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Vendors will continue to leverage their global manufacturing footprints and supply chain agility as they navigate tariffs

Key 1Q25 Devices Benchmark takeaways

AI PCs will remain a focus of vendors’ investment and product strategies over the next several quarters

OEMs are focused on developing PCs that incorporate neural processing units (NPU), which enable PCs to handle AI workloads locally. Over the next several quarters, the three major Windows PC OEMs — Dell Technologies (Dell), Lenovo and HP Inc. — will continue to develop and promote these AI PCs, releasing them in waves as the technology matures. As 2025 progresses, vendors will continue to invest in methods to differentiate their AI PC lineups from the competition, looking to gain share in the burgeoning subsegment to drive long-term revenue and margin growth within their devices businesses. Commercial devices will remain a particular focus for vendors as they build out their AI-enabled PC portfolios, due to stronger demand and those devices tending to carry higher average revenue per unit (ARPU) and stronger attach rates for services and peripherals.

Vendors outline plans to navigate the current tariff environment

During several devices vendors’ recent earnings calls, leaders were asked how they intended to navigate the current volatile tariff environment. A common thread across the responses was the confidence in each company’s ability to leverage its current business model and diversified supply chain to handle the impacts of tariffs. Dell and HP Inc. cited wide geographic reach as beneficial for navigating a challenging tariff environment, with the latter outlining plans to ensure by June that almost none of its devices sold in North America are manufactured in China. Dell and Lenovo both downplayed the effects of tariffs on their 1Q25 results, with Lenovo executives noting that the company is less concerned with the tariffs themselves than with their rapid onset. Moving forward, Lenovo will continue to leverage the in-house design and manufacturing capabilities underpinning its ODM+ model, as well as its wide global footprint.

Vendors expect the commercial PC refresh cycle to gain momentum in 2H25

For several quarters, cautious spending among both enterprises and SMBs has resulted in soft demand in the commercial PC market. During this period PC vendors have remained confident that the next major commercial PC refresh cycle, induced by factors including an aging install base and the upcoming end of Windows 10 support, will drive a rebound in the market. In 4Q24 Dell reported strong results among small and midsize businesses, something the company claimed has historically been an early indicator of a rebound in the overall commercial market. During its 1Q25 earnings call, Dell cited growing demand among both SMBs and enterprises, reporting that the refresh cycle is ramping up, although it is still “behind prior cycles.” Given these factors, TBR expects the PC segment will continue to build momentum throughout 2025, with the strongest growth coming in the second half of the year.

Google reported the strongest year-to-year devices revenue growth during 1Q25, while Apple continued to lead in terms of total revenue and gross margin

Graph: Devices Revenue Growth vs Devices Gross Margin (Source: TBR)

TBR expects PC services revenue to return to year-to-year growth during 2Q25, gaining additional momentum in 2H25 as PC unit shipments continue to climb

1Q25 conclusions on the PC Services segment

• The majority of PC services revenue continues to stem from premium support, warranty, asset recovery, security and other close-to-the-box attached services.
• PC services revenue is closely correlated with PC hardware revenue, but revenue recognition is often deferred. As a result, the trends in the overall devices market are slower to materialize in the PC services segment. For several quarters, segment revenue has declined as PC unit sales contracted sharply across the industry. During 1Q25, the segment declined slightly due largely to contracting revenue on the part of industry leader Dell.
• Growth on the commercial side of the market will be vital to performance in this segment going forward, as those PCs carry higher attach rates for services than their consumer counterparts. As the PC market gradually recovers throughout 2025, TBR believes the PC services segment will return to year-to-year growth.
• Vendors are currently focused on promoting PC services offerings designed to stimulate PC hardware refresh such as AI PC assessment services, Windows migration services and asset recycling services.

Google led benchmarked devices vendors in terms of growth during 1Q25, while Apple remained the largest vendor by total revenue

Graph: Smart Device Revenue Growth vs Smart Device Gross Margin (Source: TBR)

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Channel partners continue to invest in the expansion of services capabilities to bolster margins and win AI deals

Portfolio key trends

• Channel partners across the industry continue investing in the expansion of their services capabilities and cloud marketplace platforms to increase their hardware-adjacent revenue streams. While this initiative is driven in part by recent softness in the hardware market, software and services margins also tend to skew higher than hardware margins, and by taking a solutions-led approach, channel partners can drive deeper customer engagements and a higher level of differentiation in the market.
• TBR expects channel partners will increase go-to-market messaging around AI in 2025 as more industry-specific use cases develop, including by emphasizing that AI is built into their own service delivery platforms and by becoming certified to deploy and manage partners’ AI-based solutions.

Operations key trends

• As channel partners remain focused on growing their services revenue mix, they will continue to strategically rebalance headcount in roles such as sales and services delivery to expand their capabilities while maintaining profitability.
• Streamlining operations through digital transformation remains a key priority as channel partners seek to build a more agile business and buying experience while reducing costs. Examples include the proliferation of e-commerce portals, cloud marketplaces and analytics-based solution configuration. In growth areas, including cloud and services, digital transformation helps solution providers manage complex, multicloud environments and easily aggregate multivendor solutions.

Financial key trends

• Despite pockets of demand strength, such as in AI infrastructure, the overall channel partner hardware market has faced challenges over the past two years tied to cautious commercial spending and increasing competition for deals from both OEM partners as well as global systems integrators (GSIs) that are becoming increasingly interested in the resale of hardware to support the delivery of full-stack AI solutions.
• Multiple trends will likely help improve customer spending in 2025 including PC refresh in the commercial space and improving enterprise server demand.

M&A key trends

Acquisitions continued to be driven by channel partners’ desires to expand skill sets in cloud, cybersecurity and advisory services. TBR believes industry consolidation remains a pervasive trend among channel partners and will help companies acquire niche services capabilities. Additionally, channel partners will continue to target strategic acquisitions to expand their geographic footprints.

Services revenue is expected to see the most robust growth among all segments of the channel partner market

Segment trends

Relatively soft hardware demand acted as a headwind for most channel partners in 2024. However, looking ahead to 2025, TBR sees the continued ramp of the next major commercial PC refresh cycle synergizing with rebounding enterprise infrastructure spend, leading to forecasted aggregate growth of 4.1% among covered channel partners.

Demand for AI solutions remains elevated, and VARs are competing with both OEMs and GSIs to develop and deliver end-to-end AI solutions to customers. However, TBR believes VARs’ recent services and portfolio investments, coupled with the sheer scale of the AI opportunity, will allow all parties to benefit from the industry trend in 2025.

As VARs continue to invest in the expansion of their services capabilities to drive solutions-led engagements and as distributors further develop their cloud marketplace platforms, TBR believes channel partners’ margins will gradually expand over the next few years.

Generally speaking, channel partners with greater services emphasis fared best in 2024 amid softness in the hardware market

2024 corporate trends

Estimated aggregate corporate revenue among covered channel partners remained flat on a year-to-year basis in 2024 as soft commercial hardware demand offset growing software and services segment revenues. However, the industry’s performance represented an improvement over the group’s results in 2023, when estimated aggregate corporate revenue contracted approximately 4.6% year-to-year.

Among all covered companies, Arrow Electronics’ ECS segment delivered the strongest growth in 2024 due to the rising success of its ArrowSphere platform, which drove industry-leading software segment revenue growth during the period.

Behind Arrow Electronics’ ECS business, TBR believes WWT delivered the second-strongest revenue growth rate in 2024 due to the company’s ability to leverage its labs and integration centers to help customers develop and validate their own AI solutions. Looking ahead, TBR expects WWT’s labs and integration centers to act as a significant tailwind to the company as an increasing number of enterprise customers adopt AI solutions.

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AI data needs raise expectations about how services vendors and hyperscalers should be thinking about their relationships, with region-specific guardrails further testing joint GTM durability

Key trends
Hyperscalers’ dispersion across the globe requires these vendors, along with their services partners, to carve out new operating models that prioritize both go-to-market success and adherence to local requirements. This balance translates into a case-by-case — or rather, country-by-country — approach with local partnerships being the common thread. This is especially true for hyperscalers competing for local market share — particularly in the European Union (EU) — to the extent of relinquishing operational control (e.g., Microsoft Bleu), creating a direct opportunity for regional system integrators (SIs) and local infrastructure operators. While global SIs (GSIs) are also in contention, they must account for these vendors as they build out local presence, along with their commercial, staffing and partner models. Meanwhile, regional legislation is pushing hyperscalers to commit to investment pledges to ensure business continuity, with implications for services partners and buyers, further testing the limits of their relationships.

Go-to-market strategy
Enterprise buyers are becoming increasingly conflicted in their expectations of how vendors can best support their technology needs. When it comes to interoperability, many customers look to leverage third-party vendors, and a smaller percentage expect their cloud vendors to address these concerns directly. At the same time, buyers continue to identify technology expertise as a key skills gap in vendors’ value proposition across regions. These dynamics are further amplified in vendors’ regional go-to-market strategies, especially when it comes to accounting for the role of AI and niche vendors that bring specialized knowledge. In a nutshell, vendors cannot rely on a one-size-fits-all AI ecosystem strategy across regions. Success will require region-specific approaches: IP-led initiatives in APAC, orchestration frameworks in Europe, and startup-centric marketplaces in the Americas. All must be underpinned by interoperable APIs and strong governance to help IT services providers capture and monetize local demand. Executing against such expectations while continuing to rely on a traditional labor-arbitrage model will test professional services firms’ readiness to transform their own operations while maintaining trust with hyperscalers, which continue to explore the opportunity to drive professional services revenue by simplifying the sales process and marketplace through the use of agentic AI.

Vendors
In addition to launching bespoke operating models and investing in local infrastructure, hyperscalers are investing at the infrastructure layer to support workload portability and management capabilities that help customers adhere to sovereignty regulations. Google Distributed Cloud is probably the most sovereignty-forward example, but similar comparisons can be made for both Microsoft and Amazon Web Services (AWS). Security is another critical area hyperscalers are investing in, whether it is Microsoft’s new deputy chief information security officer (CISO) role for Europe or Google’s continued effort to expand Mandiant’s assets to prevent breaches. Orchestrating these evolving offerings into a cohesive IT estate will be an opportunity for services vendors, especially when deciding how to configure them in a way that is suitable for country-specific needs. Services — including migration, implementation, consulting and advising — all play a role in navigating the increasingly complex regulatory and security environment, requiring broad hiring in the EU, where the opportunities are the greatest.

Data location will remain a leading barrier to cloud adoption, but interoperability and breaking down data barriers across platforms will present the bigger opportunity for services vendors

As highlighted in TBR’s 2H24 Cloud Infrastructure & Platforms Customer Research, data location ranked as the second-highest cloud pain point after security, with 40% of respondents expecting their cloud vendors to directly address these concerns — largely due to vendors doing a better job of making customers aware of the various data center hosting and encryption options. Conversely, when it comes to interoperability, many customers will leverage third-party vendors, and a smaller percentage expect their cloud vendors to address these concerns directly. This speaks to both the skills that services-led firms have amassed across multiple technology platforms and the high degree of lock-in the hyperscalers still create across their infrastructure. That said, from a technology perspective, vendors are doing a better job of integrating their offerings, particularly in the area of agentic AI, which necessitates more robust data sharing. As more open frameworks, including Google’s A2A (Agent2Agent), mature and become enterprise-ready, they could create a needed level of standardization that GSIs can leverage to build new agents alongside their ISV partners. Deloitte’s partnership with Google Cloud to build ServiceNow-specific agents on A2A is a good example.

“It [sovereign cloud] helps, significantly helps. A completely separated, air-gapped environment different from the regular public cloud itself. And Switzerland, by the way, is a great example. AWS put up two regions in Switzerland. It’s a tiny country. But even then, they had two regions to satisfy this condition of resilience, etc.” — Managing Director (Firmwide) & Chief Data Architect, Financial Services

Cloud vendor insights excerpt

Microsoft’s Cloud Services & Ecosystem Strategy

Microsoft Cloud’s Latest Ecosystem Initiative (Source: TBR 1H25)

Microsoft Cloud’s Estimated Ecosystem Statistics for 1H25 (Source: TBR)

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CSPs have an opportunity to capture meaningful value from AI, but realizing this opportunity requires action and investment

Most CSPs look for quick-hit ROI from GenAI; there is still hesitancy to commit to larger-scope AI initiatives that require significant upfront investment

Leading CSPs are all dabbling with GenAI, with some use cases already commercially scaling, especially in customer care and BSS.

CFOs at some leading CSPs are getting directly involved in AI programs to give visibility to these initiatives and ensure they are paying off, looking for quick ROI.

So far, CSP managers like what they are seeing and are hopeful about the prospects of AI delivering significant outcomes, but this hope has yet to translate to large-scale investments tied to broad transformations. Rather, CSPs are focused on more tactical, smaller-scope solutions that address specific pain points or that promise fast ROI.

TBR believes it will take some time for most CSPs to evolve this investment behavior, but some other considerations also need to be factored into the equation, such as uncertainty about governance, regulation and data efficacy. For example, AI’s effectiveness correlates closely with the volume and quality of the data input into the model. The reality is that data inside CSP organizations are usually highly disaggregated in silos and on legacy systems, which poses a major challenge to undergoing large-scale AI transformations.

Realizing the $170 billion total opportunity TBR estimates AI presents the telecom industry by 2030 requires CSPs to act differently

AI presents a once-in-a-generation opportunity for the telecom industry to achieve two key objectives: generate new revenue and reduce costs.

However, there is real risk that most CSPs globally will miss out on the AI opportunity due to cultural (behavioral) and regulatory encumbrances, such as long decision cycles, an unwillingness to invest what it takes to win and general risk aversion. These encumbrances, which are endemic to the telecom industry, have resulted in CSPs largely missing out on every major opportunity in the last two decades (e.g., cloud computing opportunity, video streaming opportunity, digital advertising opportunity), many of which were won by the hyperscalers that were willing and able to take these risks.

Though leading CSPs have been investing in AI, TBR notes that most of these investments seem to be myopically focused on quick-hit wins, which is acceptable in the short term, but true opportunity capture will be contingent on broader-scope initiatives, coupled with upfront investment.

CSPs cannot afford to miss out on another opportunity, especially one that has such transformational qualities as AI. Getting AI right should be of paramount concern to CSPs, as competitors that transform with AI may obtain unapproachable differentiation compared to CSPs that do not invest in AI. Said differently, CSPs that do not get AI right may not be competitive anymore, leading to longer-term questions about their viability.

CFOs need to evangelize AI across their organizations to seize this critical opportunity. True digital transformation with AI will require significant upfront investment, and the payoff of some investments take a longer time to realize than others. CFOs need to balance investment levels with outcomes, but more support is needed from the top to approach AI in a broader manner.

TBR notes that of the $170 billion CSP AI opportunity by 2030, an estimated 50% is likely to be realized in APAC, where there are many CSPs aiming to take a leadership role in the AI market. The lack of domestic hyperscalers (outside of the U.S. and China) and government desire to have sovereignty over technologies of national importance gives CSPs an opportunity to fill in the gaps in the market. China-based CSPs are already diving headfirst into the AI opportunity, and they are likely to generate billions of dollars in new revenue and save billions of dollars in costs from AI by 2030.

Telcos have a data usability problem, which could slow the pace of AI adoption in their businesses; addressing this issue requires significant additional investment

CSPs have a data preparedness problem, which is an underestimated issue and will be costly to rectify

The efficacy of and value provided by AI are contingent on the quality, type and volume of data the AI model is trained on. It is therefore imperative that CSPs ensure their data is ready for AI and that the data is managed and governed in a sustainable way. This is especially pertinent now that GenAI can train on a broad range of structured and unstructured data.

The reality is that CSPs’ data does not reside in one data lake. Rather, it is highly disaggregated and siloed across the organization and, in some cases, may be incomplete. There are also restrictions (e.g., regulatory, privacy) and security considerations around certain types of data, which hinder the utility and accessibility of the data, creating another major challenge that companies will have to address before they can appropriately leverage AI.

Individual CSPs have access to petabytes of data, but most of this data resides in legacy systems and/or is subject to other restrictions, creating challenges around pooling and preparing the data to be leveraged for AI.

To rectify this issue, CSPs will need to clean up their technical debt and assemble their data in a single, unified platform. This horizontal data layer will be required to run and realize AI-driven outcomes at scale. Most CSPs will likely need assistance with this task, opening up opportunities for the vendor community.

Addressing this issue will also require significant investment and human resources, and TBR believes CSPs that are in the initial stages of their GenAI initiatives have underestimated or overlooked the problem.

TBR believes this issue could extend CSPs’ timelines for AI commercialization. Vendors and hyperscalers will try to mitigate the data issue by training AI models on their own datasets, which could also be fed by partners’ data, but there is a risk that this will not be sufficient and that AI models will need to be tuned to a specific CSP’s needs to realize desired outcomes. TBR also believes it will be common for stakeholders in the telecom ecosystem to be protectionist regarding their data, not wanting to (or being able to) share their data for fear of regulatory reprisals and a nullification of competitive advantages.

Sovereign cloud requirements have the potential to be a meaningful revenue driver for CSPs in select regions and countries

Countries (e.g., China, Saudi Arabia, Qatar, United Arab Emirates, South Korea) and alliance entities (e.g., NATO) that are more impacted by geopolitics and/or have strong data privacy rules and regulations (e.g., European Union [EU] member countries) are looking into establishing national AI infrastructure (e.g., data centers), also referred to as sovereign cloud.

Sovereign clouds would theoretically be set up and managed by domestic players for national security reasons and could be an area of opportunity for CSPs that reside in those countries or regions, whereby the CSP might host the cloud and provide some value-added services to manage those environments on behalf of the government and related entities.

However, TBR ultimately believes it is more likely that U.S. hyperscalers will participate in some way in the AI value chain for most countries given the broad scope of their involvement and dominance in the global AI ecosystem, despite intentions for national sovereignty. This includes CSPs partnering with hyperscalers to jointly develop, operate and manage cloud resources in-country, as evidenced by Orange’s use of Microsoft’s cloud and AI technology for its sovereign cloud joint venture with Capgemini (called Bleu) in France.

TBR estimates the potential annual AI-related opportunity for CSPs will reach $170B by 2030, approximately 53% of which is new revenue and 47% is cost efficiencies

CSPs have been largely sidelined from the new revenue opportunity presented by AI since the emergence of GenAI in 4Q22, but this is starting to change, evidenced by significant deals won by Lumen and Zayo to provide transport between data centers for AI workloads. There are also some green shoots of demand for hyperscalers to leverage CSPs’ network facilities (e.g., wire centers and central offices) and other real estate assets to colocate AI infrastructure closer to end users, as evidenced by efforts being made by Verizon and AT&T.

All CSPs that are investing in AI currently expect to reap cost efficiencies from the technology. The new revenue opportunity is more nuanced and is CSP- and market- specific in nature. APAC-based CSPs are likely to be the largest beneficiaries of new revenue from AI due to government protections, stimulus and cultural orientations toward early adoption of emerging technologies.

Total Annual Potential Value of AI to CSPs by 2030 (Source: TBR)

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Interest in AI capabilities has not waned as enterprises view the technology as critical to long-term competitive positioning

The buzz around GenAI persists as enterprise interest is leading to adoption. Yet it is still early days, and many enterprises remain in exploration mode. Some use cases, such as data management, customer service, administrative tasks and software development, have already moved from the proof-of-concept stage to production. Still, the exploration phase of AI adoption will be a slow burn as enterprises seek opportunities beyond these low-hanging fruit. As seen in the graph to the right, most enterprises are evaluating AI qualitatively, forgoing quantitative measures to keep up with peers based upon the assumption that the technology will bring transformational improvement to business operations.

Source: TBR 2H24

Reasoning models excel at performing complex, deterministic tasks, and have become the most popular models at the back end of agentic AI

The capability improvement brought by the iterative inferencing process has made reasoning models the focal point of frontier model research. In fact, most of the models sitting atop established third-party benchmarks are reasoning models, except for OpenAI’s GPT-4.5, which the company stated would be its last nonreasoning LLM. Put simply, the difference in output quality is too pronounced to ignore, especially regarding complex, deterministic tasks. As seen in the graph, reasoning models outperform their nonreasoning predecessors across the board, with the greatest distinction appearing in coding and math benchmarks. The strength in complex, deterministic tasks makes reasoning models particularly adept at powering agentic AI capabilities, offering a wider range of addressable use cases and greater accuracy. In addition, reasoning frameworks can be leveraged at any parameter count, with available reasoning models ranging from fewer than 10 billion parameters to more than 100 billion.

As SaaS vendors continue to build proprietary, domain-specific SLMs [small language models] to power their agentic capabilities, incorporating reasoning frameworks will be an important part of their development strategies. Although the capabilities of reasoning models are impressive, the models bring new challenges and are not necessarily the best choice for every application.

Simple content generation and summarization, for instance, do not necessarily require iterative inferencing. Moreover, the greater compute intensity caused by repeated processing at the transformer layer will compound existing challenges to scaling AI adoption. Not only will these models be more expensive to run for the customer, but they will also exacerbate the persistent supply shortages facing cloud infrastructure providers. Microsoft has noted infrastructure constraints as a headwind to AI revenue growth in the past several quarters, and the emerging need for test-time compute adds to these infrastructure demands. As discussed in TBR’s special report, Sheer Scale of GTC 2025 Reaffirms NVIDIA’s Position at the Epicenter of the AI Revolution, NVIDIA’s CEO Jensen Huang stated that reasoning AI consumes 100 times more compute than nonreasoning AI. Of course, this was a highly self-serving statement, as NVIDIA is the leading provider of GPUs powering this compute, but we are dealing with magnitudes of difference. For the use of reasoning models to continue scaling, this high compute intensity will need to be addressed.

SaaS vendors will need to get on board with the new Model Context Protocol to ensure customers can use their model of choice

SaaS vendor strategy assessment

From a strategic positioning perspective, TBR does not expect the rising popularity of the Model Context Protocol to have an outsized impact, primarily because we anticipate all application vendors will adopt the framework to ensure customers can leverage the model of their choice. Furthermore, cloud application vendors are positioned to benefit from the standardization of API calls between models and their workloads. Through a standardized API calling framework, these vendors will be better positioned to drive cost optimization and improve workload management for embedded AI tools.

Recent developments

The Model Context Protocol is becoming the standard: The idea of the Model Context Protocol (MCP) has been steadily gaining popularity following its release by Anthropic in November 2024. At its core, MCP aims to address the emerging challenge of building dedicated API connectors between LLMs and applications by introducing an abstraction layer that standardizes API integrations. This abstraction layer — commonly referred to as the MCP server — would establish a default method for LLM function calling, which software providers would need to incorporate into their applications to access LLMs.

This standardization offers several benefits for model vendors, such as eliminating the need to build individual connectors for each service and promoting a modular approach to AI service integration, potentially unlocking long-term advantages in areas such as workload management and cost optimization.

For SaaS vendors, there is little reason to resist the shift toward MCP, and its growing popularity may make adoption inevitable. Application vendors like Microsoft and ServiceNow have already begun implementing the protocol by establishing MCP servers for the Copilot suite and Now Assist, respectively, and TBR expects other vendors to follow.

It is important to recognize, however, that this approach better suits vendors taking a model-agnostic stance — meaning they aim to empower enterprises to use any LLM to automate agentic capabilities. A possible exception lies with vendors that are less model-agnostic. For instance, Salesforce’s emphasis on proprietary models reduces the need for MCP and favors the company’s focus on native connectors between Customer 360 workflows and xGen models.

Ultimately, TBR expects Salesforce to adopt MCP, but there is an important distinction in how different SaaS vendors may approach standardization. Today, the BYOM [bring your own model] philosophy remains a priority for Salesforce, but if the company were to eventually push customers to use its proprietary models exclusively with Customer 360, its commitment to MCP could be deprioritized in favor of tighter customer lock-in.

Google enhances AI capabilities with the launch of Gemini 2.5 Pro, revolutionizing search functionality, healthcare solutions and multimodal content generation

Google remains differentiated in the AI landscape through the deep integration of its proprietary models across a broad product ecosystem, including Search, YouTube, Android and Workspace. Although many competitors focus on niche capabilities or open-source development, Google positions Gemini as a comprehensive, multimodal foundation model designed for widescale consumer and enterprise adoption. Google’s infrastructure, proprietary TPUs (Tensor Processing Units), and access to vast and diverse data sources provide a significant advantage in training and deploying next-generation models. Gemini 2.5 Pro is a testament to this strength, offering the best performance and largest context window available on the market. Although TBR expects the top spot to continue exchanging hands, we believe Google’s models will remain among the frontier leaders for years to come.

OpenAI advances AI development with GPT-4.5, cutting-edge agent tools and a premium ChatGPT Pro subscription to expand capabilities and improve user experiences

OpenAI is the most valuable model developer in the market today, largely due to the company’s success in productizing its models via ChatGPT. The mindshare generated by ChatGPT is benefiting the company’s ability to reach custom enterprise workloads, though OpenAI must be mindful of the widening gap in price to performance relative to peers. From a sheer performance perspective, TBR believes the company’s emphasis on securing compute infrastructure via the Stargate Project, as well as its ongoing partner initiatives to gain access to high-quality training data, will ensure its models remain near the top of established third-party benchmarks over the long term.