[Summary]
DeepSeek is an AI company from Hangzhou, China, and with its High-Flyer research and capital base, it is shaking up the AI market with its low-cost API and open weight strategy. Since DeepSeek-R1 in 2025 and DeepSeek-V4 Preview in April 2026, the company has penetrated the developer community and discussions about corporate adoption to the extent that it has been called the "Linux of the AI world."
From an investor's perspective, the essence is not whether DeepSeek will directly replace OpenAI. The goal is to lower the price of AI models, reduce inference costs, and change the profitability of AI agents and in-house AI. Even if the usage of AI increases, the unit price of model API will decrease. This pressure will change the distribution of profits for cloud, GPU, SaaS, and AI app companies.
The conclusion is neutral to slightly bullish. However, serious issues remain for DeepSeek, including geopolitical risks, data management risks, censorship and security concerns, and the profitability of its low-price strategy. Cheap AI is strong. However, when it comes to corporate use, questions such as ``where to run it'' and ``what data to put in'' are viewed more unfavorably than in the stock market. It is necessary to distinguish between the winners of price destruction and the companies whose profit margins will be cut by price destruction.
This article will be organized based on the DeepSeek official API document, V4 Preview release, R1 official release, and V3/R1 technical paper that can be confirmed as of May 26, 2026.
First, the conclusion
DeepSeek is not just "cheap Chinese AI".
The essence is that the pricing system and supply structure of AI models have been changed.
When DeepSeek-R1 attracted attention in January 2025, the market feared not only that ``Chinese companies had caught up with OpenAI.'' More importantly, the assumption that creating AI absolutely requires huge GPUs, huge amounts of power, closed models, and huge amounts of capital has been shaken.
DeepSeek released the following three things at the same time.
| Elements | Questions DeepSeek poses to the market |
|---|---|
| Low-cost API | The unit cost of AI inference may be lower |
| Open weight | Companies can operate the model in-house |
| Efficient model design | GPU quantity may not be the only way to win |
This is why DeepSeek is called the "Linux of AI".
Of course, it's not exactly the same as Linux. Even though the weights and code of DeepSeek's models are made public, the training data and the entire process are not completely transparent. Therefore, in this text, we mainly treat it as an "open weight strategy" rather than "open source."
Still, the psychological impact it had on developers and companies was significant.
AI is no longer an expensive, centralized service provided only by a few large companies, but has moved closer to being something that can be incorporated into in-house environments, local environments, agent platforms, and business systems.
This is DeepSeek as an investment theme.
DeepSeek corporate structure
DeepSeek is an AI company based in Hangzhou, China.
The founder, Liang Wen-feng, is known as the co-founder of the quantitative hedge fund High-Flyer. AP reports that High-Flyer has developed a computerized stock trading model and used machine learning to hone its investment strategy.
This structure is quite unique for an AI startup.
Typical AI companies are pressured by VCs, cloud contracts, commercialization roadmaps, and IPO expectations. DeepSeek, at least in appearance, combines capital accumulated in finance with computational infrastructure, recruiting researchers, and a low-cost strategy.
| Comparison | US AI startup type | DeepSeek type |
|---|---|---|
| Sources of capital | VCs, strategic investors, cloud companies | High-Flyer financial capital |
| Revenue model | API, subscription, corporate contract | API, model release, in-house operation demand |
| Strategy | Closed high added value | Low price/open weight |
| Market pressure | Higher performance | Lower prices and in-house operation |
It's important to note that this doesn't mean that DeepSeek operates non-commercially. In fact, it appears to be a strategy to expand the market at a low price and enter the standard layer of developers and companies.
In the AI market, once something becomes the standard for developers, it is strong. Free or low-cost infrastructure can create a large commercial market around it, as was the case with Linux, PostgreSQL, Kubernetes, and Python.
DeepSeek's aim is similar to that.
Business model
On the surface, DeepSeek's business model is a combination of API billing and model publication.
However, what investors should look at is not just the sales model, but where they intend to capture value.
1. API billing
DeepSeek provides an API in an OpenAI compatible format.
The official DeepSeek API document available as of May 26, 2026 lists DeepSeek-V4-Flash and DeepSeek-V4-Pro, both of which are said to support 1M contexts, up to 384K output, JSON output, and Tool Calls.
The prices are also quite aggressive. On the official English API page, V4-Flash costs $0.14 per million input tokens and $0.28 per million output tokens. V4-Pro is priced at $0.435 per million input tokens and $0.87 per million output tokens during the discount period.
What we should be looking at here is the price design rather than the absolute amount itself.
AI agents consume more tokens than regular chat. It's a lot of planning, searching, tool invocation, code generation, validation, and re-running. As the unit price of APIs decreases, agent applications that were previously unprofitable will suddenly become a reality.
2. Open weight strategy
The official release of DeepSeek-R1 explains that it can be released under the MIT License, for commercial use, and for distillation use. DeepSeek-V3 also released model checkpoints and explained technical details such as MoE structure and MLA in a paper.
This is the biggest difference from closed API companies.
Companies have the following options:
| Usage form | Meaning |
|---|---|
| Use official API | Can be introduced quickly at low cost |
| Run on your own server | Increased flexibility in data management and customization |
| Fine-tuning | Can be optimized for each business/industry |
| Create distillation/derivative models | Can be deployed to small models and local AI |
This will move us away from just ``purchasing AI as a service'' to ``embedding it as a component.''
3. Go get developer standards
DeepSeek's low pricing and exposure strategy is not a short-term profit maximization move.
Rather, it appears to be a move toward becoming a model for developers to try out first, a model that AI agent companies call in large numbers, and a model that companies use to verify their internal AI.
The strongest model in the AI market is not necessarily the model with the highest performance.
It is a model that is ``sufficiently high-performance, inexpensive, freely usable, and easy to incorporate.''
Once this condition is met, the model goes behind the scenes of the app. Users will come into contact with services that use the DeepSeek model without being aware of DeepSeek.
Technical strength
The important technical aspect of DeepSeek is not simply that the model is large.
Designed to be cost efficient.
The DeepSeek-V3 technical report describes it as a MoE model with 671B total parameters and 37B active for each token. Furthermore, MLA, DeepSeekMoE, load balancing without auxiliary loss, multi-token prediction, etc. were adopted.
DeepSeek-V4 Preview announced in April 2026 explains that V4-Pro has 1.6T total parameters and 49B active, and V4-Flash has 284B total parameters and 13B active. The official release also brought 1M Context and DeepSeek Sparse Attention to the fore.
If you cut down on the technical terms, the point is this.
Key point
↓
Key point
↓
Key point
↓
APIKey pointThis idea is very effective in the AI market.
AI bottlenecks will span GPU, power, cooling, data center, and inference costs by 2026. Even if only performance is improved, if the single inference is too expensive, AI agents and business automation will not spread.
DeepSeek has brought the performance race back to "more efficient computational design" rather than just "bigger GPU clusters."
Illustration: AI assumptions broken by DeepSeek
Impact on the AI market
The shock that DeepSeek had on the market was clearly reflected in its stock price.
In January 2025, when DeepSeek's low-cost AI model attracted attention, NVIDIA temporarily fell sharply. According to a Reuters report, NVIDIA's market capitalization decreased by approximately $593 billion in one day.
This reaction was a little extreme.
This is not to say that demand for GPUs will disappear just because DeepSeek is released. In fact, if AI becomes cheaper, its usage will increase, and the demand for inference and agents may increase in the long run.
However, I can understand why the market was scared.
Until now, the AI investment story has been fairly simple.
AIdemandKey point
↓
GPUdemandKey point
↓
NVIDIAKey pointDeepSeek bent this straight line.
AIdemandKey point
↓
Key point
↓
Key point
↓
Key pointThis is important.
DeepSeek didn't destroy demand for AI. They came to destroy the profit distribution of AI demand.
Beneficiaries and headwind companies
DeepSeek-type AI price destruction will divide the beneficiaries and the companies facing headwinds.
| Position | Influence |
|---|---|
| AI agent company | Reducing API costs makes it easier to use multi-stage inference |
| Small and medium-sized businesses and individual developers | Lowering the hurdles to trying advanced AI |
| Companies introducing in-house AI | Increasing options for in-house operation and local LLM |
| Closed AI companies | Price competition and differentiation pressure intensifying |
| GPU/AI infrastructure companies | There is caution in the short term, but if usage increases, long-term demand will remain |
| SaaS companies | While the cost of AI functions is decreasing, it is becoming difficult to charge for AI functions alone |
A common mistake investors make is to simplify things by saying that low-priced AI = bad news for AI-related stocks.
In reality, as the unit cost of AI falls, the number of applications that use AI will likely increase. It can be used in a wide range of situations, including business automation, coding support, inquiry response, internal searches, data analysis, advertising production, and sales support.
The question is who will benefit from it.
Will the model provider take it? Will app companies take it? Will the cloud take it? Will GPU companies take it? Will corporate users absorb it as a cost reduction?
DeepSeek perturbs this distribution considerably.
Compatibility with AI agents
DeepSeek is particularly effective in the field of AI agents.
AI agents don't just answer one question. Break down the task, search for it, call the tool, write the code, fix it if it fails, and do it again.
In other words, the amount of token consumption is large.
For this reason, an agent's profitability is strongly influenced by the model unit price.
| Agent usage | Meaning of low price model |
|---|---|
| Coding support | Increase the number of trial and errors |
| Creation of sales materials | Drafting, revisions, and comparisons can be done cheaply |
| Internal investigation | Easy to read large amounts of documents |
| Customer Support | Easy to automate primary response at low unit cost |
| Business RPA | Reduces long-time inference and verification costs |
DeepSeek doesn't even need to be the highest-performance model here.
In many jobs, being ``smart enough, cheap enough, and able to call a lot'' is more valuable than being ``the best.'' In the agent era, this realistic price/performance ratio is quite effective.
Maximum risk
DeepSeek has clear risks.
1. Geopolitics and data management
The biggest risks are data management and security issues that come with being a Chinese company.
Reuters reports that Italian data protection authorities have blocked DeepSeek's chatbot, Australia has banned its use on government terminals, and Taiwan has also banned its use in government departments.
When it comes to corporate implementation, this is the coldest point.
The decision to send customer information, contracts, research and development materials, and government, financial, and medical data to external APIs is not an easy decision, even if it is source code or general documents.
If you are using a DeepSeek model, you will likely choose a design that runs in your own environment rather than a cloud API. However, in that case, the burden of GPUs, operations, security, audit logs, and model updates shifts back to the company.
2. Difficult to control due to open weight
Open weights are attractive to developers.
However, this is difficult from a safety perspective. Once a model is widely distributed, the provider cannot fully control how it is used. Issues such as modification, abuse, censorship evasion, and diversion for dangerous purposes arise.
It is possible that regulators may seek stricter rules for open weight models in the future.
3. Uncertainty of profitability
Our low price strategy is strong.
However, low prices do not necessarily translate into high profits.
It's still difficult to see where DeepSeek will profit from this. Is it API charging, implementation support for companies, integration with domestic cloud hardware, or is strategic value placed on expanding as the foundation of China's AI sphere itself?
This is where the stock market is weak.
I can see the growth. I also understand the influence. But it's still unclear where the profit pool will fall.
View towards 2027
Looking ahead to 2027, there are three points to look at with DeepSeek.
1. Penetration of internal AI infrastructure
Companies will expand generative AI from simple chatbots to internal searches, document creation, contract reviews, data analysis, and development support.
At this point, cost and data management become issues.
The DeepSeek model is compatible with in-house operations and private environments. In particular, models with sufficient performance and low cost are likely to be candidates for AI for all employees, departmental AI, and on-premises AI.
2. Divide the US-China AI sphere
AI is intertwined with cloud, semiconductors, data, regulation, and national security.
As a result, the division between US-based AI and Chinese-based AI is likely to progress further. DeepSeek can easily be seen as a representative open weight platform on the Chinese side.
This is both a tailwind and a headwind for DeepSeek.
It is easy to standardize in China. On the other hand, adoption hurdles are higher for large companies and government applications in the United States, Europe, and Japan.
3. Local AI and Edge AI
DeepSeek's efficiency will also be important in the context of local AI and edge AI.
In order to run AI on smartphones, PCs, factory terminals, robots, and in-vehicle systems, it is difficult to use only large cloud-based models. Miniaturization, distillation, and low-cost inference will be required.
If the number of R1-based distillation models and derived models increases, the options for local AI will expand.
This also relates to semiconductor investment. This is because it will not only affect cloud GPUs, but also NPUs, edge AI chips, AI semiconductors for PCs, and smartphone SoCs.
KPIs that investors should look at
DeepSeek is an unlisted company, so it is difficult to see normal sales and profits.
That's why investors need to look at surrounding KPIs.
| KPI | Reasons to watch |
|---|---|
| API price trends | Measuring the downward pressure on AI model unit prices |
| Update frequency of V4 series and R series models | See sustainability of competitiveness |
| Adopted by Hugging Face and GitHub | Has it become a developer standard |
| Examples of in-house operations by companies | Confirmation of enterprise penetration |
| Regulations and prohibitions | Strength of geopolitical risks |
| Price revisions of closed AI companies | Is DeepSeek's competitive pressure working? |
| Cost rate of agent-based services | Will the benefit of the low price model become profitable |
| GPU/Cloud CAPEX | Determine whether improving efficiency will decrease demand or increase demand |
Personally, I'd rather see how OpenAI, Anthropic, Google, Meta, Chinese AI companies, and cloud companies move prices than DeepSeek itself.
DeepSeek is scary not because it takes over the world on its own.
This is because it moves the price list of competitors.
Summary
DeepSeek is a company that has changed the pricing structure of the AI market.
In contrast to OpenAI's closed high-performance models, DeepSeek combines low-cost APIs, open weights, and efficient model design. As a result, the profitability lines for AI agents, in-house AI, local AI, and in-house operation models are beginning to change.
The expression "Linux of the AI world" may seem a bit exaggerated.
Still, the direction is pretty straightforward. DeepSeek is moving AI from being the preserve of large corporations to a foundation that developers and businesses can build into.
However, investors should not rely on enthusiasm alone.
Geopolitics, data management, regulation, censorship, safety, profitability. None of them are light.
With the advent of DeepSeek, the demand for AI will not disappear. Rather, it would be more natural to see a scenario in which usage increases as the unit price declines.
The question is who will benefit from the increased demand for AI.
The answer will be a very important turning point in the AI market from 2026 to 2027.
Source
- DeepSeek API Docs “Models & Pricing” (verified May 26, 2026) https://api-docs.deepseek.com/quick_start/pricing
- DeepSeek API Docs “DeepSeek V4 Preview Release” (April 24, 2026) https://api-docs.deepseek.com/news/news260424
- DeepSeek API Docs “DeepSeek-R1 Release” (January 20, 2025) https://api-docs.deepseek.com/news/news250120
- DeepSeek-AI “DeepSeek-V3 Technical Report” https://arxiv.org/abs/2412.19437
- DeepSeek-AI “DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning” https://arxiv.org/abs/2501.12948
- DeepSeek-R1 GitHub repository https://github.com/deepseek-ai/DeepSeek-R1
- DeepSeek-V3 GitHub repository https://github.com/deepseek-ai/DeepSeek-V3
- AP News "Upstart Chinese AI company DeepSeek's founder started out as a low-key hedge fund entrepreneur" (January 28, 2025) https://apnews.com/article/0673d5c39d90108189cc31b88d85b9f8
- Reprint from Reuters "US tech shares recover some losses from steep DeepSeek selloff" https://www.investing.com/news/stock-market-news/tech-stock-selloff-deepens-as-deepseek-triggers-ai-rethink-3833347
- Reprint from Reuters "Australia bans DeepSeek on government devices citing security concerns" https://www.investing.com/news/world-news/australia-bans-deepseek-on-government-devices-citing-security-concerns-3847809
- Reprint from Reuters "Italy's regulator blocks Chinese AI app DeepSeek on data protection" https://www.investing.com/news/economy-news/italys-privacy-watchdog-blocks-chinese-ai-app-deepseek-3840843