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

R1 is mainly open, on par with leading proprietary models, appears to have been trained at significantly lower cost, and is more affordable to utilize in regards to API gain access to, all of which point to an innovation that may change competitive dynamics in the field of Generative AI.
- IoT Analytics sees end users and AI applications providers as the most significant winners of these recent advancements, while proprietary design companies stand to lose the most, based on value chain analysis from the Generative AI Market Report 2025-2030 (published January 2025).


Why it matters


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


Background: DeepSeek's R1 design rattles the markets


DeepSeek's R1 model rocked the stock exchange. On January 23, 2025, China-based AI startup DeepSeek launched its open-source R1 reasoning generative AI (GenAI) design. News about R1 quickly spread out, and by the start of stock trading on January 27, 2025, the marketplace cap for many major technology companies with large AI footprints had actually fallen significantly because then:


NVIDIA, a US-based chip designer and developer most understood for its information center GPUs, dropped 18% in between the market close on January 24 and the market 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 business specializing in networking, broadband, and customized ASICs, dropped 11% (Jan 24-Feb 3).
Siemens Energy, a German energy technology supplier that provides energy solutions for data center operators, dropped 17.8% (Jan 24-Feb 3).


Market participants, and particularly investors, responded to the story that the model that DeepSeek launched is on par with cutting-edge models, was apparently trained on just a couple of of GPUs, and is open source. However, since that initial sell-off, reports and analysis shed some light on the preliminary hype.


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DeepSeek R1: What do we know previously?


DeepSeek R1 is an affordable, advanced thinking design that equals top competitors while cultivating openness through publicly available weights.


DeepSeek R1 is on par with leading reasoning designs. The biggest DeepSeek R1 model (with 685 billion criteria) performance is on par and even better than some of the leading models by US foundation design service providers. Benchmarks show that DeepSeek's R1 model 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 significantly lower cost-but not to the level that initial news suggested. Initial reports indicated that the training expenses were over $5.5 million, but the true value of not just training however developing the design overall has been disputed considering that its release. According to semiconductor research study and consulting company SemiAnalysis, the $5.5 million figure is just one aspect of the costs, excluding hardware costs, the wages of the research and development team, and other elements.
DeepSeek's API prices is over 90% cheaper than OpenAI's. No matter the true expense to develop the model, DeepSeek is offering a more affordable proposition for utilizing 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 model.
DeepSeek R1 is an innovative model. The related clinical paper released by DeepSeekshows the approaches utilized to establish R1 based on V3: leveraging the mixture of specialists (MoE) architecture, support knowing, and very creative hardware optimization to create designs requiring fewer resources to train and likewise less resources to carry out AI inference, resulting in its abovementioned API usage expenses.
DeepSeek is more open than the majority of its competitors. DeepSeek R1 is available free of charge on platforms like HuggingFace or GitHub. While DeepSeek has made its weights available and provided its training approaches in its term paper, iuridictum.pecina.cz the original training code and data have not been made available for an experienced person to develop an equivalent design, consider defining an open-source AI system according to the Open Source Initiative (OSI). Though DeepSeek has actually been more open than other GenAI business, R1 remains in the open-weight classification when thinking about OSI requirements. However, the release stimulated interest in the open source community: Hugging Face has actually launched an Open-R1 initiative on Github to produce a complete reproduction of R1 by developing the "missing pieces of the R1 pipeline," moving the design to fully open source so anyone can replicate and develop on top of it.
DeepSeek released effective little designs along with the major R1 release. DeepSeek launched not just the major large model with more than 680 billion parameters however also-as of this article-6 distilled designs of DeepSeek R1. The designs vary from 70B to 1.5 B, the latter fitting on many consumer-grade hardware. As of February 3, 2025, the models were downloaded more than 1 million times on HuggingFace alone.
DeepSeek R1 was perhaps trained on OpenAI's data. On January 29, 2025, reports shared that Microsoft is investigating whether DeepSeek utilized OpenAI's API to train its designs (an infraction of OpenAI's terms of service)- though the hyperscaler likewise included R1 to its Azure AI Foundry service.

Understanding the generative AI worth chain


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


The end users - End users consist of consumers and businesses that use a Generative AI application.
GenAI applications - Software vendors that consist of GenAI functions in their products or deal standalone GenAI software. This consists of business software companies like Salesforce, with its focus on Agentic AI, and startups specifically focusing on GenAI applications like Perplexity or Lovable.
Tier 1 beneficiaries - Providers of foundation designs (e.g., OpenAI or Anthropic), design management platforms (e.g., AWS Sagemaker, Google Vertex or Microsoft Azure AI), information management tools (e.g., MongoDB or Snowflake), cloud computing and data center operations (e.g., Azure, AWS, Equinix or Digital Realty), AI experts and combination services (e.g., Accenture or Capgemini), and edge computing (e.g., Advantech or HPE).
Tier 2 beneficiaries - Those whose services and products routinely support tier 1 services, consisting of service providers of chips (e.g., NVIDIA or AMD), network and server equipment (e.g., Arista Networks, Huawei or Belden), server cooling technologies (e.g., Vertiv or Schneider Electric).
Tier 3 recipients - Those whose products and services frequently support tier 2 services, such as service providers of electronic design automation software companies 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) necessary for semiconductor fabrication makers (e.g., AMSL) or business that offer these suppliers (tier-5) with lithography optics (e.g., Zeiss).


Winners and losers along the generative AI worth chain


The increase of designs like DeepSeek R1 indicates a possible shift in the generative AI worth chain, challenging existing market characteristics and reshaping expectations for profitability and competitive advantage. If more designs with similar capabilities emerge, certain players might benefit while others deal with increasing pressure.


Below, IoT Analytics examines the key winners and likely losers based on the innovations presented by DeepSeek R1 and the more comprehensive trend towards open, affordable models. This assessment thinks about the possible long-term effect of such models on the value chain rather than the instant results of R1 alone.


Clear winners


End users


Why these developments are favorable: The availability of more and less expensive models will ultimately lower costs for the end-users and make AI more available.
Why these innovations are negative: No clear argument.
Our take: DeepSeek represents AI innovation that ultimately benefits completion users of this innovation.


GenAI application suppliers


Why these innovations are favorable: Startups constructing applications on top of structure models will have more alternatives to pick from as more designs come online. As specified above, DeepSeek R1 is by far cheaper than OpenAI's o1 design, and though thinking designs are rarely used in an application context, it reveals that continuous breakthroughs and development enhance the designs and make them more affordable.
Why these developments are unfavorable: No clear argument.
Our take: The availability of more and more affordable models will eventually reduce the expense of including GenAI features in applications.


Likely winners


Edge AI/edge calculating business


Why these developments are positive: During Microsoft's current revenues call, Satya Nadella explained that "AI will be a lot more ubiquitous," as more workloads will run locally. The distilled smaller sized models that DeepSeek launched along with the effective R1 design are little adequate to operate on numerous edge devices. While little, the 1.5 B, 7B, and 14B models are also comparably powerful reasoning designs. They can fit on a laptop computer and other less powerful devices, e.g., IPCs and industrial entrances. These distilled models have actually already been downloaded from Hugging Face hundreds of countless times.
Why these developments 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 reveals a strong interest in releasing designs in your area. Edge computing manufacturers with edge AI options like Italy-based Eurotech, and Taiwan-based Advantech will stand to earnings. Chip companies that focus on edge computing chips such as AMD, ARM, Qualcomm, or perhaps Intel, might also benefit. Nvidia likewise runs in this market segment.


Note: IoT Analytics' SPS 2024 Event Report (published in January 2025) dives into the current industrial edge AI patterns, as seen at the SPS 2024 fair in Nuremberg, Germany.


Data management services suppliers


Why these developments are positive: There is no AI without information. To establish applications using open models, adopters will require a huge selection of data for training and during release, requiring appropriate information management.
Why these innovations are negative: No clear argument.
Our take: Data management is getting more vital as the number of various AI models boosts. Data management business like MongoDB, Databricks and Snowflake in addition to the respective offerings from hyperscalers will stand to earnings.


GenAI services companies


Why these developments are positive: The unexpected emergence of DeepSeek as a leading gamer in the (western) AI environment shows that the intricacy of GenAI will likely grow for some time. The greater availability of various models can lead to more intricacy, driving more need for services.
Why these innovations are unfavorable: When leading models like DeepSeek R1 are available free of charge, the ease of experimentation and application might restrict the need for combination services.
Our take: As new innovations pertain to the marketplace, GenAI services need increases as enterprises try to comprehend how to best utilize open designs for their business.


Neutral


Cloud computing suppliers


Why these developments are positive: Cloud gamers rushed to include DeepSeek R1 in their model 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 make it possible for hundreds of different models to be hosted natively in their model zoos. Training and fine-tuning will continue to occur in the cloud. However, as models end up being more efficient, less investment (capital expenditure) will be needed, which will increase earnings margins for hyperscalers.
Why these innovations are unfavorable: More models are expected to be released at the edge as the edge becomes more effective and models more effective. Inference is most likely to move towards the edge moving forward. The cost of training cutting-edge designs is likewise expected to decrease even more.
Our take: Smaller, more efficient designs are becoming more crucial. This lowers the demand for effective cloud computing both for training and reasoning which might be balanced out by greater general need and lower CAPEX requirements.


EDA Software service providers


Why these developments are favorable: Demand for new AI chip styles will increase as AI work become more specialized. EDA tools will be important for creating efficient, smaller-scale chips tailored for edge and dispersed AI inference
Why these innovations are negative: The move towards smaller, less resource-intensive designs may minimize the need for creating cutting-edge, high-complexity chips optimized for huge data centers, potentially resulting in lowered licensing of EDA tools for high-performance GPUs and ASICs.
Our take: EDA software application companies like Synopsys and Cadence might benefit in the long term as AI expertise grows and drives demand for brand-new chip styles for edge, consumer, and inexpensive AI workloads. However, the industry might need to adjust to moving requirements, focusing less on large data center GPUs and more on smaller sized, efficient AI hardware.


Likely losers


AI chip companies


Why these developments are positive: The supposedly lower training costs for designs like DeepSeek R1 might ultimately increase the total demand for AI chips. Some referred to the Jevson paradox, the idea that effectiveness results in more require for a resource. As the training and inference of AI designs end up being more efficient, the need might increase as higher efficiency results in reduce costs. ASML CEO Christophe Fouquet shared a similar line of thinking: "A lower cost of AI might indicate more applications, more applications implies more need with time. We see that as an opportunity for more chips demand."
Why these innovations are negative: The apparently lower expenses for DeepSeek R1 are based mainly on the requirement for less cutting-edge GPUs for training. That puts some doubt on the sustainability of large-scale projects (such as the recently revealed Stargate task) and the capital expense spending of tech business mainly earmarked for purchasing AI chips.
Our take: IoT Analytics research study for its latest Generative AI Market Report 2025-2030 (released January 2025) found that NVIDIA is leading the data center GPU market with a market share of 92%. NVIDIA's monopoly characterizes that market. However, that also shows how highly NVIDA's faith is linked to the continuous growth of costs on information center GPUs. If less hardware is required to train and release designs, then this could seriously deteriorate NVIDIA's development story.


Other classifications related to data centers (Networking equipment, electrical grid technologies, electrical energy providers, and heat exchangers)


Like AI chips, models are most likely to end up being cheaper 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 services) would reduce accordingly. If less high-end GPUs are required, large-capacity information centers may scale back their investments in associated infrastructure, potentially impacting need for supporting technologies. This would put pressure on business that supply vital parts, most significantly networking hardware, power systems, and cooling services.


Clear losers


Proprietary design service providers


Why these developments are positive: No clear argument.
Why these developments are unfavorable: The GenAI companies that have collected billions of dollars of financing for their proprietary models, such as OpenAI and Anthropic, stand to lose. Even if they develop and launch more open designs, this would still cut into the profits flow as it stands today. Further, while some framed DeepSeek as a "side job of some quants" (quantitative analysts), the release of DeepSeek's powerful V3 and then R1 designs showed far beyond that belief. The concern moving forward: What is the moat of exclusive design providers if advanced designs like DeepSeek's are getting launched free of charge and end up being totally open and fine-tunable?
Our take: DeepSeek launched powerful designs free of charge (for local release) or really inexpensive (their API is an order of magnitude more economical than comparable models). Companies like OpenAI, Anthropic, and Cohere will face increasingly strong competition from players that launch totally free and personalized advanced designs, like Meta and DeepSeek.


Analyst takeaway and outlook


The emergence of DeepSeek R1 reinforces a crucial pattern in the GenAI space: open-weight, affordable models are becoming viable competitors to exclusive options. This shift challenges market assumptions and forces AI service providers to reconsider their value proposals.


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


Cheaper, high-quality models like R1 lower AI adoption costs, benefiting both business and consumers. Startups such as Perplexity and Lovable, which develop applications on structure models, now have more options and can significantly decrease API costs (e.g., R1's API is over 90% more affordable than OpenAI's o1 model).


2. Most specialists concur the stock exchange overreacted, however the innovation is genuine.


While major AI stocks dropped sharply after R1's release (e.g., NVIDIA and Microsoft down 18% and 7.5%, respectively), numerous analysts view this as an overreaction. However, DeepSeek R1 does mark an authentic breakthrough in cost effectiveness and openness, setting a precedent for future competition.


3. The recipe for developing top-tier AI models is open, accelerating competitors.


DeepSeek R1 has shown that releasing open weights and a detailed methodology is helping success and accommodates a growing open-source community. The AI landscape is continuing to move from a few dominant exclusive gamers to a more competitive market where brand-new entrants can build on existing developments.


4. Proprietary AI suppliers face increasing pressure.


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


5. AI facilities providers face mixed prospects.


Cloud computing service providers like AWS and Microsoft Azure still gain from design training but face pressure as reasoning moves to edge devices. Meanwhile, AI chipmakers like NVIDIA could see weaker need for high-end GPUs if more designs are trained with less resources.


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


Despite disturbances, AI costs is expected to broaden. 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 business adoption and ongoing effectiveness gains.


Final Thought:


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


Disclosure


Companies mentioned in this article-along with their products-are used as examples to display market developments. No company paid or got favoritism in this post, and it is at the discretion of the analyst to select which examples are used. IoT Analytics makes efforts to vary the companies and products mentioned to assist shine attention to the various IoT and related technology market gamers.


It is worth noting that IoT Analytics might have industrial relationships with some business pointed out in its short articles, as some companies accredit IoT Analytics market research study. However, for privacy, IoT Analytics can not reveal specific relationships. Please contact compliance@iot-analytics.com for any questions or concerns on this front.


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