DeepSeek R1 s Implications: Winners And Losers In The Generative AI Value Chain

R1 is mainly open, on par with leading exclusive models, appears to have been trained at substantially lower cost, and is more affordable to use in regards to API gain access to, all of which point to a development that might alter competitive characteristics in the field of Generative AI.
- IoT Analytics sees end users and AI applications companies as the most significant winners of these current advancements, while exclusive design providers stand to lose the most, based upon worth chain analysis from the Generative AI Market Report 2025-2030 (released January 2025).


Why it matters


For providers to the generative AI worth chain: Players along the (generative) AI worth chain might require to re-assess their value propositions and line up to a possible reality of low-cost, light-weight, open-weight designs.
For generative AI adopters: DeepSeek R1 and other frontier models that might follow present lower-cost choices for AI adoption.


Background: DeepSeek's R1 design rattles the markets


DeepSeek's R1 design rocked the stock markets. On January 23, 2025, China-based AI start-up DeepSeek released its open-source R1 reasoning generative AI (GenAI) design. News about R1 rapidly spread out, and by the start of stock trading on January 27, 2025, the market cap for lots of significant innovation business with big AI footprints had actually fallen dramatically because then:


NVIDIA, a US-based chip designer and developer most known for its data center GPUs, dropped 18% in between the marketplace close on January 24 and the marketplace close on February 3.
Microsoft, the leading hyperscaler in the cloud AI race with its Azure cloud services, dropped 7.5% (Jan 24-Feb 3).
Broadcom, a semiconductor company concentrating on networking, broadband, and customized ASICs, dropped 11% (Jan 24-Feb 3).
Siemens Energy, a German energy innovation vendor that provides energy options for information center operators, dropped 17.8% (Jan 24-Feb 3).


Market participants, and specifically financiers, responded to the narrative that the model that DeepSeek released is on par with cutting-edge models, was supposedly trained on only a number of countless GPUs, and is open source. However, since that initial sell-off, reports and analysis shed some light on the initial buzz.


The insights from this article are based upon


Download a sample to find out more about the report structure, choose meanings, select market data, extra information points, and patterns.


DeepSeek R1: What do we understand previously?


DeepSeek R1 is a cost-effective, advanced reasoning model that matches leading competitors while fostering openness through publicly available weights.


DeepSeek R1 is on par with leading thinking designs. The biggest DeepSeek R1 model (with 685 billion specifications) performance is on par or perhaps much better than a few of the leading designs by US foundation design service providers. Benchmarks show that DeepSeek's R1 design performs on par or much better than leading, more familiar models like OpenAI's o1 and Anthropic's Claude 3.5 Sonnet.
DeepSeek was trained at a considerably lower cost-but not to the level that initial news recommended. Initial reports suggested that the training costs were over $5.5 million, however the real value of not just training however establishing the design overall has been discussed since its release. According to semiconductor research and consulting company SemiAnalysis, the $5.5 million figure is only one component of the costs, leaving out hardware costs, the incomes of the research and development team, and other aspects.
DeepSeek's API prices is over 90% cheaper than OpenAI's. No matter the real cost to develop the model, DeepSeek is using a much less expensive proposal for using its API: input and output tokens for DeepSeek R1 cost $0.55 per million and $2.19 per million, respectively, compared to OpenAI's $15 per million and $60 per million for its o1 design.
DeepSeek R1 is an ingenious design. The related clinical paper released by DeepSeekshows the approaches used to develop R1 based upon V3: leveraging the mix of professionals (MoE) architecture, reinforcement knowing, and really innovative hardware optimization to develop models requiring less resources to train and fakenews.win likewise fewer resources to carry out AI inference, leading to its abovementioned API use costs.
DeepSeek is more open than most of its rivals. DeepSeek R1 is available totally free on platforms like HuggingFace or GitHub. While DeepSeek has actually made its weights available and offered its training approaches in its term paper, the original training code and information have actually not been made available for a competent individual to develop a comparable design, consider specifying an open-source AI system according to the Open Source Initiative (OSI). Though DeepSeek has been more open than other GenAI companies, R1 remains in the open-weight classification when thinking about OSI standards. However, the release stimulated interest in the open source neighborhood: Hugging Face has actually introduced an Open-R1 effort on Github to create a full recreation of R1 by constructing the "missing pieces of the R1 pipeline," moving the model to completely open source so anybody can replicate and build on top of it.
DeepSeek released powerful small models together with the significant R1 release. DeepSeek launched not only the major big model with more than 680 billion parameters however also-as of this article-6 distilled models of DeepSeek R1. The models range from 70B to 1.5 B, the latter fitting on many consumer-grade hardware. Since February 3, 2025, the designs were downloaded more than 1 million times on HuggingFace alone.
DeepSeek R1 was potentially trained on OpenAI's information. On January 29, 2025, reports shared that Microsoft is investigating whether DeepSeek used OpenAI's API to train its models (an infraction of OpenAI's terms of service)- though the hyperscaler also added R1 to its Azure AI Foundry service.

Understanding the generative AI value chain


GenAI costs advantages a broad market value chain. The graphic above, based on research study for IoT Analytics' Generative AI Market Report 2025-2030 (launched January 2025), depicts key beneficiaries of GenAI spending across the worth chain. Companies along the worth chain consist of:


Completion users - End users include consumers and businesses that utilize a Generative AI application.
GenAI applications - Software suppliers that consist of GenAI features in their items or offer standalone GenAI software. This consists of business software business like Salesforce, with its focus on Agentic AI, and startups particularly focusing on GenAI applications like Perplexity or Lovable.
Tier 1 recipients - Providers of foundation models (e.g., OpenAI or Anthropic), model management platforms (e.g., AWS Sagemaker, Google Vertex or Microsoft Azure AI), data management tools (e.g., MongoDB or Snowflake), cloud computing and information center operations (e.g., Azure, AWS, Equinix or Digital Realty), AI specialists and combination services (e.g., Accenture or Capgemini), and edge computing (e.g., Advantech or HPE).
Tier 2 recipients - Those whose items and services frequently support tier 1 services, consisting of suppliers of chips (e.g., NVIDIA or AMD), network and server devices (e.g., Arista Networks, Huawei or Belden), server cooling technologies (e.g., Vertiv or Schneider Electric).
Tier 3 beneficiaries - Those whose product or services routinely support tier 2 services, such as companies of electronic style automation software application service providers for chip design (e.g., Cadence or Synopsis), semiconductor fabrication (e.g., TSMC), heat exchangers for cooling technologies, and electric grid technology (e.g., Siemens Energy or ABB).
Tier 4 beneficiaries and beyond - Companies that continue to support the tier above them, such as lithography systems (tier-4) essential for semiconductor fabrication makers (e.g., AMSL) or business that provide these providers (tier-5) with lithography optics (e.g., Zeiss).


Winners and losers along the generative AI worth chain


The rise of models like DeepSeek R1 indicates a possible shift in the generative AI value chain, challenging existing market dynamics and improving expectations for success and competitive advantage. If more models with similar abilities emerge, certain players may benefit while others face increasing pressure.


Below, IoT Analytics assesses the key winners and likely losers based on the innovations introduced by DeepSeek R1 and the more comprehensive trend toward open, cost-efficient models. This evaluation thinks about the possible long-term impact of such models on the worth chain instead of the instant effects of R1 alone.


Clear winners


End users


Why these innovations are positive: The availability of more and more affordable models will eventually reduce costs for the end-users and make AI more available.
Why these developments are unfavorable: No clear argument.
Our take: DeepSeek represents AI development that eventually benefits the end users of this technology.


GenAI application providers


Why these innovations are favorable: Startups developing applications on top of foundation models will have more options to pick from as more designs come online. As specified above, DeepSeek R1 is by far cheaper than OpenAI's o1 model, and though thinking designs are hardly ever used in an application context, it reveals that continuous breakthroughs and innovation improve the designs and make them more affordable.
Why these developments are negative: No clear argument.
Our take: The availability of more and less expensive designs will eventually reduce the expense of consisting of GenAI functions in applications.


Likely winners


Edge AI/edge calculating companies


Why these innovations are positive: During Microsoft's recent incomes call, Satya Nadella explained that "AI will be far more common," as more workloads will run locally. The distilled smaller sized models that DeepSeek launched together with the effective R1 design are little sufficient to run on many edge devices. While little, the 1.5 B, 7B, and 14B designs are likewise comparably powerful thinking designs. They can fit on a laptop computer and other less powerful devices, e.g., IPCs and industrial entrances. These distilled designs have already been downloaded from Hugging Face hundreds of thousands of times.
Why these innovations are negative: No clear argument.
Our take: The distilled models of DeepSeek R1 that fit on less effective hardware (70B and below) were downloaded more than 1 million times on HuggingFace alone. This shows a strong interest in releasing designs locally. Edge computing makers with edge AI solutions like Italy-based Eurotech, and Taiwan-based Advantech will stand to revenue. Chip companies that concentrate on edge computing chips such as AMD, ARM, Qualcomm, and even Intel, might also benefit. Nvidia also runs in this market sector.


Note: IoT Analytics' SPS 2024 Event Report (published in January 2025) explores the most recent commercial edge AI trends, as seen at the SPS 2024 fair in Nuremberg, Germany.


Data management companies


Why these developments are positive: There is no AI without data. To establish applications using open models, adopters will need a variety of data for training and throughout release, needing proper information management.
Why these innovations are negative: No clear argument.
Our take: Data management is getting more vital as the variety of various AI designs boosts. Data management companies like MongoDB, Databricks and Snowflake in addition to the respective offerings from hyperscalers will stand to profit.


GenAI providers


Why these developments are favorable: The unexpected emergence of DeepSeek as a leading gamer in the (western) AI environment shows that the complexity of GenAI will likely grow for some time. The higher availability of different models can cause more complexity, more need for services.
Why these developments are negative: When leading designs like DeepSeek R1 are available free of charge, the ease of experimentation and execution might limit the requirement for combination services.
Our take: As brand-new developments pertain to the marketplace, GenAI services demand increases as enterprises attempt to comprehend how to best utilize open models for their organization.


Neutral


Cloud computing suppliers


Why these developments are favorable: Cloud gamers rushed to consist of DeepSeek R1 in their design management platforms. Microsoft included it in their Azure AI Foundry, and AWS allowed it in Amazon Bedrock and Amazon Sagemaker. While the hyperscalers invest heavily in OpenAI and Anthropic (respectively), they are likewise model agnostic and allow hundreds of various models to be hosted natively in their design zoos. Training and fine-tuning will continue to take place in the cloud. However, as designs end up being more effective, less financial investment (capital investment) will be required, which will increase profit margins for hyperscalers.
Why these developments are unfavorable: More designs are expected to be released at the edge as the edge ends up being more powerful and models more effective. Inference is most likely to move towards the edge going forward. The expense of training cutting-edge designs is also anticipated to decrease further.
Our take: Smaller, more efficient designs are becoming more crucial. This lowers the need for effective cloud computing both for training and inference which may be balanced out by greater overall demand and lower CAPEX requirements.


EDA Software service providers


Why these innovations are favorable: Demand for brand-new AI chip designs will increase as AI workloads become more specialized. EDA tools will be critical for designing effective, smaller-scale chips tailored for edge and distributed AI reasoning
Why these innovations are unfavorable: The approach smaller sized, less resource-intensive models might reduce the need for creating cutting-edge, wiki.dulovic.tech high-complexity chips enhanced for huge information centers, potentially leading to reduced licensing of EDA tools for high-performance GPUs and ASICs.
Our take: EDA software providers like Synopsys and Cadence might benefit in the long term as AI specialization grows and drives demand for brand-new chip designs for edge, customer, and affordable AI work. However, the industry may need to adapt to moving requirements, focusing less on big information center GPUs and more on smaller, efficient AI hardware.


Likely losers


AI chip business


Why these innovations are positive: The presumably lower training expenses for models like DeepSeek R1 could eventually increase the overall need for AI chips. Some described the Jevson paradox, the concept that efficiency leads to more require for a resource. As the training and inference of AI models become more efficient, the need could increase as higher efficiency leads to reduce costs. ASML CEO Christophe Fouquet shared a similar line of thinking: "A lower expense of AI could imply more applications, more applications indicates more demand over time. We see that as a chance for more chips need."
Why these innovations are unfavorable: The presumably lower costs for DeepSeek R1 are based mainly on the requirement for less advanced GPUs for training. That puts some doubt on the sustainability of massive jobs (such as the recently revealed Stargate project) and the capital expenditure spending of tech companies mainly allocated for buying AI chips.
Our take: IoT Analytics research for its newest Generative AI Market Report 2025-2030 (released January 2025) discovered that NVIDIA is leading the data center GPU market with a market share of 92%. NVIDIA's monopoly identifies that market. However, that also shows how strongly NVIDA's faith is connected to the ongoing development of costs on information center GPUs. If less hardware is needed to train and release designs, then this could seriously deteriorate NVIDIA's development story.


Other categories related to information centers (Networking equipment, electrical grid technologies, electrical power service providers, and heat exchangers)


Like AI chips, models are likely to end up being less expensive to train and more efficient to release, so the expectation for further information center facilities build-out (e.g., networking devices, cooling systems, and power supply solutions) would decrease accordingly. If fewer high-end GPUs are needed, large-capacity information centers may downsize their financial investments in associated facilities, potentially impacting demand for supporting innovations. This would put pressure on companies that supply vital parts, most significantly networking hardware, power systems, and cooling services.


Clear losers


Proprietary model providers


Why these developments are positive: No clear argument.
Why these innovations are negative: The GenAI business that have actually collected billions of dollars of financing for their exclusive models, such as OpenAI and Anthropic, stand to lose. Even if they develop and launch more open models, this would still cut into the revenue flow as it stands today. Further, while some framed DeepSeek as a "side job of some quants" (quantitative analysts), the release of DeepSeek's effective V3 and after that R1 designs proved far beyond that belief. The question moving forward: What is the moat of proprietary model providers if cutting-edge models like DeepSeek's are getting launched for complimentary and end up being fully open and fine-tunable?
Our take: DeepSeek launched powerful models free of charge (for regional implementation) or extremely cheap (their API is an order of magnitude more inexpensive than comparable models). Companies like OpenAI, Anthropic, and Cohere will face progressively strong competitors from players that launch free and personalized innovative models, like Meta and DeepSeek.


Analyst takeaway and outlook


The introduction of DeepSeek R1 enhances a crucial pattern in the GenAI area: open-weight, cost-effective designs are ending up being viable competitors to proprietary options. This shift challenges market presumptions and forces AI service providers to reconsider their worth propositions.


1. End users and GenAI application service providers are the biggest winners.


Cheaper, top quality designs like R1 lower AI adoption expenses, benefiting both enterprises and consumers. Startups such as Perplexity and Lovable, which develop applications on structure models, now have more options and can considerably minimize API expenses (e.g., R1's API is over 90% less expensive than OpenAI's o1 design).


2. Most specialists agree the stock market overreacted, however the innovation is real.


While major AI stocks dropped sharply after R1's release (e.g., NVIDIA and Microsoft down 18% and 7.5%, respectively), many experts see this as an overreaction. However, DeepSeek R1 does mark an authentic advancement in expense performance and openness, setting a precedent for future competitors.


3. The dish for constructing top-tier AI designs is open, speeding up competition.


DeepSeek R1 has proven that releasing open weights and a detailed method is assisting success and deals with a growing open-source community. The AI landscape is continuing to move from a few dominant exclusive players to a more competitive market where brand-new entrants can build on existing advancements.


4. Proprietary AI providers deal with increasing pressure.


Companies like OpenAI, Anthropic, and Cohere must now separate beyond raw design efficiency. What remains their competitive moat? Some might move towards enterprise-specific services, while others might explore hybrid business models.


5. AI facilities providers deal with blended potential customers.


Cloud computing providers like AWS and Microsoft Azure still gain from design training but face pressure as reasoning transfer to edge gadgets. Meanwhile, AI chipmakers like NVIDIA might see weaker demand for high-end GPUs if more models are trained with fewer resources.


6. The GenAI market remains on a strong growth path.


Despite interruptions, AI spending is anticipated to expand. According to IoT Analytics' Generative AI Market Report 2025-2030, international spending on structure models and platforms is predicted to grow at a CAGR of 52% through 2030, driven by enterprise adoption and ongoing performance gains.


Final Thought:


DeepSeek R1 is not just a technical milestone-it signals a shift in the AI market's economics. The recipe for constructing strong AI designs is now more extensively available, guaranteeing greater competition and faster development. While exclusive designs need to adjust, AI application providers and end-users stand to benefit many.


Disclosure


Companies pointed out in this article-along with their products-are utilized as examples to showcase market advancements. No company paid or received preferential treatment in this short article, and it is at the discretion of the expert to pick which examples are utilized. IoT Analytics makes efforts to differ the companies and items pointed out to help shine attention to the numerous IoT and related innovation market gamers.


It is worth keeping in mind that IoT Analytics might have commercial relationships with some companies discussed in its posts, as some business license IoT Analytics market research study. However, for confidentiality, IoT Analytics can not divulge private relationships. Please contact compliance@iot-analytics.com for any questions or issues on this front.


More details and additional reading


Are you interested in finding out more about Generative AI?


Generative AI Market Report 2025-2030


A 263-page report on the enterprise Generative AI market, incl. market sizing & projection, competitive landscape, end user adoption, patterns, difficulties, and more.


Download the sample to read more about the report structure, choose definitions, choose data, extra information points, trends, and more.


Already a subscriber? View your reports here →


Related short articles


You may also have an interest in the following posts:


AI 2024 in evaluation: The 10 most notable AI stories of the year
What CEOs discussed in Q4 2024: Tariffs, reshoring, and agentic AI
The commercial software market landscape: 7 key statistics going into 2025
Who is winning the cloud AI race? Microsoft vs. AWS vs. Google


Related publications


You might also be interested in the following reports:


Industrial Software Landscape 2024-2030
Smart Factory Adoption Report 2024
Global Cloud Projects Report and Database 2024


Subscribe to our newsletter and follow us on LinkedIn to remain current on the most current trends shaping the IoT markets. For complete business IoT coverage with access to all of IoT Analytics' paid material & reports, consisting of devoted expert time, take a look at the Enterprise subscription.