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Inside Nvidia’s $4B Optical Strategy—and Why CPO Changes Everything
Within the AI investment theme, there is nowhere that the supply chain shifts faster than in networking, leading companies to gain content on new platforms or lose incremental share. The reason is straightforward: much of the market is tied to a single customer, Nvidia; and Nvidia is rolling out new architectural iterations at an unusually fast pace. When it comes to networking, two of the most important architectural advancements are the increase in pod and cluster sizes and the transition to 200G per lane.
Is Nvidia Stock a Buy? Why Semiconductor Strength May Signal a Market Top
In this report, we take a deeper look at the technical scenarios, which suggests that Nvidia’s latest high is shaping up to be a potential bull trap. That view is corroborated by the broader semiconductor complex. Specifically, the failure of other key sub-sectors to confirm the move higher.
Nvidia’s $20 Trillion Thesis Is Intact. My 2026 Allocation Isn't
The thesis on Nvidia's hardware moat has played out exceptionally well, but that also highlights one of the biggest risks investors face, which is becoming emotionally attached to a winning stock. While I still believe Nvidia will reach $20 trillion by 2030, I believe much of that 310% return is likely to be back-half weighted in the years of 2028-2030. This is what separates investors from AI enthusiasts. While an AI enthusiast can sit back, relax and discuss specifications and other fandom, an investor must always answer — is my capital better deployed elsewhere?
Bitcoin 2026 Price Prediction: Why the Dollar, Global Liquidity and Volume Signal More Downside Ahead
In our last Bitcoin analysis, "Bitcoin After the Cycle Peak: What Comes Next and How We're Positioning", we argued that Bitcoin was closer to a cycle low than most believed, even if one final drop remained ahead. Since that publication, the probability of another drop occurring in the coming weeks has increased meaningfully. If it does, it should set up a tradeable bounce within what we believe is an ongoing bear market. What gives us the confidence that we are in a new bear market cycle, rather than a pullback within a larger uptrend, is that Bitcoin continues to track sentiment patterns and global liquidity cycles with remarkable consistency. Recognizing this unconventional correlation has been the foundation of a framework that has filtered out narrative-driven noise and kept us on the right side of every major Bitcoin trend since 2020. These are themes that we first introduced in August of 2025, when Bitcoin was trading at around $115,000.
2026 Stock Market Outlook: Cycle Convergence & What's Next
In our last broad market update, the S&P 500 was trading near 6,850, grinding through its fifth consecutive month of going nowhere. I drew a clear line in the sand at the 6,780 level. This was where the bulls needed to hold to keep the broader uptrend intact heading into 2026. This level (SPX6780) remains of utmost importance for the bulls. If it breaks, then the period of volatility has already begun as we head toward 6500 – 6300 in the coming weeks. This will likely complete the first leg in a larger correction, as we mount a bounce that makes a lower high into later 2026. That level clearly broke, dropping the broad market over 400 points, and finally bottoming at 6,316 on March 30th. We are now staging a bounce, which our analysis suggests will likely fail to make new highs, as the period of volatility that we have been flagging since November takes accelerates.
Arm Stock Could Win as Agentic AI Shifts the Bottleneck to CPUs
Arm unveiled an AGI CPU to address one of AI’s biggest bottlenecks, which is orchestration. During the chatbot craze of 2023-2025, GPUs did most of the heavy lifting while CPUs had become an afterthought. Yet with agentic workloads, which is perhaps the single largest catalyst on the horizon for the AI trade in 2026 and beyond, the importance of CPUs is set to increase. Regarding how Arm fits in, the company’s expertise in lowering power requirements could matter more than the market expects. After years of supplying the architecture IP behind other companies’ CPUs, Arm is preparing to directly compete with its customers and x86 CPU competitors by transitioning to a chip designer themselves. This comes during a time when CPU cores are expected to go up 4X from 30 million CPU cores per gigawatt to 120 million CPU cores per GW. Below, we explain why Arm may be joining a group of select, core AI names positioned to deliver over the long haul.
Nvidia Stock Prediction: The Path to a $20 Trillion Market Cap is Strengthening
The $20 trillion market cap will not come from GPU unit growth alone, though unit growth remains very important. Rather, the value proposition will increasingly focus on economic output. This marks a tremendous shift for how Nvidia is evaluated. As the AI market shifts toward inference, Nvidia’s product cycles will be optimized around token economics such as throughput, latency, power efficiency and cost per token. The goal is no longer to simply sell faster and more powerful chips, but to deliver superior economic value at the system level relative to custom silicon (in other words, let the battle begin). Leading up to this, Nvidia was competing on performance metrics, and MLPerf benchmarks still matter of course. But going forward, workload economics and system-level efficiency will play a much larger role in how their systems are evaluated.
Nvidia Stock to See New Growth Catalyst; 35X Faster AI with Groq 3 LPX
At GTC this week, Jensen Huang stated the revenue opportunity for Nvidia’s artificial intelligence chips may reach at least $1 trillion through 2027, up from a previous target of $500 billion. While that grabbed most of the headlines, there was another jaw-dropping statistic that will set the stage in the coming years - which was the ability to drive up to 35X higher throughput per megawatt with its new Groq 3 LPX racks. The Groq acquisition is aimed to solve a different limiter, which inference throughput per watt, where memory bandwidth can become the gating factor to token output and cost. Nvidia is preparing to position its GPUs to be among the best inference options available, utilizing Groq’s unique SRAM-based architecture to significantly turbocharge token throughput and accelerate inference performance.
