On January 27, 2025, NVIDIA shares fell roughly 17% and the company’s market value dropped by about $600 billion as investors reacted to DeepSeek’s new reasoning model and claims of cheaper AI development. That was a sharp repricing of expectations—not $600 billion leaving NVIDIA’s bank account, and not proof that its business was finished. The privacy rumor was also only half right: an iPhone app does not gain unrestricted access to Messages or email just by being installed, but anything you send to DeepSeek’s hosted chatbot goes to the service.
What happened to NVIDIA on January 27, 2025?
DeepSeek-R1’s rapid rise prompted investors to question whether advanced AI would require as much expensive computing infrastructure as expected. NVIDIA shares fell approximately 17% in that session, and its market capitalization declined by roughly $600 billion, according to 9to5Mac’s January 27, 2025 report.
Market capitalization is the market’s value for a company’s outstanding shares. A decline in that figure is not a cash loss from the company’s accounts, nor does it mean revenue fell by the same amount. It reflects investors marking down what they think the company may be worth, based on expectations about future earnings and risk.
The sell-off was an expectations shock. DeepSeek raised the possibility that developers could achieve strong results with less compute per model or task. If that proved durable, demand for the most expensive chips might grow more slowly than investors had assumed. One trading session, however, could not establish what customers would buy over time or how NVIDIA’s future earnings would change.
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What is DeepSeek?
DeepSeek is a Chinese AI company associated with quantitative hedge fund High-Flyer and its founder, Liang Wenfeng. The name can refer to the company, its consumer-facing chatbot and apps, or specific models; those are not interchangeable. The company’s official site presents its services, while its technical releases include DeepSeek-V3 and DeepSeek-R1.
V3 drew attention for its reported training efficiency. R1, released in January 2025, focused on reasoning and came with smaller distilled variants. The R1 repository and R1 paper provide technical details and model materials.
“Open source” needs qualification in this context. Published weights and code can let others inspect, adapt, or run a model, subject to the relevant license. That does not automatically make all training data, infrastructure details, or commercial rights open. Nor does access to model weights mean the official hosted chatbot has the same behavior or privacy terms as a locally run copy.
What did R1 demonstrate, and how did DeepSeek pursue efficiency?
R1 showed that large-scale reinforcement learning could produce strong reasoning behavior without relying entirely on the conventional recipe of supervised fine-tuning followed by human preference training. It also highlighted inference-time scaling: a model can spend additional computation while answering, rather than doing all useful computation during initial training. Performance comparisons depend on the model version, prompts, sampling settings, benchmark design, and whether the test uses raw weights or a hosted product.
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Architecture and hardware efficiency
DeepSeek-V3 uses a mixture-of-experts design. Rather than activating every parameter for every token, the model routes a token through a subset of experts. This can reduce active computation compared with a dense model of similar total parameter count, though it does not mean the full model requires no memory or infrastructure.
DeepSeek’s architecture also sought to improve memory use and communication efficiency. The company’s work occurred amid U.S. export controls that limited Chinese access to some advanced accelerators available to U.S. firms. That context makes efficiency important, but it does not establish that DeepSeek used no NVIDIA chips or that every hardware and development cost was captured in a headline figure.
Reinforcement learning, generated data, and distillation
R1’s technical report emphasizes reinforcement learning to develop reasoning behavior. Generated or synthetic data and automated evaluation can help scale training, but they can also reproduce errors or undesirable patterns. DeepSeek also released distilled models built on smaller Qwen- and Llama-family models, allowing those models to imitate aspects of a larger model’s behavior. Distillation explains the smaller derivatives; it is not a complete explanation for how the original R1 was developed.
What the $5.6 million figure covers
DeepSeek reported approximately $5.6 million in compute cost for a specified final V3 training run in its V3 technical paper. The figure is often rounded to $6 million, but it is not a complete accounting of the company’s research and development, hardware acquisition, data preparation, earlier experiments, staffing, or ongoing infrastructure. It should be read as a reported cost for a defined training run, not the all-in cost of creating DeepSeek or its model family.
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Did DeepSeek make NVIDIA irrelevant?
No. DeepSeek challenged the assumption that frontier AI always requires ever-larger and more expensive training runs; it did not show that NVIDIA products had become obsolete. Efficiency can reduce compute needed for a particular model or task, but it can also make AI affordable for more companies and applications, expanding the total number of workloads.
Training and inference are different demands
Training builds or updates a model; inference is the computation used to produce answers after training. A more efficient training run could reduce demand for chips for that run. But a popular reasoning model may perform additional computation each time it answers, and broader adoption can mean more requests, longer context windows, agents, and fine-tuning. Lower cost per task and higher total infrastructure demand can coexist.
What NVIDIA argued
In its statement on DeepSeek-R1, NVIDIA called the model an “excellent AI advancement” and emphasized test-time scaling, inference, GPUs, and networking. That is NVIDIA’s interested interpretation of an event that investors initially read as a possible threat to hardware demand—not an independent forecast or proof of future demand.
A one-day share-price move cannot show whether customers canceled orders, whether DeepSeek’s costs are directly comparable with other companies’ total budgets, or whether one model can replace the many workloads served by data-center hardware. It shows that investors reconsidered the expected economics of AI infrastructure.
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Was the iPhone privacy claim true?
The viral claim needs to be split into device access and cloud data handling. iOS sandboxing limits what an app can read from the phone without permission. That does not prevent a cloud AI provider from receiving text or files a user deliberately submits.
| Claim | Verdict | What it means |
|---|---|---|
| Installing DeepSeek gives it access to all iPhone messages and email. | Misleading | iOS apps run in a sandbox, and protected resources are controlled by permissions. Installation alone does not grant unrestricted access to Messages, email, photos, contacts, or local files. See Apple’s platform security guide. |
| DeepSeek cannot collect personal information. | False | A hosted chatbot receives prompts and uploads. Its privacy policy also describes collection of service-related information such as account, device, network, and usage data. The policy may change, so readers should review its current wording and date. |
| Sign in with Apple makes the chatbot private. | Misleading | Sign in with Apple can limit disclosure of a user’s real email address, including through a private relay address. It does not hide prompts or files sent to the chatbot after login. |
| Data may be stored in China. | A policy-based concern | DeepSeek’s policy has identified China as a storage location for collected personal information. Storage location can affect legal jurisdiction and access risk, but it does not by itself prove that every prompt is provided to a government. Check the policy in force when using the service. |
The practical distinction is simple: iPhone permissions govern what the app can silently take from the device; the provider’s policy and practices govern information sent to its servers. Neither sandboxing nor an email relay makes a hosted chatbot a suitable place for secrets.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should users avoid sending to a hosted AI service?
- Passwords, authentication codes, API keys, private source code, or security credentials.
- Confidential business documents, client material, unpublished intellectual property, or information covered by employer, school, or regulatory rules.
- Medical, financial, or legal records, or personal information about someone else.
For work, use only a deployment approved by your organization. Hosted DeepSeek, a third-party API, a cloud marketplace deployment, and a model running locally can have different operators, logging, retention, and privacy terms. Choosing a DeepSeek model through another platform does not automatically put the interaction under DeepSeek’s own policy—or make it private.
Local inference can keep prompts on a machine under the user’s control, but it requires compatible hardware and operational care. Users must manage model provenance, software updates, access controls, and performance, and local models can still produce unsafe or incorrect outputs. For ordinary experimentation, avoid sensitive inputs regardless of deployment.
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Why do model behavior and benchmarks vary?
DeepSeek’s hosted chatbot may refuse or redirect questions about politically sensitive Chinese history. Behavior can differ among the official hosted service, downloadable weights, third-party deployments, and products that wrap a DeepSeek model. A third-party service may add its own system prompts, moderation, retrieval, logging, or privacy rules. One viral prompt is not enough to characterize every version.
Likewise, a benchmark result does not establish that a model is best for every task. Scores can depend on test design, prompt format, sampling settings, model revision, and whether tools or extra inference computation are allowed. For a practical choice, test the exact deployment on representative tasks and assess its data terms and policy behavior alongside answer quality.
The broader lesson from the DeepSeek shock
AI economics are not determined by training cost alone. Compute can be spent during pretraining, post-training, or each inference request. A lower-cost model can intensify competition and widen access while also increasing total usage. DeepSeek made efficiency a more urgent question for the market; it did not settle how much infrastructure the AI industry will ultimately need.
The January 2025 episode also illustrates why financial headlines need precise units. NVIDIA’s roughly $600 billion decline referred to market capitalization, the V3 figure referred to a reported training-run compute cost, and the iPhone rumor confused device permissions with data a user submits online. Those are three different claims, each requiring its own context.
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