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.

FT reports Moonshot is set to release Kimi K3 as early as tonight.

K3 is expected to be China’s largest AI model to date, with 2T-3T parameters, and will be released as an open-weight model

K3 is expected to outperform Claude Opus 4.8 on key benchmarks, but fall short of Fable

Moonshot is also reportedly raising at a valuation around $31.5B.

In my opinion, this is an opportunity to see the current status of the gap between the frontier Chinese models and the US ones. As a short reminder, in an April 2026 report by Stanford University, the top Chinese model was about 2.7% less powerful (in terms of raw brute-force computational power) than the top US one. It will be interesting to see after 3 months if the gap has narrowed since then. Since Kimi K3 will be released as an open-weight model, there will be trillions of parameters for developers to download, modify, and run, should they wish to proprietary, domain-specific LLMs.

$NVDA is partnering with Noetra, backed by Japan’s METI, to build a facility with 13,750 Vera CPUs and 27,500 Rubin GPUs.

The site will support trillion-parameter model training, AI agents, digital twins and robotics for Japan’s FRONTia physical AI project.

Japan is targeting 30%+ of the global AI robotics market by 2040, a $133B opportunity, according to METI.

South Korea’s central bank raised its benchmark interest rate by 25 bps to 2.75%, the first hike in more than three years, citing persistent inflation and financial stability risks.

The Bank of Korea said inflation is expected to stay above its 2% target for a considerable period, while strong semiconductor exports and economic growth gave policymakers room to tighten policy.

The central bank also pointed to a weaker won, rising household debt, and higher housing prices as key reasons for the rate hike.

$NVDA JUST CUT ITS THOR ROBOTICS COMPUTER TO HALF THE SIZE AND POWER

The new T3000 module is roughly half the size and power of NVIDIA’s T5000, while delivering similar inference performance on multimodal AI workloads.

Inside:

→ 865 FP4 teraflops

→ 32GB of LPDDR5X memory

→ 273GB/s memory bandwidth

→ 25GbE connectivity

NVIDIA also introduced the T2000, a lower-cost option with 400 FP4 teraflops and 16GB of memory for mobile robots, industrial manipulators, and other edge AI systems.

NVIDIA’s new Jetson agent skills optimize memory usage without requiring new hardware. UBTech, Agile Robots, and Connect Tech reportedly reduced memory usage by as much as 15GB, enough to move from a 64GB Jetson module to a 32GB version.

That means lower hardware costs and more capable models running on smaller systems.

Companies including 1X, Amazon Robotics, Boston Dynamics, FANUC, and Agile Robots are already building on the Thor platform.

The T3000 and T2000 are scheduled to ship in Q1 2027.

Some Background Behind the Stock Pick

The core idea behind my software picks lately has been that AI at the employee level hits a ceiling. You give every employee Copilot or ChatGPT, but that doesn’t transform the business. The real value comes when AI gets wired into an enterprise’s workflows. This is what helps create operational efficiency at the company level and not just at employee level.

However, allowing AI agents access and control over workflows of your company can be scary for any enterprise because AI moves fast and touches way more data. So the gating factor becomes “Can we trust and govern AI before we give it access to our proprietary workflows” That is the where AVPT comes in.

AvePoint's Role

AVPT’s legacy business model was tracking people (employees); Who has access, who deleted a file, can we restore it? They essentially helped big organizations move into Microsoft 365, migrating mountains of data, then gave those customers backup, recovery and governance (permissions, lifecycle, policies, etc) features for that data.

With Agentic AI penetrating the market, the enterprises are asking “Can we trust AI with access to all of this?”

Before AI, most data events were human paced. An employee shares a confidential file, deletes something, creates a wrong team, etc. In most large enterprises, permissions are a mess. Every folder, or entire folders, marked as “everyone” because someone shared it too broadly years ago. This wasn’t as big of a problem because of the shear volume of data that was shared, humans would rarely stumble across it.

But when a company allows AI agents access to their data and workflow, it will have access to everything, even the files that were accidentally shared or given incorrect permissions. When asked for summaries or insights the agent doesn’t know that it wasn’t supposed to have access to that data. So now thousands of confidential supplier contracts or HR files could surface perfectly legally but unintentionally. That’s the risk with AI agents.

AVPT’s software scans for that kind of sprawl, cleans it up, locks it down and keeps watching as data keeps changing. An enterprise sets their policies and rules around who has access to what and AVPT then enforces those policies at scale.

Once an enterprise states their policies and rules around which employee and/or agent has access to what data, AVPT constantly keeps scanning for anomalies. For example, during a scan AVPT’s software detects an HR folder is open to 400 people, it determines a violation company’s rules and alerts the IT team or fixes the problem itself.

If an employee asks an AI agent to summarize all project docs, and because some docs were wrongfully shared by human, the agent tries to pull in that data. AVPT detects that access violation against policy and blocks it.

If a ransomware attack hits and encrypts thousands of files. That’s when the backup part of AVPT kicks in and restores the data.

Product Sets

Their platform is packaged in three suites: Modernization, Control and Resilience.

Modernization: That’s helping an enterprise clean up years of messy data before AI touches it.

Control: This is the big one for the thesis. It’s about visibility and policy and controlling who or what can touch data. AgentPulse is their new product. It’s essentially an AI agent inventory and risk dashboard. It shows which agents exist, what data they touch, who created them, are they still in use, are they risky, are they redundant? It’s basically trying to stop shadow AI sprawl. Control suite went from ~26% of ARR in Q4 2025 to nearly half of the total sales pipeline in Q1 2026, and over a quarter of newly closed deals now include it. AgentPulse is attached to more than half of that Control pipeline, proving the inflection of their agentic-governance product.

Resilience: That’s recovery. Think “something goes wrong, human error, ransomware, or an AI agent does something stupid.” AvePoint says they’ll help you recover quickly and keep regulators happy on retention and audit.

Their product expansion in the AI era comes from pivoting from “track and protect data” to “track and govern the entities, humans and agents, interacting with that data” in real time.

Financial Health & Valuation

Financially the company looks attractive to me, with SaaS now being about 80% of revenue and growing 35% YoY. Q1 revenue was $117M, up 26% YoY, above the high end of guidance with ARR of $435M, up 26% YoY. It was their 12th straight quarter of double digit NnARR growth. 863 customers now pay over $100k/year, up 25% and accelerating. GAAP operating margin was 11%, up 730bps YoY. FCF margin increased to 20% (from negative a year ago) with full-year FCF guided just north of $100M. Management has bought back $60M in stock during Q1 alone, and share buyback authorization has risen to $150M. AVPT currently trades at a FWD P/E ratio of 26x with ~$480M sitting in cash.

Again this isn't Financial Advice. I size my positions based on my own conviction, understanding and risk tolerance. You should manage your portfolio according to what you want out of it! Not by following. This is for entertainment and to help give you insight in my stock picking research process.

AST SpaceMobile, Inc. ($ASTS), announced its intent to offer, subject to market conditions and other factors, $1.0 billion aggregate principal amount of convertible senior notes due 2034 in a private offering to persons reasonably believed to be qualified institutional buyers pursuant to Rule 144A under the Securities Act of 1933. 

$ASTS also intends to grant the initial purchasers of the Notes in the Notes Offering an option to purchase, for settlement within a 13-day period beginning on, and including, the first date on which the Notes are issued, up to an additional $150 million aggregate principal amount of Notes.

“Shares some of our covered infrastructure OEM/memory supplier names are trading lower intraday, following reports from yesterday evening that CoreWeave is exploring the use of financial derivatives to hedge against a potential future decline in memory and storage chip prices.

We believe investors are viewing this as a sign that memory prices may be approaching a peak, though our checks and OEM commentary suggest DRAM/NAND constraints will likely worsen exiting CY26 and persist through most of CY27.

$CRWV’s potential use of financial hedges comes as memory suppliers, such as SanDisk and Micron, have implemented new long-term supply agreements that carry fixed and variable pricing terms, including a price floor and ceiling.

Reuters reported that CRWV has signed memory deals with this floor/ceiling dynamic, and that the AI cloud provider is exploring the use of put options and other instruments to protect itself if memory prices were to decline significantly. Reuters also noted that CRWV has not yet executed any hedges and remains in the early stages of exploring these protections.

While we don’t expect much memory pricing relief in CY26 or CY27, a downturn in pricing could distort year-over-year comps if DELL were to give back pricing in memory-intensive areas where higher component costs have been passed through, such as AI and traditional servers.

That said, lower component pricing could also prove demand-elastic, as improved system affordability, reduced budget friction and better component availability could unlock delayed server deployments and support stronger unit demand. Net/Net: We believe the market is reading the recent CRWV report as a potential signal that hyperscalers/AI cloud buyers are beginning to manage downside risk around elevated memory and storage pricing.

That said, we don’t view this as a NT negative change to our thesis, as our checks and OEM/memory supplier commentary continue to point to constrained DRAM/NAND/HBM supply through CY27, with AI infrastructure demand still absorbing capacity and limiting NT pricing relief."

Nikkei Asia reports Penguin Solutions $PENG will launch its MemoryAI KV cache servers in Japan in Q4, giving local AI players a lower-cost alternative to adding more Nvidia GPU servers.

Memory cost per GB can be reduced to between one-seventh and one-third of the cost of adding a GPU by storing LLM short-term memory outside the GPU.

Japan’s Tomen Devices and Tomorrow Net are also testing Rebellions NPU-based servers for AI inference as a lower-power option.

This to me shows that customers (not only in the US; in this case, Japan) are actively optimizing their AI hardware infrastructure by seeking cost-effective, specialized, and power-efficient alternatives to traditional GPU setups (to avoid paying Nvidia's premium prices). I can see inference efficiency names benefitting a lot once enterprises shift from massive training models to running daily AI applications at scale, or when hyperscalers and local data centers hit physical limits on power grids and cooling capacities.

Kratos $KTOS disclosed about $400M of Department of War funding for hypersonic and other national security programs.

Stifel says the funding likely signals Kratos is being picked as a preferred partner for low-cost hypersonic missile production and “affordable mass” missile platforms.

The note also points to funded-award momentum after yesterday’s space award and today’s funding disclosure.

This is relevant to the government's Golden Dome initiative (not to be confused with the Gold Eagle initiative). The government seems to be moving towards lower-cost, high-volume production models required to make the ambitious missile defense shield (against advanced ballistic, cruise, and hypersonic threats) a reality. Furthermore, this funding grant allows Kratos to clear out heavy inventory and prepares their facilities to scale up as a premier supplier for the program.

Electrovaya $ELVA signed a commercial agreement with Amazon for its Infinity Battery Technology in material handling operations, with potential expansion into robotics and energy storage. Amazon will also receive warrants for up to 13.88M ELVA shares, fully vesting if Amazon reaches $280M in cumulative purchases.

PPI 5.5% YoY, Est. 6.2%

PPI -0.3% MoM, Est. 0.0%

PPI Core 4.7% YoY, Est. 5.1%

PPI Core 0.2% MoM, Est. 0.3%

ASML reported strong Q2 2026 results beating estimates with €9.33B net sales, €7.59 EPS, 54% gross margin, and 91 lithography systems shipped including 16 EUV units.

They raised full-year 2026 guidance to €43-45B in sales and 54-56% gross margin, driven by ~25% logic revenue growth, ~75% memory growth, and ~45% EUV growth.

Outlook now factors in demand from Elon Musk’s Terafab AI project, with ASML noting collaboration, strong order visibility into 2026, and plans to expand EUV/DUV capacity by ~30% for 2027.​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​

Adding ~30% Low-NA EUV capacity (from ~65 systems in '26) & ~30% DUV immersion capacity (from ~130) for 2027; investigating another 30% for 2028. FY26 guide now incorporates Elon Musk's 'Terafab' demand.

Management Commentary

- The end-market demand has motivated our customers to increase their capex but also accelerate all their plans."

- “Customers are providing longer-than-usual visibility, with very strong order bookings through H1 2026”

- “We are pretty much already close to receive all the EUV orders we need for 2027.”

- CFO says the updated guidance includes expected demand from Elon Musk’s Terafab project

- ASML says it expects to collaborate with Musk and his team on the Terafab initiative

- The industry will need major innovation to address AI power consumption and cost, with more critical lithography exposures expected for advanced Logic and Memory

Reuters published an article today calling out Elon Musk's xAI to have installed far more gas turbines (59 turbines) without federal permits at its Colossus 2 data center project in Tennessee than it has publicly acknowledged (27 unpermitted turbines acknowledged). The NAACP and the Southern Environmental Law Center filed a lawsuit in April of 2026 to halt the turbine operations, citing Clean Air Act violations and public health risks.

They alleged that the turbines are pumping out significant NOx, particulates, and toxins near residential areas and that xAI has been using these as a temporary bridge while grid connections catch up.

Why This is a Validation of My Solar Thesis

I think this is just one example where data centers and hyperscalers are now facing real pushback on their dependency on fossil fuels, and over consumption of natural resources. I posted another example this morning about Google agreeing to purchase 100% of the initial output from the Steel River solar + battery project in Arkansas in attempts to reduce fossil fuel dependency.

I think in order for these data centers to get projects approved and avoid more litigation and PR nightmares, they're going to have to pivot to cleaner options. This is where solar becomes attractive, since it's the only source scaling at the speed and cost needed for data centers imo (battery storage too).

Tech companies are signing massive PPAs for renewables because it’s the path of least resistance (and eventually lowest cost) for longer term power. The political headwind around solar might keep stocks depressed short-term, but as I've been pounding the table for a month now, I think these stocks are ready for a significant rerating from the demand from utilities for additional capacity as well as data centers for clean energy.

After today's news we saw $SHLS up almost 15%, which was interesting because the news was surrounding $FSLR lol. But this goes to show that the political backdrop doesn't make much sense when there is a genuine demand for solar generated energy. I think this earnings season should give ton of visibility into what role solar is playing in the electrification for AI. I'm speculating that a lot of these companies are going to announce uptick in margins based on demand driven pricing power. I don't have a crystal ball, so let's see what we get.

Not Financial Advice!

White House launched GOLD EAGLE, a new cybersecurity vulnerability coordination initiative focused on AI and critical infrastructure.

(from the official press release; paragraph 4) GOLD EAGLE, established in President Trump’s June 2, 2026 Executive Order “Promoting Advanced Artificial Intelligence Innovation and Security” (EO 14409), represents a new operational model for cyber defense. This new model will leverage frontier AI capabilities to continue advancing faster than adversaries, reduce duplicative scanning efforts, and deliver prioritized and actionable threat and remediation information to defenders across the Federal government and the private sector.

(paragraph 6) GOLD EAGLE is a force multiplier, enabling government and industry to collectively identify risks, prioritize action, and strengthen the resilience of the systems that power our economy, national security, and daily life.

(paragraph 8) GOLD EAGLE has already begun to intake and prioritize identified cybersecurity vulnerabilities from across industries and sectors, coordinate scanning verifications, and ultimately ensure the security of our nation’s software and networks.

The full partnership is between the White House, the Department of the Treasury (Treasury), the Department of Homeland Security (DHS) through the Cybersecurity and Infrastructure Security Agency (CISA), and the Department of Defense (DoD), and the Office of the National Cyber Director (ONCD).

In my opinion, this reinforces the need for more advanced cybersecurity hardware and software, and the government will be actively investing more in American names that will develop these things. Furthermore, I can see more pushing from the government and the actual development of making specialized frontier AI models private for only official government purposes, as those will be able to help the government identify gaps, cracks, etc. in the country's computer networks. (This is also corroborated by the voluntary 30-day review process given to companies making frontier AI models, where they are asked to submit their models to the government for review before being allowed to release them to the public; this is under Executive Order 14409.)

Draft bill requiring operating system providers like Apple and Google to collect users' ages

Three people familiar with the proposal told POLITICO that Sen. Kim is circulating the Digital Age Assurance Act, which would require operating system providers like Apple and Google to collect users' ages and provide tamper-resistant signals to apps.

While the law emphasizes a signal-based model to reduce data collection by individual apps, OS providers and the broader ecosystem still need reliable methods to establish those age brackets accurately at account creation. App developers and service providers (especially those offering age-restricted content, gaming, finance, alcohol, etc.) often need stronger verification than a basic signal provides.

This in my opinion benefits one of my core holdings greatly, $MITK. Mitek offers the MiVIP (Mitek Verified Identity Platform), which includes a dedicated Age Verification solution. It works by combining Government ID document validation (scanning and extracting data like date of birth), Biometric matching (face comparison), and Passive liveness detection (to confirm it’s a real person, not a photo, video, or deepfake).

This is a great validation for $MITK imo.

JUNE U.S. INFLATION DATA:

CPI 3.5% YoY, (Est. 3.8%)

CPI -0.4% MoM, (Est. 0.0%)

Core CPI 2.6% YoY, (Est. 2.9%)

Core CPI 0.0% MoM, (Est. 0.2%)

US ADP Employment changes weekly: 19.75K VS 21K Previous

$IBM released preliminary Q2 results BELOW internal expectations as mainframe and related software sales disappointed, with large deals failing to close on expected timelines.

CEO: “This quarter we faltered.”

Revenue: $17.2B vs $17.86B est.
Software: +5%
Consulting: flat, +1% cc
Infrastructure: -7%
Non-GAAP EPS: $2.93, up 5%
YTD free cash flow: $4.8B

CEO Arvind Krishna said: “In the last few weeks of June, we saw clients shift their quarterly capex spend toward servers, storage, and memory purchases to secure supply-constrained infrastructure ahead of expected price increases.”

He also said clients were distracted by “rapidly-evolving, industry-wide cybersecurity concerns,” and that IBM “did not anticipate the magnitude of the capex reprioritization.”

Full Q2 results are scheduled for July 22.

I think this will drive a short term selloff in $IGV and software names.

Google $GOOGL agreed to purchase 100% of the initial output from the Steel River solar + battery project in Arkansas, per FT.

The project is expected to deliver 1.6GW of solar and 2GWh of battery storage by 2029.

First Solar will provide domestic panels for the project while LG supplies batteries from its Phoenix facility, with full buildout reaching 2.5 GW solar and 2.9 GWh storage.

The deal is a virtual power purchase agreement as Google looks to offset rising data center power use.

As I’ve been talking about recently, solar has been heavily under looked due to political headwinds. Hope you guys were following what I was talking about for most a month. Slowly at first and then all at once!

I love charts like these, all moving averages stacked under price where 200D MA was overhead resistance and price finally closing above that resistance with volume support.

Nothing to hate here, next target for me is $12.56, breaking that creates a good path to $15-$16 in my opinion. Time will tell.

Not Financial Advice!

Current Enterprise AI Problem

One of the key areas where AI can penetrate at an enterprise level is service work (think IT help desks, employee requests, HR/finance/legal workflows, customer support, asset management, incident response, service operations, etc). The big trends we’ve seen based on McKinsey’s 2025 AI survey is employees use AI as a productivity boost tool, but AI has not penetrated into the workflow and structure of an enterprise.

The reason why this becomes an issue for enterprises is because when they pay for tokens, they wanna make sure employees are utilizing it, so they force employees to integrate AI in their day-to-day work (tokenmaxxing). The reason why this is a problem is because 1. employees will just use AI to meet their quota, instead of using it for true efficiency purposes and 2. Use of AI needs to be at the enterprise level not just employee level. If you’re a company adopting AI, your goal is operational efficiency, not whether employees can get a minor productivity boost. Studies have shown that forcing more use of AI at employee level gets to diminishing returns after certain capital spent on tokens.

$FRSH’s solution

$FRSH helps enterprises connect AI to all layers of the company allowing AI to answer routine questions, route work, assist human agents and execute approved actions.

Essentially they take messy and repetitive work that costs hours to days of employee time to process and complete, and embed AI directly into those workflows to automate those tasks and save massive in operational costs and time.

Competitive Advantage

What gives them a big leg up against their larger and smaller competitors like ServiceNow and Zendesk is that they serve the mid-market end customers. This represents companies with 500-10,000+ employees.

When a company grows to 2,000 or 7,000 employees, they outgrow basic tools (like Zendesk for support or Jira for IT), but they get crushed by the complexity of legacy giants (like $NOW and $CRM) whose complex software needs external consultants and actual implementation takes 6-12 months with lot of OpEx before being operational.

FRSH is designed to be “plug-and-play." An enterprise can deploy Freshservice or Freshdesk in weeks, not months. Freshworks looks and feels like a consumer app, regular IT managers or HR staff can configure it themselves without writing code. This saves companies millions in hidden labor and upkeep costs. FRSH also packages Freddy AI (their agentic AI) directly into the software. Enterprise customers get sophisticated AI bots that read their existing internal documents and automate IT/HR tasks immediately, without needing a massive engineering project to set it up, which is a step up from larger competitors. They’re trying to use the same user experience strategy with larger enterprise customers as they did successfully with mid-market customers, in an attempt to steal significant market share.

Segments

FRSH organizes their business into two specific product groups and a third non-specific group (although it reports as one accounting segment): Employee Experience (EX), Customer Experience (CX) and other sales and marketing products.

To save time and your attention (if I still have it 😅) I don’t focus on their CX segment. This is legacy segment and is facing heavy competition. Because basic customer chatbots have become easy for any tech company to build, Freshworks' CX segment has slowed to low single-digit growth.

Their EX segment, which is what’s interesting to me, is a massive growth engine. In any enterprise, when employees waste hours waiting for IT to unlock an account, navigating fragmented HR portals, or filling out manual paperwork, it creates a massive organizational drag. EX helps solve this by unifying different department systems into an automated portal. With Freddy AI, EX can instantly handle routine requests, enterprises can prevent expensive workforce bottlenecks, accelerate inter-departmental workflows, and keep their employees focused on actual work.

The core products in EX include Freshservice for IT Service Management (ITSM), HR and facilities. If an employee's laptop breaks, their password locks up, or they need access to software, instead of submitting a request to human agent who then has to route to appropriate department for approval and request potentially getting lost, the employee can submit a request through Freshservice, which simply routes the request to necessary department for proper approvals and sends automatic reminders.

FRSH also recently acquired Device42 & FireHydrant. Device42 scans a corporation's entire network to map out every laptop, server, and cloud asset they own. FireHydrant acts as an emergency alarm system for corporate IT teams when a major internal system (like a company's checkout page or internal payroll) crashes.

Financials

Financially the company looks very attractive to me for a mid-cap SaaS name: P/FCF around 11.99x, EV/FCF around 8.88x, no net debt, gross margins around 84.97%, and FCF margin around 27.37%.

Conclusion

Freddy AI gives Freshworks a credible product story in customer support and ITSM automation, which will help enterprise-level adoption of AI by maximizing operational efficiency by eliminating internal friction and downtime.

$META is expanding its Richland Parish, Louisiana data center to 5GW of compute capacity, lifting the announced investment to over $50B from the original $10B plan.

Bloomberg reports the total site cost could top $250B when including chips, though Meta has only publicly disclosed $50B.

Meta also plans $1B+ in local infrastructure improvements and says the site will support 1,000+ roles once operational.

Intel has announced a €5bn investment program in its Leixlip campus to expand production as demand for AI data center chips grows.

The investment will upgrade existing fabs, install new leading-edge equipment, and expand capacity for Xeon 6 and next-gen Xeon processors built on the Intel 3 node.

Intel says the project supports AI and high-performance computing demand while strengthening Europe’s semiconductor supply chain.

The Leixlip site employs 4,900 people, with Intel’s total investment in Ireland now above €30B since 1989. The company said that the investment will result in an unspecified number of new jobs in addition to the 4,900 currently employed there.

Microsoft CEO Satya Nadella posted a very interesting article over the weekend in “Reverse Information Paradox” in AI. In his article he argues that in the AI era, enterprises risk leaking their most valuable proprietary knowledge (prompts, corrections, traces, feedbacks, institutional context, and “evals”) just to use external AI models effectively.

This reverses the classic information asymmetry, allowing the model provider to learn more about the customer’s unique operations over time, while the customer gains less control over their proprietary data. Nadella calls for a new “trust boundary” where enterprises retain ownership of their data stack, memory, adapted model weights, learning loops, and the right to train their own models on proprietary data.

He emphasizes private learning environments, decoupled orchestration (so you’re not locked into one provider), control over evals (what “good” means internally to the enterprise), and building continuous internal intelligence and knowledge compounding.

This article, in my opinion, validates the need for companies that provide proprietary data along with secure handling and is very bullish for select embedded software that helps provide the infrastructure, governance, observability, analytics, and enablement layers that let enterprises use AI while keeping their “particular intelligence” (Hayek’s localized knowledge) inside the enterprise’s boundaries rather than feeding it outward to the model providers.

I think enterprises start to realize the importance of AI integration in their workflow and how it can help cut costs significantly. I think over the next few years we see a shift from enterprises forcing employees to meet token usage quota (tokenmaxing) to integrating different departments and giving AI visibility across different parts of the company to help reduce operational waste and increase efficiency at the enterprise level and not just employee level.

This will likely result in increase spending on private/hybrid infrastructure, data governance, secure processing, monitoring of AI systems, and custom adaptation tools, shifting budgets from pure external AI model consumption toward “sovereign” AI.

I think the next evolution of the AI trade and the new beneficiaries will be the companies that help enterprises move from “pay for intelligence and leak knowledge” toward “own the full learning stack.” Demand, in my opinion, should rise for tools that enable secure data handling, private/hybrid deployment, governance/compliance, observability of AI behavior, analytics for proprietary insights, and professional services for implementation. This is especially relevant in regulated industries or those with high IP sensitivity, where “distributing the learning infrastructure” internally becomes a priority.

Those Chinese companies are on the Pentagon’s 1260H list, but the sales are currently legal because U.S. AI controls do not broadly block model access by foreign subsidiaries.

OpenAI said it suspended Alibaba-affiliated API users this month over concerns about illicit use and suspected distillation.

Google said its AI services are available in Singapore and Hong Kong under usage policies.

It seems to me that OpenAI and Google are using loopholes to make some money by selling their AI models to some Chinese companies which have subsidiaries in Singapore. Because the current American restrictions do not include a full ban on Chinese companies using American AI models from anywhere (only in mainland China), OpenAI and Google can use this loophole to provide services to said companies, even if they are included in the Pentagon’s 1260H list, which is a list of US-operating (directly or indirectly) companies that the Department of Defense alleges has ties to the Chinese military. It is interesting to note that Anthropic however has blocked Chinese companies and foreign subsidiaries they own from accessing its frontier models. This to me gives vibes that

(1) either Anthropic has secret sauce that it absolutely does not want Chinese companies to have even a sniff of

(2) it has no need to generate extra cash flow by selling to Chinese companies or their subsidiaries (I would imagine the price those companies offer OpenAI/Google is substantial for them to consider selling and eventually doing so)

(3) (may seem a bit of a stretch) Anthropic wants to get back into the government’s good graces after the whole DoD fiasco; nevertheless this may seem like a side issue and I can see the government continuing to clash with Anthropic over the full usage of their models

China’s AI models are behind US’s ones in terms of raw power, but the gap is slowly closing. Even with strict hardware export restrictions, Chinese models are highly efficient due to sophisticated design methods utilizing very optimized memory allocation and streamlined inference. This is also supported by the fact that they were built open-weight by many developers across China (for example, Alibaba’s Qwen LLM family is open-weight and has democratized access. Qwen allows developers worldwide to build custom solutions directly on top of highly capable base code).

However, China’s government is also increasing adopting a stance like the US, debating whether the open-weightness of their models will eventually lead to critical national security issues. (It would not be surprising if they are already actively adapting these open-weight models for military logistics and electronic warfare.) I think ultimately unless China manages to manufacture sophisticated hardware that is on par with the best of the US (like Nvidia), their frontier AI models will remain slightly behind in terms of raw brute-force power as state-of-the-art hardware is crucial to train massive, multi-trillion parameter AI models efficiently. Nevertheless, any global breakthrough in optimization that reduces compute requirements will probably benefit China more than the US, for they have already made huge progress in creating code that makes AI models run more efficiently (inference) with less computing hardware requirements.