The Thesis

Samsung’s preliminary Q2 2026 results confirm the memory supercycle thesis in full: operating profit of roughly 89.4 trillion won (about $58.4 billion) surged 19-fold year-over-year and beat consensus estimates of around 86 trillion won by roughly 6%. Revenue more than doubled year-over-year to 171 trillion won. On operating income, Samsung has now posted the highest quarterly profit ever recorded by a technology company, ahead of Nvidia’s most recent quarter. And yet Samsung shares fell as much as 6.8% in Seoul on the news — a reminder that in this cycle, “beat” and “priced in” are not the same thing.

TeraWulf signed a 20-year lease with Anthropic for its Justified Data campus in Hawesville, Kentucky, a deal expected to generate approximately $19 billion in contracted revenue over the initial term. The announcement sent TeraWulf shares up more than 17% on the day — a striking outcome for a company that started life as a Bitcoin miner.

The scale of the campus. Justified Data is expected to support roughly 401 megawatts of critical IT load once fully built. Initial capacity comes online in the second half of 2027, with the site reaching full capacity by early 2028. That timeline matters: this is a multi-year commitment, not a near-term revenue bump, and the $19 billion figure averages out to roughly $950 million a year across the lease term, with actual cash flows depending on phased delivery and escalation terms.

The memory trade finally blinked. Micron and SanDisk each fell roughly 10.6% on Wednesday, July 1, with Western Digital and Seagate dropping 6.3% and 5.2%. Thursday brought a second leg down: SanDisk lost another 11%, Seagate 7%, Micron 4%. The Roundhill Memory ETF (DRAM) — the cleanest sector proxy, launched only in April — shed nearly 11% Wednesday and another 5% Thursday. The Philadelphia Semiconductor Index posted a 7.9% weekly decline, its worst since April 2025.

Micron broke ground this week on a roughly $9 billion HBM fab inside its existing Hiroshima campus, with first shipments targeted for the summer of 2028. Strip away the ribbon-cutting and the strategic logic is genuinely sound: HBM is the most constrained component in the AI supply chain, Micron is the number-three player trying to close the gap on SK Hynix and Samsung, and the Japanese government is covering a large slice of the bill. Every part of that is defensible. The problem isn’t the decision — it’s the arrival date. This capacity lands in 2028, which is precisely the year the supply-glut argument that drove this week’s memory selloff says the cycle rolls over. The same event is the bull’s bottleneck-reliever and the bear’s Exhibit A, and which one it becomes won’t be knowable for two years.

Palantir closed July 1 at $125.73, up 7.8% on the day and adding roughly $21.7 billion in market value on unusually heavy volume of 57 million shares. The move capped a nine-day run of catalysts, but the headline driver was Alex Karp’s appearance on CNBC’s “Squawk Box,” where the CEO combined a genuine product announcement with a public airing of grievances against the rest of the AI industry.

The Announcement Underneath The Noise

The interview was nominally about Palantir’s expanded partnership with Nvidia: an “intelligent engine” that runs Nvidia’s Nemotron open models inside sovereign, secured environments for government agencies and critical infrastructure operators. The pitch is data and model-weight control — customers keep ownership of their compute and their data stack rather than routing it through a third-party API.

Samsung and SK Hynix unveiled a combined roughly $1.3 trillion (2,000 trillion won) decade-long investment plan for new fabs, AI data centers, and chip cluster development. Both stocks fell anyway — Samsung down over 5%, SK Hynix down over 3% on the announcement day, following an even sharper 9%+ plunge earlier in the week. The knee-jerk read: investors think the spending is reckless, a repeat of the 2018-2019 memory bust, or proof the AI trade is cracking.

On June 2, Jensen Huang turned to Matt Murphy on a Computex stage in Taipei and called Marvell the next trillion-dollar company. The stock rose 32.52% that day, its largest single-session gain on record, adding roughly fifty billion dollars in market value before the close. Every desk on the Street ran the clip. Almost none of them ran the number underneath it.

The number was disclosed six days earlier, on the May 27 fiscal first-quarter call, and it carried no theater at all. Marvell raised its forward guide by roughly five billion dollars and lifted interconnect growth from 50% to over 70% year over year. The trillion-dollar line moved the tape. The guide raise moved the thesis. Those are not the same event, and conflating them is how investors end up paying for sentiment while telling themselves they bought fundamentals.

When Jensen Huang stood onstage with Matt Murphy at Computex in Taipei and called Marvell the next trillion-dollar company, the market did not treat it as a courtesy. The stock posted its largest single-day gain on record, jumping more than thirty percent the following session. Endorsements from rivals are usually cheap. This one was not, because Huang was not flattering a partner. He was describing the part of the AI buildout he understands better than anyone, and naming the company that owns it.

CXL was sold as a capacity story: extend the memory pool past the DIMM slots soldered to the motherboard. Marvell’s argument with Structera is sharper than that. The pool itself is half-empty. The data sitting in DRAM is compressible, almost no CXL controller touches it, and Structera does — in dedicated silicon, at line rate, invisible to the host.

The number circulating is 3.64x, the top of the range Marvell cites for mixed real-world data types, which it claims match or closely approach what host-side LZ4 achieves in software. Field reporting has been more conservative; ServeTheHome quoted Marvell putting practical ratios at 1.8x to 2x. Both numbers point the same way. Even a flat 2:1 halves the effective cost per gigabyte of a memory pool, and memory is the single largest line item in that pool.

The headline says OpenAI is leaning toward delaying its IPO to 2027. The price action says something narrower: the most leveraged claim on that listing just repriced. SoftBank fell as much as 13% in Tokyo, the worst session since August 2024, while OpenAI’s own business did not change at all.

That gap between the news and the reaction is the entire story. This was never a fundamentals event. OpenAI filed a confidential S-1 on June 8, revenue is still growing, and the company told regulators it had not settled on timing. What moved was the calendar, and the calendar is the only thing SoftBank shareholders were actually long.

The Wall Street Journal headline frames the memory shortage as a problem to be solved. It isn’t. The more accurate reading of the supply picture is that the crunch is the predictable output of a fixed production base being reallocated toward AI, and there is no near-term lever — industrial or political — that changes that math before 2027. For the three companies that own the supply, that is not a crisis. It is the most durable pricing-power setup the industry has seen in a generation.

There is one argument running underneath every chip-stock target reset this week, and it is not really about chips. It is about whether memory, storage, and the wires between accelerators are commodity components that move on the old PC-and-mobile cycle, or mission-critical AI infrastructure whose demand scales with every model upgrade, every reasoning capability, and every agentic deployment.

Bank of America just answered that question with its wallet. On June 23 — a day the group was getting hit, not bid — Vivek Arya raised Micron to $1,500 from $950 and reframed DRAM and high-bandwidth memory as structural AI infrastructure rather than a cyclical good. The same desk lifted Marvell to $365 the same session and circulated a note arguing the broader memory-plus-interconnect complex represents another trillion-dollar opportunity for chip names. That is the tell. When one analyst makes the identical structural call across DRAM, NAND, and custom silicon on a down day, it is not a price target. It is a thesis.

The dominant worry about the AI buildout is a timing mismatch: roughly $700 billion of hyperscaler capital expenditure committed in 2026, against application revenues that critics call nascent. The bear frames this as a financing problem waiting to happen. The bull case is narrower and more mechanical, and it is worth stating in its strongest form: the capex curve and the revenue curve are shaped to cross, and the crossover is arriving now rather than at the end of the decade.

The memory industry spent thirty years teaching investors one lesson: never believe “this time is different.” Boom, over-invest, glut, collapse. Price the top early, because the top always comes. That instinct is now the most expensive mistake in semiconductors. The DRAM and NAND supercycle that began in 2024 is not late-cycle. It is early. And the mechanism that has ended every prior memory cycle has been disabled.

The demand is structural, not cyclical

Start with the numbers, because they are not subtle. IDC puts DRAM revenue at $418.6 billion in 2026, up roughly 177 percent year over year, with total memory rising from $226 billion in 2025 to $594.7 billion in 2026 and $790.4 billion in 2027. Bank of America frames the period as a supercycle on the scale of the 1990s boom, with DRAM revenue up 51 percent and NAND up 45 percent. Contract prices through early 2026 rose 90 to 95 percent quarter over quarter. DDR5 spot prices quadrupled from September 2025. Supplier inventories sit at two to four weeks.

The demand-side case for the AI buildout rests on token consumption going vertical: agentic workflows firing 10 to 20 inference calls per task, enterprise API volumes measured in billions of tokens per minute, hyperscaler revenue compounding faster than capex. That argument has a physical counterpart that rarely gets stated in the same breath. Every one of those tokens is a memory access. The token-growth curve is not an abstraction floating above the supply chain — it is the buyer standing on the other side of the DRAM and HBM order book.

Noam Mizrahi has been saying the same thing for two years, and the market has only just decided to believe him. Marvell’s corporate CTO, based at the company’s Israeli site and a Technion graduate, has argued since the early innings of the AI build-out that the constraint on the next leap was never going to be the processor. It was going to be the wire between the processors — and then the optics, when copper ran out of reach. The industry called this a backwater. Marvell bet the company on it. The bet has now compounded into one of the most violent re-ratings the semiconductor tape has produced this cycle.

The instinct after a move like this is to call the top. SanDisk has gained more than 4,400% over the past year. Micron has added roughly 810%. Both trade within a few dollars of their 52-week highs. Every rule of thumb says a chart like that is closer to its end than its beginning. The rules of thumb are wrong here, and the reason is structural, not technical.

Apple Just Confirmed the Thesis

This week Tim Cook told the Wall Street Journal that price increases across Apple’s lineup are unavoidable, and he named memory as the cause. The September iPhone 18 Pro is expected to carry the first higher sticker price, with TechInsights estimating that preserving Apple’s margin would require adding roughly $270 to the starting price. The market read Apple shares as a wash. It read the memory names as a green light.

KeyBanc’s John Vinh raised his Marvell price target to $385 from $260 on Thursday, a 48% increase, and kept his Overweight rating. The headline number is large. The argument behind it is more interesting, because it inverts the hierarchy that has carried the stock for two years.

For most of the AI cycle, Marvell has been priced as a custom-silicon story. The thesis was the XPU pipeline: bespoke accelerators for AWS and Microsoft, a clear line of sight to roughly $10 billion in custom-chip revenue by fiscal 2029, and a seat at the table next to Broadcom in the ASIC duopoly. Networking was the supporting act. Vinh has now promoted it to lead. He came out of recent investor meetings calling networking the most durable growth opportunity Marvell has, and put a number on the scale-up market — optical links, silicon photonics, and high-speed switching — of around $30 billion by 2030.

When Jensen Huang stood on the Computex stage and called Marvell a potential trillion-dollar company, it sounded like a courtesy extended to a new partner. It is not. It is a forecast with a visible arithmetic spine. Marvell closed near $325 in mid-June carrying a market capitalization around $272 billion. A trillion dollars is roughly 3.7 times that. The question is not whether the path exists. It exists, it is mapped, and Marvell’s own guidance lays most of the mileposts. The question is how many years it takes and what has to hold along the way.

The headline number is clean and the headline framing is wrong. Nvidia did not buy $2 billion of Marvell stock in the market. On March 31, 2026, it purchased two million shares of newly issued Series A Convertible Preferred Stock at a stated value of $1,000 each, a private placement that put $2 billion of fresh cash directly onto Marvell’s balance sheet. That distinction is the entire story. Nvidia did not become a passive holder of MRVL. It became a senior, structured creditor-equity hybrid with a conversion option struck deep below where the stock now trades, and it did so as the price of admission to a partnership designed to neutralize the single largest threat to its own franchise.

Lumentum (NASDAQ: LITE) and Coherent (NYSE: COHR) are the two Western names every AI-optics conversation eventually circles back to. Both have been pulled out of telecom-cyclical obscurity and re-rated into large-caps by the same force: NVIDIA’s data-center buildout and its need to move colossal amounts of data between accelerators with optics instead of copper. Both took a $2 billion equity investment from NVIDIA in March 2026, the identical capital-plus-purchase-commitment template, on the same day the company committed to locking in its photonic supply chain.

SanDisk’s move from a $38.50 spinoff price to roughly $1,980 — about 5,000 percent in sixteen months — is not one rally but two stories stacked on top of each other, and the market is pricing them as if they were the same thing. Separating them is the only way to understand where the stock can go.

The Thesis

The first story is real and measurable: a NAND flash supply squeeze. AI inference has turned high-capacity flash into a constrained resource. Average selling prices per gigabyte are climbing, exabytes shipped are rising, and SanDisk has converted both into record revenue and a fiscal-2026 trajectory that Bank of America models at 176 percent growth. That is a cyclical earnings boom with unusually firm footing, anchored by multi-year contracts — five signed, three of them carrying $42 billion in minimum revenue and more than $11 billion in financial guarantees — structured so margins hold even at the price floor. This is the opposite of spot-commodity NAND, and it is what the bulls point to first.

The instinct to compare SanDisk and Kioxia is correct, but the framing usually is not. These are not two competing bets. They are one bet, expressed twice — and the wiring that connects them runs through the same factory floor.

The Thesis

SanDisk and Kioxia are both pure-play NAND flash manufacturers riding the same AI-inference storage squeeze. Both have re-rated into the mega-cap tier — roughly $293 billion for SanDisk, roughly $260 billion for Kioxia — and both are wagering that flash can climb the AI memory hierarchy and shed its commodity discount. The decisive fact is that they share the physical means of production: the Yokkaichi and Kitakami fabs that stamp out their NAND are a single joint venture, recently extended through 2034. When one company describes its market, it is describing the other’s. The useful question is therefore not which company wins, but which is the cleaner expression of an identical trade — and where the two diverge enough to matter.

SanDisk is the reference point that started this. After spinning out of Western Digital in early 2025, the stock bottomed near forty dollars in April of that year and now trades close to nineteen hundred — a forty-five-fold move accomplished in roughly twelve months. It is the kind of chart that sends investors hunting for the next one. But the lesson of SanDisk is easy to misread. It did not climb because of a proprietary technology nobody else had. It climbed because NAND flash entered a brutal undersupply, pricing inflected, and a newly independent company captured the entire swing. That is a commodity supercycle, not a moat. Memory re-rated because the physics of supply and demand turned, and the same mechanism will eventually turn the other way.

Jensen Huang confirmed at GTC Taipei 2026 on June 1 that all three major memory manufacturers — Samsung Electronics, SK Hynix, and Micron Technology — have been qualified and are already in production as HBM4 suppliers for Nvidia’s Vera Rubin AI platform. The announcement ended months of supply-chain speculation and, for Micron, reversed a narrative that had pressured the stock since March.

The qualification matters beyond the supplier list. Vera Rubin is Nvidia’s next-generation AI infrastructure platform, combining Vera CPUs with Rubin GPUs and large HBM4 memory stacks per server. It is in full production and scheduled to begin shipping in Q3 2026. HBM4 doubles the interface width of its predecessor, with the JEDEC standard supporting up to 2 TB/s per stack on a 2,048-bit bus versus approximately 1 TB/s for HBM3E. Every major hyperscaler ordering Vera Rubin systems will depend on this memory supply chain holding.

Qualcomm has spent the better part of two years trying to convince the market it is something other than a smartphone modem company with a licensing book. As of June 2026, the market has decided to believe it — the stock is up roughly 62% in a single month and sits near $250, an all-time high. The harder question is whether the business has changed as fast as the multiple has.

Thesis

The headline was a record: $2.418 billion in revenue, up 28% year-over-year, with $0.80 of non-GAAP earnings. The headline is not the story. The story is what management did to the out-year model. On the print it raised the fiscal 2028 revenue outlook toward $15 billion and the fiscal 2027 outlook to approach $11 billion, and it did so on bookings rather than hope, citing AI-related order momentum it called exceptional. Marvell is no longer a diversified chip vendor with an AI option bolted on. It is a custom-silicon and interconnect company whose addressable market is being rewritten by the hyperscaler decision to design proprietary accelerators and buy the connective tissue around them.

Meow Technologies is introducing banking services designed for AI agents. The announcement is easy to dismiss as a novelty. It should not be.

The premise is simple: AI agents that execute tasks autonomously will, in an increasing number of workflows, need to transact. Paying for API calls, purchasing data, settling micro-transactions, managing operational budgets — these are functions that autonomous systems need if they are to operate without constant human intervention at the payment layer. Meow is building the financial infrastructure for that pattern.

SiFive has raised $400 million to accelerate RISC-V-based data center solutions. The headline reads as another semiconductor funding round. The subtext is a bet on architectural decoupling at the infrastructure level.

RISC-V is an open instruction set architecture. Unlike x86 (Intel/AMD) or ARM (licensed through Arm Holdings), RISC-V carries no royalty obligation and no single corporate owner. Any organization can implement it, modify it, and deploy it without licensing exposure. For years this was an academic curiosity. It is no longer.

Turing has launched Turing Frontier, a platform that connects AI laboratories with domain experts for evaluation, fine-tuning, and validation work. The product category is modest. The structural position it occupies is not.

What Turing Frontier is building is the interface layer between AI systems and the specialized human judgment those systems cannot reliably replicate. This is not a novelty. Every serious AI deployment in high-stakes domains already has a version of this layer — it is just typically ad hoc, expensive to staff, and impossible to scale. Turing is betting it can systematize and productize that function.

Xoople has raised $130 million to build what it describes as a “system of record for the physical world.” That framing deserves more attention than the funding number.

A system of record is not a search tool. It is not a visualization layer. It is the authoritative source that other systems defer to — the tier of infrastructure that becomes load-bearing over time. Applying that concept to physical-world data means Xoople is not competing with satellite imagery vendors or GIS platforms. It is claiming the layer beneath them.

Table of Contents

1. What is a Vector Database?
2. Core Concepts
3. How Vector Search Works
4. Choosing a Vector Database
5. Getting Started
6. Embedding Models
7. Indexing & Storage
8. Querying & Filtering
9. RAG: Retrieval-Augmented Generation
10. Performance Tuning
11. Security Considerations
12. Real-World Examples

What is a Vector Database?

A vector database is a database optimized for storing and searching high-dimensional numerical vectors — called embeddings — that represent the semantic meaning of data (text, images, audio, etc.).