NVIDIA, Huawei, and Intel are racing for AI chip dominance in 2026, but a hidden shortage may decide who actually wins.
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In this update:
1. Beijing is reportedly set to approve Nvidia's H200 for a handful of Chinese firms, but the cap may land below 200,000 chips, less than half of what was requested.
2. The real limit on how many AI chips reach the market right now is not the chip itself. It is high-bandwidth memory and TSMC's CoWoS packaging, both sold out into 2027.
3. DeepSeek's own R2 model is the clearest public evidence that Huawei's Ascend chips can run inference well, but still cannot reliably finish a frontier training run.
Every big AI story eventually comes back to one thing: the physical chip that runs the math. Chatbots, self-driving software, and drug discovery models do not work without AI chips. In 2026, the fight over who makes them, who buys them, and who gets banned from buying them has turned into one of the biggest business and political stories of the year.
This piece breaks down what AI chips actually are, why Nvidia still leads the pack, why Huawei is closing the gap faster than most people expected, and why Intel keeps showing up to a fight it has not won yet. We also go past the headlines into the parts of this story that rarely get covered: the memory shortage that decides who can actually ship a chip, the workload questions that decide whether Huawei's hardware is the right call, and the allocation politics that decide who gets supply first. No hype, no guesses about the future. Just what is happening right now with sources?
A quick note on tone before we get into it. This market moves fast enough that a headline from March can look outdated by June, and much of the coverage treats every product announcement like it has already shipped. We are sticking to what companies have confirmed, what analysts have independently verified, and what executives have said on the record. If a number looks impressive, we will tell you whose number it is and how far it has been tested.
What is an AI chip, in plain terms
An AI chip is a processor built to handle the specific kind of math that machine learning needs: huge batches of matrix multiplication, done in parallel, over and over. Regular CPUs can do this math too, but they do it one step at a time. AI chips do thousands of steps at once, which is why training a model on a CPU can take months while the same job on the right AI chip takes days.
Most AI chips fall into two jobs. Training chips build the model, feeding it enormous datasets so it learns patterns. Inference chips run the finished model, answering a prompt or recommending a video. Training needs raw power. Inference needs speed and a low cost per query, since a company might run billions of queries a day. The AI models that actually use this hardware, including systems like Anthropic's newer Claude models, are themselves shaped by which chips their training runs land on.
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NVIDIA's GPUs dominate training. Intel, AMD, and a handful of Chinese firms are trying to carve out space in the inference market instead, because inference is cheaper to build for and the market keeps growing as more companies deploy AI instead of just testing it.
Size matters here, too. A modern AI chip is not one small piece of silicon. NVIDIA's Blackwell and Vera Rubin chips are built from multiple chiplets fused together, wrapped in cooling systems that can weigh more than the server rack around them. Huawei gets around its manufacturing limits the opposite way, linking thousands of smaller chips together with fast interconnects so the total system output competes even when the single chip does not. Both approaches solve the same problem from different directions, and which one wins probably depends more on cost per query than on any single benchmark.
Why AI chip news will not slow down this year
Three things are colliding at once. First, the AI accelerator market itself has become enormous. NVIDIA's own fiscal 2026 results show data center revenue of $193.7 billion for the year ended January 25, 2026, up 68% from the year before, out of total revenue of $215.9 billion. That is not a niche industry anymore. That is one of the largest revenue lines in global technology, and the growth kept going: in the quarter ended April 26, 2026, Nvidia's data center revenue reached $75.2 billion on its own, up 92% year over year, according to the company's first quarter filing.
Second, the US and China are locked in an export control fight that keeps changing week to week. Washington restricts what Nvidia and AMD can sell to China, then loosens the rules, then Beijing adds its own roadblocks on the other end. The restrictions started under President Biden in 2022 and were expanded by President Trump in 2025, before parts were reversed, allowing Nvidia to sell its H200 chip and AMD to sell its MI308 chip to China as of May 2026.
Third, China is no longer just buying chips. It is building its own, and the results are good enough to matter for at least one job in the AI pipeline. That is the part of this story that gets the least attention, and it is probably the most important one.
NVIDIA AI chips: still the biggest name, still the biggest target
NVIDIA's market share in AI accelerator GPUs sits at roughly 85 to 90 percent globally. For context, that is a level of dominance most industries never see from a single company. CEO Jensen Huang built this lead on two things: raw chip performance and CUDA, the software layer that lets developers use Nvidia hardware without starting from scratch every time.
The current flagship architecture is Vera Rubin, announced at CES 2026. It is built on TSMC's N3P 3 nanometer process with HBM4 memory and 336 billion transistors, and Nvidia says it cuts inference token generation costs by 10 times and reduces the GPU count needed for training mixture of experts models by 4 times compared to Blackwell, the generation before it. Those are Nvidia's own numbers, so treat them as a ceiling rather than a guarantee, but the direction is clear: each generation is meant to make AI cheaper to run, not just faster.
Here is the part that should worry Nvidia more than any single competitor chip: the China problem. Before export controls tightened, Nvidia held almost 95 percent of the advanced AI chip market in China, and the country made up about 13 percent of Nvidia's total revenue. That business has mostly evaporated, and Huang has said as much plainly in interviews.
NVIDIA vs. Huawei AI chips: the fight nobody thought Huawei could win this fast
In May 2026, Huang told CNBC that Nvidia has effectively given up on competing for China's advanced AI chip market against Huawei, even in the same stretch where Nvidia reported a quarter with revenue up 85 percent to $81.6 billion, according to its first quarter fiscal 2027 results. Think about that for a second. A company can be printing money globally and still admit it lost an entire country to a rival that barely existed in this market five years ago.
Huawei's answer to Nvidia is the Ascend line, built through its HiSilicon chip division. The current workhorse is the Ascend 910C, and Huawei is not being shy about scaling it up. According to Bloomberg reporting cited by RCR Wireless, the company plans to produce around 600,000 Ascend 910C units in 2026, nearly double the prior year's output, with total Ascend family production reaching up to 1.6 million dies once older models are included.
On raw performance, Huawei is still behind. The 910C is built on SMIC's enhanced 7-nanometer process, compared to the 4-nanometer TSMC node Nvidia uses for its B200, and delivers roughly one-third the BF16 throughput of Nvidia's B200. Huawei's answer to that gap is not a better single chip. It is more chips wired together. The newer Ascend 950PR entered mass production using SMIC's N+3 process, a 5-nanometer-class node, and packs 1.56 petaflops of FP4 compute, 112 GB of domestically made HBM memory, and a 2-terabyte-per-second interconnect into a single chip at a 600-watt envelope. That is a real jump from the 910 generation, and it happened faster than most Western analysts expected.
The revenue numbers back up the shift. Huawei's AI chip revenue is projected to hit $12 billion in 2026, up 60 percent from $7.5 billion the year before, and the company is on track to control 60 percent of China's AI chip market by the end of the year. Behind Huawei, smaller Chinese players like Cambricon, Moore Threads, and MetaX are grabbing whatever share is left. The total addressable market for AI accelerators inside China alone is expected to reach $30 to 35 billion in 2026.
Why the real bottleneck is not the chip. It is HBM memory and packaging
Almost every article about this market treats chip supply as a function of how many wafers TSMC can produce. That misses the layer of the story that actually decides who can ship hardware in 2026: high bandwidth memory, known as HBM, and TSMC's CoWoS advanced packaging process that fuses the memory onto the chip. Neither sits fully inside any single chipmaker's control, and both are sold out well past this year.
TSMC's own CEO, C.C. Wei, told shareholders at the company's June 2026 annual meeting that CoWoS capacity remains extremely tight and sold out through 2026. Multiple analyst estimates, including figures reported by DigiTimes and cited across Tom's Hardware and Morgan Stanley research notes, put Nvidia's booked share of TSMC's 2026 CoWoS capacity at roughly 800,000 to 850,000 wafers, close to 60 percent of the total. That allocation covers current Blackwell Ultra production and reserves capacity for the incoming Rubin architecture at the same time, which leaves a shrinking slice for everyone else, including Broadcom, AMD, and Google's custom TPU chips. Google reportedly had to cut its 2026 TPU production target from about 4 million to 3 million units for exactly this reason.
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Huawei's version of this problem is HBM itself rather than packaging. Analysis from the SemiAnalysis newsletter estimates that Chinese firms had stockpiled roughly 13 million HBM stacks from Samsung before export controls tightened, enough to support Ascend production through 2024 and 2025. China's domestic memory maker, CXMT, is projected to produce only around 2 million HBM stacks in 2026, enough for perhaps 250,000 to 300,000 Ascend 910C equivalent packages. That is the real reason Huawei's roadmap leans so heavily on linking more chips together rather than making each chip carry more memory. It has less domestic HBM to work with, not less silicon capacity.
Figure 1: estimated share of TSMC's 2026 CoWoS advanced packaging capacity by customer, based on DigiTimes and Morgan Stanley figures reported by Tom's Hardware and industry trackers. Figures are analyst estimates, not company disclosures, and should be read as directional rather than exact.
NVIDIA AI chip demand China deal: what is actually being sold, and what is not
This is the part of the story that confuses people, because headlines make it sound like a done deal when it is closer to a stalemate. In mid-2026, the US Commerce Department cleared around 10 Chinese companies, including Alibaba, Tencent, ByteDance, and JD.com, to buy Nvidia's H200 chip, with a cap of 75,000 units per customer, and licensed Lenovo and Foxconn as distributors.
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That sounded like a breakthrough. It was not one yet. As of mid-May 2026, not a single H200 chip had actually been delivered to China, according to Reuters reporting picked up by Yahoo Finance, despite the US approvals, because the deal remained stuck between American export rules and Chinese supply chain scrutiny. As of this week, the picture has shifted again, but is still not resolved. According to a TrendForce report from July 9, 2026, citing Reuters and The Information, Beijing appears ready to finally allow a limited group of AI companies, including Alibaba, ByteDance, and DeepSeek, to acquire H200 chips, but the approved volume may land below 200,000 units, less than half of what Chinese buyers reportedly requested.
Why would China slow-walk its own companies from buying chips they are allowed to buy? Because Beijing is actively pushing its tech firms toward domestic suppliers like Huawei, and every H200 that gets approved is one less reason for a Chinese cloud company to keep investing in the Ascend ecosystem. It has become less a technology problem now, and more a policy negotiation, and Nvidia is stuck in the middle of it.
Even without China, Nvidia is not exactly struggling. The company's own fiscal first quarter commentary noted that no shipments of Hopper-series data center products to China occurred during the quarter at all, compared with $4.6 billion in the same quarter a year earlier, and revenue still hit a record. Huang still thinks the country matters long term, telling investors he expects the new CPU market driven by agentic AI systems to be worth $200 billion, with China included in that estimate.
Ascend vs Nvidia: the workload question that decides the answer, not the marketing
Most coverage treats Huawei catching up as one universal claim. That is the wrong way to think about it. The honest answer depends entirely on what job the chip is doing, and the clearest public evidence of that split comes from DeepSeek, the Chinese AI lab behind the R1 model.
According to Financial Times reporting carried by Tom's Hardware, Chinese authorities encouraged DeepSeek to train its follow-up R2 model on Huawei's Ascend chips after the success of R1. DeepSeek tried. The training runs hit unstable performance, slower chip-to-chip connectivity, and limits in Huawei's CANN software toolkit, and even after Huawei sent its own engineers on site, the team could not complete a successful full training run on Ascend hardware. DeepSeek switched back to Nvidia chips for training and kept Ascend for the inference side of the model instead, and the delay pushed R2's launch back by months.
That single case study is a useful lens for the wider market. Here is a rough breakdown of where the evidence points, drawn from the DeepSeek episode, along with public specs and vendor claims:
| Workload | Where Ascend stands today | Deciding factor |
|---|---|---|
| Inference on an already trained model | Viable, and Huawei's main current strength | CANN software is mature enough for serving; the interconnect scale compensates for the lower per-chip throughput |
| Training a frontier model from scratch | Not yet reliable at scale, per DeepSeek's own experience | CANN and driver stability at large cluster sizes, plus no native FP8 support |
| Fine-tuning or reinforcement learning on an existing model | Mixed, workload dependent | Smaller cluster sizes reduce exposure to interconnect and stability issues seen in full pretraining |
| Latency-sensitive, high-concurrency serving | Competitive for many Chinese domestic deployments | Cost and availability inside China often outweigh the raw throughput gap versus Nvidia |
The takeaway is not that Huawei has closed the gap with Nvidia. It is that Huawei has closed the good enough gap for one job, inference, and China has decided that is enough for now, while it keeps working on the harder problem of training.
Intel nvidia ai chip competition: the underdog story that is not over yet
Intel spent years watching Nvidia and AMD take over the AI chip market while its own attempts, like the Gaudi series, landed with a thud. In 2026, Intel is trying again, and the plan looks more grounded than flashy.
The centerpiece is Crescent Island, an inference-focused data center GPU that Intel first showed at the OCP Global Summit in October 2025 and detailed further at Computex 2026. The interesting part is the memory choice. Instead of the expensive HBM that Nvidia and AMD use, Crescent Island's reference design runs on 160 GB of LPDDR5X, the kind of memory more commonly found in laptops and phones, according to Intel's own announcement. At Computex, Intel confirmed board partners will have the flexibility to build variants with up to 480 GB of that same memory, and the chip carries a 350-watt power target aimed at air-cooled enterprise servers, per TechSpot's coverage of the announcement. Customer sampling is scheduled for the second half of 2026, with general availability expected in 2027.
That is a deliberate cost play, not a corner cut by accident. Intel is betting that cheaper memory in air-cooled server racks beats expensive HBM in liquid-cooled ones, for a large chunk of everyday inference work. Intel CTO Sachin Katti has framed the strategy around a shift from static training toward constant, everywhere inference driven by agentic AI, arguing that this calls for systems that pair the right silicon with the right task rather than one chip trying to do everything. In plain terms, Intel is not trying to beat Nvidia at the training game Nvidia already won. It wants the cheaper, high-volume inference lane instead.
The business numbers give Intel some room to try. Intel's data center and AI segment posted $5.1 billion in revenue in the first quarter of 2026, up 22 percent year over year, and Intel's stock had surged more than 200 percent year to date by mid-2026. Some of that comes from a broader manufacturing comeback story, not just AI chips. Intel secured more than $18 billion in fresh funding, including $11.1 billion from the US government, $5 billion from Nvidia itself, and $2 billion from SoftBank. Yes, Nvidia is funding a company that is trying to compete with it. Business rivalries are complicated like that, and the funding also lands inside a wider push by Washington to rebuild domestic chip manufacturing, a theme that shows up elsewhere in US tech policy this year, including in how politically branded tech ventures have leaned on similar buy-American messaging.
Intel's other angle is its own foundry business. Panther Lake, its first laptop chip built on the new 18A manufacturing process, is already shipping in more than 200 laptop designs and delivers 180 total platform TOPS, though that figure combines the CPU, GPU, and NPU together rather than measuring one component alone. Clearwater Forest, Intel's first 18A server chip, launched in the first half of 2026 with up to 288 cores. Every one of these products is also a pitch to outside customers: if Intel can manufacture its own chips well on 18A, it can convince other companies to let Intel manufacture their chips too.
Will Intel actually dent Nvidia's inference lead? Too early to say. Crescent Island has not shipped to a single paying customer yet, and Intel has not published raw throughput numbers for the chip. Sampling later this year will tell us more than any press release has so far.
Why a chip's spec sheet number and its real-world number are rarely the same
This is the section almost every consumer-facing chip article skips, and it is one of the most useful things to understand before comparing any two chips by their advertised numbers. Engineers who train large models track a metric called model FLOPs utilization, or MFU: the share of a chip's theoretical peak compute that a real training or inference job actually achieves once memory bandwidth limits, chip-to-chip communication overhead, and software scheduling are factored in.
Meta's own published research on Llama 3.1 reported an MFU of 38 to 43 percent during training, and separate industry benchmarking generally puts well-optimized dense model training in the 40 to 55 percent range on Nvidia's mature CUDA stack, with mixture of experts training often landing lower, around 25 to 40 percent, because sparse routing fragments compute across the cluster. Inference has its own ceiling: prefill stages often reach 30 to 50 percent MFU, while decode stages can sit in the single digits by design, since decode is limited by memory bandwidth rather than raw compute.
Why this matters for a buyer: a chip with a higher peak FLOPS number on paper can still lose in practice if a competing chip's software stack is mature enough to capture a larger share of that peak. CUDA has had over a decade of kernel-level optimization work behind it. Huawei's CANN toolkit and AMD's ROCm are newer and generally show a wider gap between advertised peak and real achieved throughput, which is part of why DeepSeek's training runs on Ascend hardware ran into instability that a comparable Nvidia cluster would not have hit as often. Comparing two AI chips by peak TFLOPS alone, without asking how much of that peak a real workload can capture, is the single most common mistake in casual chip coverage.
Other AI chip companies and AI chip makers worth knowing
NVIDIA, Huawei, and Intel get the headlines, but the list of serious AI chip makers is longer than that, especially inside China, where export restrictions forced an entire domestic industry into existence almost overnight. This is one of the odder side effects of the export control fight. The US intended to slow China down. Instead, it created guaranteed demand for half a dozen Chinese chip startups that might not have survived a fully open market where Nvidia could sell its best hardware to anyone who wanted it.
AMD remains Nvidia's closest Western rival, with its Instinct line competing directly in data center training and inference. Alibaba's chip division, T-Head, has quietly become a real commercial player, with cumulative AI chip shipments now exceeding 470,000 units, more than 60 percent of which go to external commercial customers outside Alibaba itself, spanning manufacturing, automotive, and finance.
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Baidu's Kunlunxin division is another one to watch. Its third-generation P800 chip runs on a 7-nanometer process and delivers 345 TFLOPS of FP16 performance, benchmarked against Nvidia's older A100 and Huawei's Ascend 910B. Hygon, which builds both CPUs and AI accelerators, posted first-quarter 2026 revenue of 4.034 billion yuan, up 68 percent year over year, with a next-generation chip aimed at matching H100-class performance later this year.
None of these companies threatens Nvidia's global lead today. Together, though, they represent something bigger than any single product launch: a parallel AI hardware ecosystem growing inside China, built specifically because Nvidia's best chips are not available there. That is not a temporary gap. It is a structural shift in how the global AI chip supply chain works, one that connects to the broader story of infrastructure most people never think about until it breaks or gets rationed.
Outside China, the field looks different. AMD's Instinct MI400 series and its Helios rack systems, both shown at CES 2026, target the same hyperscale customers Nvidia serves: Microsoft, Meta, Amazon, and Google. Qualcomm has pushed further into laptop chips with AI acceleration built in rather than added afterward, chasing the same Copilot Plus PC certification that Intel's Panther Lake already meets. Cerebras keeps building wafer-scale chips, a genuinely different physical approach where one giant piece of silicon replaces dozens of smaller GPUs wired together. None of these companies will unseat Nvidia this year. Each one chips away at a slightly different corner of the market, and a few small corners eventually add up, similar to how satellite internet providers like Starlink chipped away at ground-based ISPs one region at a time rather than all at once.
Chip allocation politics: who actually gets Nvidia's supply first
This is the layer of the story that decides who can actually build with this hardware, and it has almost nothing to do with which chip is objectively best. NVIDIA's supply is not sold on a first-come basis. Hyperscalers, including Microsoft, Amazon, Google, and Meta, signed multi-billion-dollar reservation agreements well in advance of delivery, and their combined 2026 AI infrastructure spending is projected in the neighborhood of $650 to $725 billion across the four companies, according to multiple analyst compilations of the companies' own capital expenditure guidance. That scale of forward commitment is why a startup ordering the same generation of chip can face a materially longer wait than a hyperscaler ordering the same part.
Smaller AI labs and startups have responded by working around direct allocation entirely. A growing share of AI compute now runs through neocloud providers, companies that rent out GPU capacity by the hour rather than sell chips outright, specifically because these buyers cannot secure direct allocation from Nvidia at any price that makes sense for their scale. That rental market has its own price swings tied to the same underlying scarcity: spot pricing for older-generation chips has run well above blended reservation rates during periods of tight supply.
There is also a persistent enforcement problem that gets far less attention than product launches. Despite export controls, reporting over the past two years has documented Nvidia chips reaching China through third-country resellers and shell distributors, a gap that neither Washington nor Nvidia has fully closed. None of this changes the headline story of who makes the best chip. It does change who can actually get one, on what timeline, and at what contract size, which, for most companies evaluating AI infrastructure in 2026, is the more practical question.
Common AI chip claims worth checking against the record
A lot of AI chip coverage repeats the same lines without checking whether they still hold up. Here is where several of the most common ones land once checked against the reporting above.
| The claim | What the record shows |
|---|---|
| Higher peak TFLOPS means a faster chip in practice | Real throughput depends on memory bandwidth, interconnect speed, and software maturity as much as raw compute, as the MFU gap between advertised and achieved performance shows |
| US export controls have stopped China's AI chip progress | They redirected demand toward domestic suppliers like Huawei, Cambricon, and Moore Threads, effectively creating guaranteed customers that those firms might not have had in an open market |
| Huawei's Ascend chips now match Nvidia's best hardware | Ascend is competitive for inference on already trained models. DeepSeek's own R2 training attempt shows frontier-scale training is a different, unresolved problem for Huawei |
| Intel's Crescent Island proves Intel is back in the AI chip race | As of mid-2026, Crescent Island has not shipped to a single paying customer. Sampling and shipping are two different milestones, and general availability is not expected until 2027 |
| The H200 approval means Nvidia's China business is recovering | Even the newest approval reporting suggests a cap below 200,000 units, less than half of what was requested, with actual deliveries still unresolved as of this month |
The pattern across all five is the same one you see in other corners of tech reporting too, from oversold 5G coverage maps to the kind of router and ISP marketing claims that don't survive a technical read: a headline can be technically accurate and still misleading once the deployment reality is checked.
What this actually means if you are not a chip investor
Most people reading AI chip news are not buying Nvidia stock or reading Huawei's quarterly filings for fun. Here is the plain version of why any of this matters to a regular business or user.
AI chip supply affects what AI tools cost and how fast new features roll out. When training chips are scarce or expensive, cloud AI subscriptions cost more, and smaller companies get squeezed out of building their own models. When inference chips get cheaper, as Intel and others are betting they will, running AI features inside everyday apps gets cheaper too, which usually shows up as lower prices or more free tier access for users, including the kind of AI tools people use for everyday tasks like budgeting and personal finance planning.
Image Credit: Leonardo AI
The China split matters even if you never touch a Chinese AI product. Two separate hardware and software ecosystems, one built on Nvidia and CUDA, one built on Huawei and its own tools, means AI models trained on each stack behave a little differently. Developers building global products increasingly have to think about which stack their user base runs on, especially if they operate in or sell into China.
The export control back and forth is not just a headline, either. Every time Washington tightens or loosens the rules, it changes what Nvidia can sell, which changes Nvidia's revenue guidance, which moves markets that touch retirement accounts and index funds well beyond anyone who follows semiconductors specifically.
There is also a power, water, and jobs angle that gets less attention than the chip specs themselves. Training and running these chips at scale requires enormous data center capacity, and the water and electricity demands of that buildout are their own growing story worth understanding on its own terms. Some of the newest US data center projects have leaned on green building standards to manage that footprint, a path similar to what facilities elsewhere have already tested with mixed results. Meanwhile, Intel's 18A process runs through fabs in Arizona and Oregon, and Intel has framed its foundry push as a bet on rebuilding advanced chip manufacturing inside the US. Whether that succeeds depends on outside customers actually trusting Intel to build their chips, not just Intel's own products. Every Panther Lake laptop that ships without a defect and every Clearwater Forest server that hits its performance target is effectively a sales pitch to companies deciding right now whether to hand Intel their own chip designs to manufacture.
What to watch in AI chips news for the rest of 2026
A few concrete things will tell us more than any prediction can. Watch whether Nvidia's H200 chips actually ship to China in meaningful volume, not just get approved on paper, given that even this month's reporting still points to a capped, unresolved outcome. Watch Huawei's Ascend 950DT launch, expected in the fourth quarter, since it targets training and decoding workloads with 144 GB of HBM memory and a 2 terabyte per second interconnect, a step toward the training capability Huawei still lacks. Watch Intel's Crescent Island sampling results in the second half of the year, since that is the first real test of whether Intel's cheaper memory bet works in practice rather than on a spec sheet. And watch whether China's CXMT can actually scale HBM output past its current 2 million stack ceiling, since that single number may decide how many more Ascend chips Huawei can build once its stockpiled foreign memory runs out.
Keep an eye on the policy side just as closely as the product side. In this market, a signature in Washington or Beijing can move more revenue in a single afternoon than a new chip launch does in a year.
USA Beam takes
The AI chip race in 2026 is not simply Nvidia against everyone else. It is Nvidia's performance lead against Huawei's speed of scale, against Intel's bet on cheaper inference, playing out inside a US-China policy fight that neither company controls, and underneath all three, a memory and packaging shortage that none of them fully control either. NVIDIA still makes the best single chip on the market by a wide margin, and its revenue numbers show customers are still lining up despite losing China almost entirely. Huawei has not closed the technical gap in training, as DeepSeek's own R2 experience shows, but it has closed the good enough gap for inference, and that is the part of the market growing the fastest. Intel has real funding and a real product timeline for the first time in years, though nothing it has shown has shipped to a paying customer yet. The facts point to a market splitting into two tracks rather than one company winning outright, constrained on every side by how much memory and packaging capacity actually exists, and that split is probably the more important story than any single chip release this year.
