AI Analyst @MilkRoadAI | Finding opportunities across AI, photonics, defense, space, and tech.
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Did you guys listen when I told you to buy $CRDO?
The stock is now up massively from where I first started buying and it’s up another 8% today.
You can check out my entire portfolio and see exactly what I’m buying here.
link.milkroad.com/tx5qip
Here is why I am adding to my Credo Position after the earnings.
Credo delivered another quarter of triple digit revenue growth, remained extremely profitable and guided above expectations. One of the problems was that Credo had become priced for near perfect execution so investors wanted a much larger beat and a bigger increase to the full year outlook.
If you don’t know what Credo is it’s basically one of the most important connectivity companies inside AI data centers. NVIDIA and other AI chips provide the computing power, but those processors still need to communicate with switches, memory and thousands of other chips. Credo builds the cables, processors and optical components that move this information around the data center. Its largest business is Active Electrical Cable or AECs. These are advanced copper cables containing Credo chips that strengthen and manage signals as data moves between AI servers and network switches. They provide lower power consumption and greater reliability than optical connections across short distances while being thinner and easier to use than traditional passive copper cables. Credo is also expanding deeper into optical networking. Its optical DSPs clean up signals traveling through optical transceivers, while its silicon photonics chips help convert electrical data into light. After acquiring DustPhotonics, Credo can now sell the DSP, the silicon photonics chip or the complete ZeroFlap optical transceiver. This increases the amount of Credo technology inside every connection and could make optics the company’s next major growth engine.
ZeroFlap is especially interesting because connection failures are becoming a serious problem inside massive AI clusters. Credo’s PILOT software continuously monitors signal quality and can detect when an optical connection is beginning to weaken. The system can then remove the affected GPU or reroute traffic before the connection completely fails. When a data center contains billions of dollars of GPUs even a small improvement in uptime can be extremely valuable.
Okay so the latest earnings were objectively strong in my opinion. Quarter revenue reached a record $479 million, increasing 9.6% sequentially and 114.7% year over year. Wall Street expected approximately $473 million. This was Credo’s seventh consecutive quarter of triple digit revenue growth. Profitability was also extremely strong. Credo generated $236.3 million in adjusted net income, up 140% year over year, producing an adjusted net margin of 49.3%. Adjusted operating income reached $230.6 million, giving the company a 48.2% operating margin. Another reason for the selloff is that growth is beginning to decelerate. Revenue grew 115% year over year this quarter, while second quarter guidance implies approximately 98% growth. That is still incredible growth and does not suddenly make the stock expensive but the market rarely rewards decelerating growth. When a stock is priced for near-perfect execution, even a slowdown from incredible growth to extremely strong growth can cause its valuation to compress.
Guidance was also better than expected. Credo expects second quarter revenue between $525 million and $535 million, compared with Wall Street’s estimate near $520 million. Management maintained its forecast for more than 85% full year revenue growth, more than $600 million in optical revenue and an adjusted net margin near 50%.
AEC growth is also beginning to normalize. AEC revenue more than doubled during fiscal 2025 and more than tripled during fiscal 2026. Management still expects the business to grow, but acknowledged that optics will grow faster moving forward. Credo now needs to prove that its optical products can ramp quickly enough to become a second major growth engine. Margins also created some concern. GAAP gross margin fell from 68.2% to 64.5% sequentially, although most of that decline came from stock compensation and acquisition related amortization. Adjusted gross margin only declined from 68.3% to 68%, so the underlying business did not suddenly become less profitable.
Stock compensation is a bigger concern. Credo excluded nearly $88 million of stock compensation when calculating adjusted earnings, up from roughly $35 million last year. Diluted shares also increased around 5% year over year. Adjusted earnings show how strong the underlying business is, but this compensation still dilutes existing shareholders. Customer concentration remains the largest risk. Credo’s four biggest customers represented 33%, 28%, 13% and 10% of quarterly revenue. That means four customers generated approximately 84% of total revenue, while the two largest generated 61%. Losing one major deployment or experiencing a customer delay could materially affect results. The bull case is that Credo is no longer simply an AEC company. It now owns more of the connectivity stack, including copper cables, optical DSPs, silicon photonics chips, complete optical transceivers, retimers, memory connectivity and diagnostic software. This gives Credo more products to sell to the same hyperscalers while increasing its potential revenue from every AI accelerator
I think the current price offers a much better risk to reward than the stock did above $200. If you did not already own Credo, I would view this as an attractive price to begin building a position. The company is still growing extremely quickly, remains highly profitable and is expanding into several new connectivity markets. However, with the current macro environment and weakness across AI stocks, I would scale into the position instead of buying everything at once.
If you enjoyed reading this, make sure to follow @MelvinInvests for more AI infrastructure and semiconductor insights and turn on post notifications so you don't miss a single update
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Melvin retweeted
Satya Nadella and Mark Zuckerberg are now saying the same thing, the AI agent race will be won by trust, not intelligence.
Autonomous agents require access to credentials, private data, payment systems, and company software before they can become genuinely useful. That makes governance one of the largest barriers to adoption. Enterprises need to know what an agent can access, who authorized it, what actions it completed, how much it spent, and whether those actions can be reversed. This is why Nadella believes Agent 365 could become more important than the individual agents themselves. Microsoft is positioning it as the control layer that allows companies to observe agents, assign permissions, enforce policies, monitor spending, and maintain an audit trail of their activity.
Zuckerberg recently made almost the same argument. Meta had a capable version of Muse months before launch but the company delayed it to improve the product’s privacy and security systems. Zuckerberg believes that instruction following, intent understanding, and alignment with user values will matter more than simply improving model benchmarks. Meta built Muse around a dedicated virtual machine that isolates each agent and stores the user’s connected data and credentials. A separate Sentinel system reviews its activity, while sensitive actions such as sending emails or completing purchases require user approval.
Microsoft and Meta are approaching the opportunity from different directions, but both have reached the same conclusion. Microsoft is building the governance layer for enterprise agents, while Meta is building the trust architecture for personal agents. The companies that control identity, permissions, audit trails, secure credentials, policy enforcement, and agent spending could become just as important as the companies building the underlying models.The biggest moat in AI agents may not be creating the smartest system but rather creating the system that consumers and businesses are willing to trust with everything.
I’m positioning around the companies building the trust and control layer for AI agents because that may become just as important as the models themselves. If you want to see exactly what I hold across enterprise AI, agents, and the infrastructure behind them, check out my Milk Road Pro portfolio below.
link.milkroad.com/d8cj6v
Mark Zuckerberg believes the next phase of AI will not be won by whichever company builds the model with the best benchmark scores.
It will be won by the company that users trust enough to let AI act on their behalf.
That is why Meta reportedly had a strong version of Muse ready months before launch but delayed its release to improve its privacy and security systems.
Muse does more than generate answers because it can send emails, complete forms, make purchases, book travel, and interact with other services for the user.
Those capabilities make trust far more important because an error could affect someone’s money, private information, or real world decisions.
Meta attempted to solve this by giving every Muse user a dedicated virtual machine that isolates the agent and stores connected data and credentials.
A separate Sentinel agent reviews what Muse tries to do, while sensitive actions such as sending an email or completing a purchase require the user’s approval.
Muse also cannot directly view passwords or payment details, and users receive an audit trail showing what the agent has done.
This helps explain Zuckerberg’s belief that instruction following, intent recognition, and alignment with personal values could matter more than another improvement in math or coding benchmarks.
The most capable agent will have limited value if people do not trust it with their inboxes, calendars, payments, and personal data.
The early evidence suggests that Meta’s approach is attracting interest.
Muse recorded more than 2.5 million downloads shortly after launch, while one estimate found that more than 95% of its users also used Facebook and 63% used Instagram.
That demonstrates Meta’s biggest advantage in AI.
The company does not need to build distribution from scratch because it can introduce Muse to an enormous existing network and eventually connect it with WhatsApp, Instagram, Facebook, and its hardware.
The AI race may have started with model intelligence but Zuckerberg is betting that it will ultimately be decided by trust, distribution, and the ability to complete real tasks safely.
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America has a massive lead in AI compute but China is exposing a weakness in that advantage owning more chips matters a bit less if your competitor can generate intelligence at a fraction of the cost. The United States controls nearly three quarters of global AI computing power compared with just over 14% for China yet cheaper Chinese models already starting to dominate OpenRouter’s rankings by tokens processed.
This creates an uncomfortable possibility. China could produce more usable AI output without matching the United States chip for chip, simply because its models generate more tokens from every dollar spent on compute. If that efficiency continues improving while China adds more data centers and domestic chips, America’s compute lead could become less valuable than it appears.
The most important measure may no longer be how much compute a country owns but how much intelligence that compute can produce. Because Chinese labs have limited access to the most advanced chips, they have been forced to make their models more efficient. They are using techniques that activate only the parts of a model needed for each task, reducing the processing power and memory required to generate every token. Yes, obviously they are using distillation as well but all that 'efficiency' leads directly to lower prices. Chinese open models can be 60% to 90% cheaper than leading American alternatives, while still delivering strong enough performance for many everyday workloads.
Token cost becomes even more important as AI moves from simple chatbots to agents. Each step consumes more tokens, so even a small difference in token price can become a major expense when companies run millions of AI tasks. This gives cheaper Chinese models a major advantage. If a model costs one fifth or one tenth as much, developers can let it work longer, attempt tasks multiple times and serve far more users for the same budget. On OpenRouter, Chinese models have been priced 60% to 90% below leading American models, helping their share of US enterprise token usage rise as high as 46% by mid-2026.
This is where the compute race and the token price race begin to connect. Lower prices create more usage. More usage keeps China’s chips and data centers busy, brings in revenue and gives developers more feedback to improve their models. That demand can then support additional investment in chips, power, networking and data centers. The cycle can quickly reinforce itself. Better efficiency lowers token prices, lower prices attract more users, and more users create the demand needed to build more compute. China may start with far less computing power but it can use lower prices to grow token volume faster and then reinvest that growth into expanding its infrastructure.
American AI companies face the opposite pressure. They are spending enormous amounts on GPUs, electricity and data centers, but competition keeps pushing token prices lower. If they must match Chinese prices while carrying much higher development and infrastructure costs, their profit margins could shrink and it could become harder to earn a return on their massive AI investments. This does not mean China will soon own more advanced chips than the United States. America still has a major advantage in semiconductors, cloud infrastructure and total computing capacity. The larger risk is that China wins the economics of compute first by producing more affordable tokens from every chip it has. If that happens, China will not need to beat America chip for chip. It could close the gap by combining more efficient models, cheaper tokens and a steadily expanding compute base. The winner of the AI race may not be the country with the most GPUs but the country that turns those GPUs into the greatest amount of affordable intelligence.
I’m positioning around both sides of this trade because cheaper models do not reduce the need for infrastructure, they can actually drive even more usage and compute demand. If you want to see exactly what I hold to capture that value across chips, memory, neoclouds, and the broader AI stack, check out my portfolio below.
link.milkroad.com/d8cj6v
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Melvin retweeted
Jensen Huang just made one of the most controversial arguments in AI that slowing development could make the technology less safe.
His position is that AI safety is fundamentally an engineering problem that requires better technology, more computing power and more rigorous testing to solve.
In other words, the answer to unsafe AI is not freezing today’s models in place but building stronger systems around them that can monitor behavior, verify outputs, restrict access, and intervene before something goes wrong.
There is already evidence supporting part of that argument.
Techniques such as reinforcement learning from human feedback can steer model behavior, while retrieval augmented generation can ground answers in approved information rather than relying entirely on the model’s memory.
More capable safety models could eventually monitor other AI systems in real time, detect suspicious behavior and stop agents from completing harmful actions.
This becomes increasingly important as AI moves beyond answering questions and starts sending emails, writing code, accessing databases, and operating software independently.
However, the same progress that improves safety also creates more capable systems that are harder to contain.
That tension became clear when OpenAI agents reportedly escaped a restricted testing environment, reached the open internet, and accessed Hugging Face’s infrastructure during a cybersecurity evaluation.
The incident was not simply evidence that AI had become uncontrollable but it exposed failures in sandboxing, network isolation, access controls, and monitoring, the exact engineering systems designed to contain the agents.
That supports Huang’s argument that many near term AI risks resemble traditional cybersecurity failures, only with faster and more autonomous software operating inside the system.
The solution requires isolated testing environments, strict permission limits, adversarial evaluations, continuous monitoring, human approval for sensitive actions, and immediate shutdown mechanisms.
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China started an AI price war America is not be ready for.
Its models are drastically cheaper, forcing enterprises to choose between US security and Chinese economics.
But here are five companies positioned to win.
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$AVGO is the American cost reduction play.
It helps companies such as Meta and OpenAI build custom accelerators optimized for their specific workloads, potentially lowering inference costs versus general-purpose hardware.
As Chinese pricing pressures American AI companies, demand for cheaper custom silicon will accelerate.
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$NET allows developers to run lower cost open models across its global edge network.
Workers AI provides serverless access to more than 50 models, letting customers pay only for the inference they actually use.
Cloudflare benefits whether the winning model comes from China or America because it can become the neutral distribution layer.
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Melvin retweeted
Hyperscaler capital spending is not declining but only the percentage growth rate is expected to slow after an extraordinary surge (Save this).
Spending across the five largest hyperscalers rises from roughly $150 billion in 2023 to $800 billion in 2026, before reaching an estimated $1.2 trillion in 2027 and $1.4 trillion in 2028. That represents more than a ninefold increase in five years. The additional $200 billion projected for 2028 alone is almost as much as the hyperscalers spent during all of 2024. The $1.2 trillion expected in 2027 is also greater than their combined spending from 2023 through 2025. This does not look like the end of the AI infrastructure cycle but rather looks like the point where an experimental buildout becomes an industrial economy.
Goldman Sachs estimates that roughly $7.6 trillion could be invested across AI compute, data centers, and power infrastructure between 2026 and 2031. The first phase primarily rewarded the companies selling GPUs. The next phase should spread more of that spending into memory, networking, optical components, cooling, electrical equipment, energy storage, construction, and power generation. That shift is becoming necessary because the bottleneck is no longer just producing enough chips but rather finding enough power and physical infrastructure to operate them.
The IEA expects global data center electricity consumption to roughly double between 2025 and 2030, while electricity demand from AI-focused facilities is projected to triple. The winners of this phase will be the companies sitting directly in front of the physical bottlenecks that $1.4 trillion of annual hyperscaler spending cannot avoid. That means memory, networking, optical components, cooling, electrical equipment, energy storage, power generation and the hyperscalers that can turn all of this infrastructure spending into actual earnings. Between now and when this buildout fully matures will be one of the best periods to make money in the market. If you want to see exactly what I hold to capture that value across AI infrastructure, you can check out my full Milk Road Pro portfolio using the link below.
link.milkroad.com/d8cj6v
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Melvin retweeted
Copper may be the most obvious trade nobody is positioned for.
AI, power grids, EVs and robots are pushing demand higher while new supply struggles to keep up.
Here are five copper stocks positioned to win.
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This is the reason why Nebius will continue to go even higher.
Nebius is generating increasingly more contracted annual revenue from every gigawatt of power capacity it builds. This chart shows annual recurring revenue per gigawatt rising from approximately $6 billion in the third quarter of 2025 to $12 billion in the second quarter of 2026. BNP Paribas estimates that Nebius new second quarter contracts reached approximately $20 billion of ARR per gigawatt, while short term third quarter contracts could reach $40 billion per gigawatt when that capacity is delivered in 2027. That would be more than six times the $6 billion level recorded in 2025. Now that does not mean Nebius is already generating $40 billion in annual revenue but it does mean that each future gigawatt of infrastructure could support much more contracted revenue than before.
The increase ie because of stronger demand for newer Nvidia systems, larger customer contracts and premium pricing for reliable managed AI infrastructure. Nebius has also raised its standard GPU prices, increased Token Factory inference pricing and introduced dynamic spot pricing for spare capacity. Together, these moves allow Nebius to charge more for guaranteed capacity and that can increase revenue per GPU. Higher revenue per gigawatt can dramatically improve data center economics. The cost of land, power connections, buildings and cooling does not necessarily increase as quickly as contract revenue, so a larger portion of the additional revenue could eventually flow into gross profit and operating income.
Long term contracts also give Nebius better revenue visibility before the capacity becomes operational. That can help the company secure financing, purchase additional GPUs and build more data centers because lenders can see contracted demand supporting the investment. The potential flywheel is pretty straightforward, Nebius signs larger contracts, those contracts unlock financing, the financing funds more capacity and the additional capacity allows Nebius to sign even larger customers.
Bullish on Nebius and make sure to follow @MelvinInvests for more insights.
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I’m an analyst at Milk Road, and my job is to find underrated gems before the market catches on.
We called names like MU, CRDO, NBIS, and BE over the last 3 months.
Join me and my team for just $1.milkroad.com/pro/?utm_medium…
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Copper may be the most obvious trade nobody is positioned for.
AI, power grids, EVs and robots are pushing demand higher while new supply struggles to keep up.
Here are five copper stocks positioned to win.
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I’m an analyst at Milk Road, and my job is to find underrated gems before the market catches on.
We called names like MU, CRDO, NBIS, and BE over the last 3 months.
Join me and my team for just $1.milkroad.com/pro/?utm_medium…
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Anthropic just signed a massive $11.6B deal with Akamai largely for CPU infrastructure.
And this chart shows exactly why.
JP Morgan estimates the GPU to CPU ratio falls from 4.0x in 2024 to just 1.9x by 2028, meaning more CPU capacity will be needed for every unit of GPU compute.
As agents handle more tasks and run more workflows, CPU demand should keep climbing.
Read below to see which stocks could benefit the most from this CPU demand.
Meta’s Muse agents are about to ignite the next CPU boom (Save this).
The more tasks these agents complete, the more CPU power Meta will need to run them.
Here are the five stocks positioned to win from this.
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Melvin retweeted
Bill Ackman made the perfect bull case for Meta months before even Muse arrived.
At the time, the market looked at Meta’s massive AI spending and assumed Mark Zuckerberg was lighting money on fire.
However, @BillAckman argued that investors were asking the wrong question.
Investors should not focus only on how much Meta was spending but rather on why it was spending.
If Meta had doubled its capital expenditures simply to protect its existing business, then the stock deserved to fall.
But if the company was investing in AI infrastructure that could create new products and generate attractive returns, the spending was growth capex rather than maintenance capex.
But now Muse made that argument much easier to understand.
Meta used its AI infrastructure to launch a personal agent that could send emails, book travel, make purchases, and complete other tasks through its own app or WhatsApp.
Instead of building distribution from scratch, Meta could place Muse directly inside an ecosystem already used by billions of people.
Muse also introduced paid subscription tiers, giving Meta another potential revenue stream beyond advertising.
Muse may only be the beginning because if Meta can keep turning AI infrastructure into new products, that massive capex bill starts looking a lot more like an investment than an expense.
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HOLY SMOKES!
Higgsfield just reached $1 billion annual revenue run rate faster than Anthropic and OpenAI and this may be one of the strongest bull signals yet for Nebius.
Because nebius is helping provide the infrastructure supporting that growth.
Higgsfield used Nebius AI Cloud and NVIDIA HGX B200 systems to train its large scale image and video diffusion models.
Nebius also helped create the storage, networking and data pipelines needed to keep Higgsfield’s GPUs operating efficiently.
When Higgsfield had a $200 million revenue run rate, it already had more than 15 million users and was processing over 4.5 million image and video generations every day.
The amount of infrastructure required at a reported $1 billion run rate could be enormous.
Revenue and compute usage will not necessarily increase at the same rate, but more paying customers, larger enterprise accounts and higher generation volumes should create greater demand for GPUs, storage and networking.
AI video is especially compute intensive because every request can require the system to generate hundreds or thousands of frames and users also create several versions, make edits and rerun prompts before selecting a final result.
Every one of those actions consumes additional computing capacity.
Higgsfield also needs large GPU clusters to train new models, improve video quality and run repeated preference training cycles.
Nebius can therefore benefit from Higgsfield’s major training runs and its recurring inference usage.
Higgsfield is exactly the kind of customer Nebius wants because its infrastructure needs can keep expanding without Nebius having to find a new buyer for every additional GPU it installs.
The relationship also gives Nebius a powerful reference customer because it shows other AI startups that Nebius can support a highly demanding video platform as it scales from zero to millions of users and a reported $1 billion run rate.
I hold Nebius because I want exposure to the infrastructure underneath companies like Higgsfield as their usage explodes.
If you want to see how I’m positioned around Nebius, neoclouds, and the broader compute trade, check out my Milk Road portfolio below.
link.milkroad.com/tx5qip
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Melvin retweeted
Everyone thinks data center moratoriums are killing the AI boom but the actual numbers tell a different story.
More than 300 local governments have adopted data center restrictions across the United States, with new moratoriums accelerating sharply during 2026.
A moratorium usually pauses new applications while officials review concerns involving electricity, water, noise and land use.
At first glance, hundreds of restrictions sound like a major threat to the AI infrastructure buildout.
However, SemiAnalysis estimates that only 1,525 megawatts is actually delayed out of roughly 20 gigawatts located inside restricted areas.
Including New York’s statewide action raises the total meaningful delay to approximately 2.3 gigawatts.
Most moratoriums cannot stop projects that already have permits, zoning approval or construction underway.
Many restrictions will also expire before future projects need approval, while developers can sometimes relocate, redesign their campuses or build on site power systems.
SemiAnalysis still expects more than 38 gigawatts of US data center capacity to be delivered in 2027, more than double the 2026 level.
Approximately 22 gigawatts is already under vertical construction, while much of the remaining capacity is financed or undergoing site preparation.
The political backlash is real but the money, equipment and construction are already moving.
These restrictions could actually make approved land and secured power connections more valuable because developers with permits can continue building while competitors remain stuck.
Moratoriums could also accelerate behind the meter power, including natural gas generation, fuel cells, batteries and microgrids.
Build out still continues and it's not going to be stopped by anyone.
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Melvin retweeted
This is the reason why Nebius will continue to go even higher.
Nebius is generating increasingly more contracted annual revenue from every gigawatt of power capacity it builds. This chart shows annual recurring revenue per gigawatt rising from approximately $6 billion in the third quarter of 2025 to $12 billion in the second quarter of 2026. BNP Paribas estimates that Nebius new second quarter contracts reached approximately $20 billion of ARR per gigawatt, while short term third quarter contracts could reach $40 billion per gigawatt when that capacity is delivered in 2027. That would be more than six times the $6 billion level recorded in 2025. Now that does not mean Nebius is already generating $40 billion in annual revenue but it does mean that each future gigawatt of infrastructure could support much more contracted revenue than before.
The increase ie because of stronger demand for newer Nvidia systems, larger customer contracts and premium pricing for reliable managed AI infrastructure. Nebius has also raised its standard GPU prices, increased Token Factory inference pricing and introduced dynamic spot pricing for spare capacity. Together, these moves allow Nebius to charge more for guaranteed capacity and that can increase revenue per GPU. Higher revenue per gigawatt can dramatically improve data center economics. The cost of land, power connections, buildings and cooling does not necessarily increase as quickly as contract revenue, so a larger portion of the additional revenue could eventually flow into gross profit and operating income.
Long term contracts also give Nebius better revenue visibility before the capacity becomes operational. That can help the company secure financing, purchase additional GPUs and build more data centers because lenders can see contracted demand supporting the investment. The potential flywheel is pretty straightforward, Nebius signs larger contracts, those contracts unlock financing, the financing funds more capacity and the additional capacity allows Nebius to sign even larger customers.
Bullish on Nebius and make sure to follow @MelvinInvests for more insights.
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