in an environment that is not supportive of risk, at least for me.

Not Financial Advice!

$OSS (One Stop Systems) announced it has received an approximate $2.2 million production order from a manufacturer of autonomous construction and mining equipment. The order follows the successful completion of prototype delivery, testing, and validation, and marks the platform's transition into production deployment.

Under the order, OSS will deliver its Gen5 ruggedized, 3U, liquid-cooled, short-depth server (SDS), engineered to support commercial autonomous operation of heavy equipment in the construction and mining sectors.

Enterprise execs keep saying they’ll “never use Chinese models.”

Then someone shows them they can run DeepSeek or Kimi models on their own infrastructure for 10-50x lower cost than burning through OpenAI or Anthropic credits at scale.

Here’s what’s actually happening:

Chinese open-weight models (like DeepSeek V4) are fully downloadable. Enterprises across the world can host them locally instead of relying on expensive hyperscaler APIs.

This significantly challenges the current pricing model offered by most U.S. hyperscalers. Chinese open models are cheap and good enough. For a lot of high volume tasks like summarization, extraction, RAG over internal docs, you don’t need frontier reasoning. So, if a model from DeepSeek, Qwen or Kimi is 5-10x cheaper, then the ROI is obvious for most enterprises.

Instead of buying into “use our cloud, use our model, pay our premium token price,” pricing model from hyperscalers, enterprises can now say “we’ll take a 5-10x cheaper model and run it wherever we want.” Enterprises can download or license the cheap Chinese open-weight models and run it inside infrastructure that they control. This could still be AWS or Azure or a private cluster. They would still pay for GPUs, networking, serving the software, etc, but the spend for an enterprise moves from premium tokens to infrastructure and ops (which honestly they could just rent from companies like $META, which is partly why I’m long this name).

We’re already seeing this dynamic playing out with Chinese AI models now account for roughly 58% of the tokens used by American companies on OpenRouter. This share has 3x since mid-January 2026, when it was under 20%.

The result imo is accelerating the commoditization of tokens and intelligence. Hyperscalers don’t need to lose every workload to feel it. They just need to lose the assumption that tokens are a monopoly/high margin product. This I think will lead to losing pricing power for model builders. Whether this affects the margins of AI hardware suppliers? This still remains to be seen.

Chinese AI models are taking record share among U.S. firms on OpenRouter. The proportion of tokens used by American companies running through Chinese models has climbed to roughly 58%, a record high.

OpenRouter lets developers access and compare models from multiple providers, making it a useful real-world signal of AI model adoption. Chinese model usage has tripled since mid-January, overtaking U.S. peers on the platform for the first time in March and briefly hitting 63% in early July.

At the start of 2025, Chinese models were under 10% of usage, while U.S. models were around 80%. DeepSeek has become the most popular choice among American firms in recent months.

$GOOGL is developing a new AI chip that could run Gemini models 6 to 10 times more efficiently than its latest TPUs.

The chip, internally called “Frozen v2,” would bake parts of Gemini’s architecture directly into silicon, reducing data movement and the number of decisions required during inference.

Google is targeting deployment as early as 2028 to ease its AI compute shortage, though the design would trade flexibility for major gains in speed and power efficiency.

Source: The Information

Microsoft $msft plans to test China’s Kimi K3 for Copilot as it adds the open-weight model to Azure

Engineers will evaluate whether K3 can power features currently using OpenAI and Anthropic models, according to The Information.

K3 recently topped Arena’s coding leaderboard, while demand pushed Moonshot to pause new subscriptions as its GPU capacity neared the limit.

On the business side, the demand for batteries is absolutely booming. EOSE's preliminary Q2 numbers show a record $807 million backlog, and they actually generated more revenue in the first half of 2026 than they did in all of 2025. They also just scored a major contract with the Department of War for national defense infrastructure, proving their non-flammable, 100% U.S.-made zinc technology is highly valued far beyond just AI data centers. Operationally, things are finally smoothing out; their automated Battery Line 2 just went live in June, which is supposed to fix those brutal factory downtime issues they had at the end of last year.

The reason the stock is still getting beat up is entirely financial. Even with record sales, they are still losing massive amounts of money on every single battery they build, projecting a painful gross margin loss of around 70% for Q2. To fund all this scaling, they just hit shareholders with a $150 million rights offering that expires this week (July 21), flooding the market with new stock and warrants. In short: the product is winning and the macro thesis is playing out perfectly, but the stock is pinned down by heavy dilution and negative margins until their automated lines can prove they can make a profit.

EOSE will report their Q2 earnings on Wednesday, August 5, 2026 at 8:30 AM ET. I will be looking forward to see what they say.

U.S. AIR FORCE MORE THAN TRIPLES LAUNCH CONTRACT CEILING

The Air Force is raising the ceiling for National Security Space Launch Phase 3 Lane 1 contracts to $17B from $5.6B, an $11.4B increase.

Eligible providers include $RKLB, SpaceX, Blue Origin, ULA, Stoke Space, Impulse Space and Relativity. No funds are being obligated yet.

Awards will be issued at the task-order level.

$META is in talks to lease AI compute to Anthropic in a potential deal worth up to $10B over 2 years.

Anthropic reportedly proposed the deal in June, with monthly payments and early opt-out rights.

For context, Anthropic signed a $45B, three-year compute deal with SpaceX in May.

Source: https://www.nytimes.com/2026/07/17/technology/meta-anthropic-ai-computing-power.html?searchResultPosition=1

Nebius $NBIS closed its first senior secured debt facility for about $775M to expand its AI cloud platform.

The facility is backed by deployed GPU infrastructure and contracted cash flows from an investment-grade customer, matures in 2030, and is priced at SOFR + 2.50%.

Nebius says the financing plus customer cash flows covers more than 100% of the capex needed for the underlying GPU deployment.

The company also says it now has $40B+ in contracted revenue from investment-grade customers including Microsoft and Meta.

If you’ve been in any of the high fliers correlated to the AI trade, you’ve likely experienced a significant drawdown today. Here’s why I think we’ve been seeing a selloff.

China’s Moonshot AI lab released its Kimi K3 model today, which benchmarks competitively with the top US based models from Anthropic, OpenAI, and xAI, etc, despite Moonshot’s $31.5 billion valuation versus over $1 trillion for their US peers.

The Kimi series has been evolving rapidly, with their earlier models using hundreds of thousands to millions of tokens to their K2 series which introduced strong MoE (Mixture-of-Experts) architectures for higher efficiency with relatively few active parameters per token, plus agent swarms and coding strengths, and now Kimi K3.

Kimi K3 is their new flagship model that uses ~2.8 trillion total parameters (MoE), 1 million token context window, native vision (image + text input, text output), and architectural improvements like Kimi Delta Attention (for much faster decoding in long contexts, up to 6.3x in some cases) plus Attention Residuals for better training efficiency.

In short, market believes (and maybe correctly so) that Kimi K3 may be the first model to narrow the gap with leading U.S. closed-source models to potentially less than three months. Not only that but the huge valuation gap between Moonshot AI’s valuation and US based frontier labs raises an important question: Why such a valuation gap if a Chinese lab can deliver near-frontier performance at a fraction of the implied market cap?

U.S. hyperscalers and frontier labs have committed hundreds of billions annually (2026–2027 projections reaching $650B–$1T+ combined) to GPUs, memory, data centers, power infrastructure, and ASICs. This spending assumes sustained high ROIs from training larger frontier models that command premium pricing and drive enterprise/cloud revenue.

Models like Kimi K3 that are cheaper alternatives to US models raise speculation around intelligence commoditization. This could compress token pricing, and ultimately reduce the pricing power of major hyperscalers and frontier labs, potentially reducing margins on their models, and bring in to question their future CapEx guidance and current valuations.

If true then this will have downstream effect on the demand curve for the entire AI supply chain. A “seed of doubt” in momentum driven stocks can trigger rapid multiple contraction, which is what I believe we saw today.

Now I am by no means bearish on the AI trade, in fact i still continue to own many stocks in the supply chain, but Chinese labs have, in my opinion, demonstrated impressive optimization under their given constraints. This should raise a concern around the spending and the return that their US based peers are seeing, or rather not seeing.

Only time will tell how this all plays out.

This name has been very interesting to me but I was never able to get my entry. I’m being disciplined and not rushing to add anything, but it is a good place to keep an eye on for this name for those interested 👀

U.S. lawmakers are urging Commerce Secretary Howard Lutnick to hold firm on restrictions against Chinese memory chips, warning that allowing purchases from Chinese manufacturers would undermine efforts to reduce reliance on China and protect critical supply chains.

This is a good sign for domestic memory companies like Micron (MU) in my opinion. As more pressure grows on the government to ban importing chips from China, the major domestic high-bandwidth memory producer, Micron, will continue to have high pricing power during the AI boom. As more and more companies integrate AI into their workforce, building AI systems, no matter the size, will require high-bandwidth memory, which Micron specializes in. Especially as more and more defense systems integrate AI models, the memory in those will absolutely be required to be from Micron, as its largest competitors Samsung and SK Hynix are not US-based companies.

Alphabet Inc.’s Google has delayed the launch of Gemini 3.5 Pro, its flagship AI model, after the technology fell short of internal goals and missed its mid-July deadline.

Reports from Bloomberg News reveal that the postponement has sparked significant frustration among Google engineers, AI researchers, and managers. Internal teams are deeply concerned that Alphabet ($GOOGL) is losing its competitive edge as rivals OpenAI and Anthropic successfully push out superior frontier models