TBR perspective

If the key takeaway from Mobile World Congress 2025 (MWC25) in Barcelona, Spain, could be boiled down to one word, that word would be: warning.
 
Though warnings for the telecom industry have been trumpeted ever since the LTE cycle underwhelmed and failed to bring significant new revenue to telcos, TBR notes that the warning has reached a new level and that the telecom industry faces arguably the most uncertain period in its 150-year history.
 
There is real concern that endemic, chronic issues that have been challenging the telecom industry for many years could be compounded by the agenda and policies of the new U.S. administration, which has created global uncertainty regarding geopolitics, the strength of nation-state alliances, trade policy, economic development and other key areas, ratcheted up to levels not seen since the Cold War.
 
Amid this uncertainty, the modus operandi for telcos and their network vendors is to shrink back to basics and cut costs. With catalysts for sustainable, ROI-positive new revenue for telcos remaining unclear, the will to spend more on capex is simply nonexistent. Rather, telcos are becoming more fixated on cost reduction, especially through AI and M&A.
 
Using history as a guide, deep structural change and regulatory reform, such as that yearned for by the telecom industry, typically only occur in times of monumental crisis, such as severe macroeconomic deterioration, which tends to force governments into action and drive broader restructuring and changes at organizations.
 
For example, two of the most significant, large-magnitude industrial changes across major societies in the past 150 years occurred during the Great Depression of the 1930s, which reshaped labor and industrial dynamics, and the Great Recession of 2007-2009, which reshaped the financial services industry and real estate market. Telecom will, unfortunately, need a similar economically driven crisis to bring about the changes that the industry desperately needs.
 
The telecom industry might finally be getting the fundamental, transformational change it needs, and President Donald Trump may well be the catalyzing agent for this change.
 

Impact and Opportunities

Europe risks reaching a point of no return and dragging its telecom industry down with it

Europe is regressing, and its way of life is threatened unless drastic change is implemented. This was a frequently discussed theme at MWC25. Although Europe has a leading educational system and talented workforce, regulation and taxation in the region have become so restrictive that they are causing chronic disinvestment, brain drain (mostly to the U.S.) and capital flight (again, mostly to the U.S.).
 
There has also been an acceleration in the decline of birth rates, which portends future, structural headwinds for European society. Though it is not too late to bring Europe back from the brink and reassert the continent as a major powerhouse in the global economy, the window to fix the situation is closing.
 
For example, TBR’s research suggests Europe is approximately five to seven years behind other first-world economies in key technological areas, such as 5G, cloud, AI and quantum computing, and the gap is widening as the pace of technological change accelerates.
 
How Europe responds to the impact of the Trump administration will solidify the bloc’s future. Broad-based, structural reform is required to ensure the highest probability of success, with European Union (EU) member states acting more like one, unified bloc that is leveraging the best that each state offers.
 
As part of this, a regulatory overhaul is required, with increased domestic investment and less restrictive encumbrances to economic development enacted. Additionally, M&A, especially as it pertains to nationally important entities, such as telecom operators, must be allowed in order to attain a competitive level of scale and increase the health and financial well-being of these sectors.
 
There are simply too many communication service providers (CSPs) in Europe (between 100 and 200, depending on how operating companies [OpCos] and subsidiaries are counted), most of which are sub-scale, impacting their ability to innovate and invest, especially on the world stage.
 
With three CSPs now remaining in most major countries, Europe’s telcos are minnows in a sea of big fish. More years of the same will further constrict and make the telecom industry even more unhealthy. Structural reform must happen now.
 
Relevant documents pertaining to the future of the EU, such as the Draghi and Leiter reports, were frequently mentioned at MWC25, and many European influencers and decision makers are using those documents to draw ideas from and promote change.
 
A potential silver lining for Europe is that Trump’s new world order may usher in a renaissance for the continent, whereby the EU bands together in solidarity and cooperation to address its weaknesses and focus on becoming competitive again on the world stage.
 
Though the deck is stacked against Europe due to the sheer number and scope of problems that the continent faces, recent events that coincided with the timing of MWC25, such as Germany’s new stance on debt and defense spending, could shake the continent awake from its multidecade slumber and be a watershed moment for structural change.

Growth remains the No. 1 problem for the telecom industry, still with no viable solution

When adjusting for inflation, the telecom industry is shrinking and has been for some time. Though mobile has been offsetting chronic weakness in legacy wireline businesses, even now mobile is exhibiting maturity from a global perspective, with industry-level revenue growth rates flatlining.
 
While it is true that fixed wireless access (FWA) is a new driver of revenue growth, thanks to 5G, the size of the pie is likely to continue shrinking on an inflation-adjusted basis as CSPs fight to attract and retain new subscribers and engage in pricing tactics, such as offering discounts for bundling (aka convergence) to achieve this goal.
 
The reality is that network APIs, edge computing (including AI inferencing at the edge), network slicing and other areas frequently viewed as growth opportunities for telcos over the past few years are still not yielding substantial revenue, and the revenue that is derived from these areas is more cosmetic (i.e., revenue positive but lacking profitability and scale) than genuine (i.e., ROI positive) in nature.

AI gains traction and becomes more sophisticated

One area in which leading telcos are making progress is applying generative AI (GenAI), and now agentic AI, to boost productivity and reduce costs. TBR notes that the increased level of sophistication of AI solutions demonstrated at MWC25 shows significant progress since MWC24, a bright spot for the industry.
 
Customer care and billing remain the most popular domains to apply AI currently, and these areas represent low-hanging fruit, but sales, marketing and, increasingly, the network domain will all be impacted by AI as well. Many use cases and business cases for AI and GenAI in the telecom industry make logical sense and have the potential to produce profoundly significant business outcomes, especially related to cost efficiency.
 
Technological readiness for and commercialization of AI and GenAI are in process, and much more innovation is in store. Additionally, AI will take on increased importance as telcos navigate the deteriorating geopolitical and economic environment and look to sustain their bottom lines.

5G standalone (SA) adoption remains extremely sluggish, and the gap is widening between leading operators and late adopters

The cost and complexity associated with deploying a 5G core, coupled with the lack of a clear ROI from having a 5G core, continues to stifle the pace of commercial deployment of the technology. While approximately 326 CSPs globally have deployed 5G to date, just 123 have officially signed deals to purchase and deploy a 5G core, and about half of those (62 out of the 123 operators) have not actually begun commercial deployment.
 
Additionally, of the 61 CSPs that have deployed a 5G core, most are not what some consider a “complete 5G network,” meaning the architecture utilized for the 5G core is not cloud-native. Given that a 5G core is a prerequisite for network slicing, deploying forthcoming 5G Advanced features, and using other key features associated with 5G, such as for B2B use cases, this means most CSPs that have deployed 5G to date are still not able to participate in these nascent areas.
 
CSPs cannot hope to capture revenue from network slicing, AI inferencing at the edge, or forthcoming use cases enabled by 5G Advanced if they do not invest in the infrastructure needed to provide these services at scale and with low latency. Most CSPs’ cautious capex strategies are hindering their future revenue growth opportunities and risk ceding the value capture from these services to hyperscalers, most likely, or to their own incumbent vendors that elect to bypass the CSP middleman.

FWA is starting to get the attention it deserves from telcos but still has significant untapped potential

TBR continues to believe that FWA represents one of the biggest opportunities for mobile network operators to monetize their 5G investments and drive scalable revenue growth. Though CSP deployment of 5G FWA continues to grow, most CSPs keep underestimating the potential of the technology, likely because FWA ties up a lot of spectrum resources for relatively low average revenue per user (ARPU). There is also an embedded industry bias toward full fiber, though TBR believes this mindset has begun to soften as FWA has proved its staying power.
 
Technological innovations currently available (e.g., multiband carrier aggregation, beamforming, extended range millimeter wave, non-line-of-sight antenna design, New Radio Unlicensed [NR-U], integrated access and backhaul [IAB], silicon advancements) are likely to bring dramatic improvements in network performance, energy efficiency, and the usability of spectrum to support services such as FWA at large scale. FWA customer-premises equipment (CPE) is also becoming more cost effective to buy and deploy.
 
TBR maintains that 5G FWA should be thought of as wireless fiber and that the notion of having to deploy fiber to every business and residential premises globally is not only economically unfeasible but also unrealistic from a pure time-to-market standpoint to meet digital equity initiatives. 5G FWA can address these challenges and is a far more realistic and economically feasible technology to help the world bridge the digital divide, bring more competition into the global broadband market and support new use cases.
 
Changes to the Broadband Equity, Access, and Deployment (BEAD) Program and other stimulus programs in the U.S. to legitimize FWA (and satellite broadband) is a great step forward in leveraging fiber alternatives without sacrificing significant performance and other benefits that fiber-to-the-premises provides. More than 90% of households and most businesses globally do not need more than 100Mbps of broadband speed, mostly thanks to advancements in video compression technologies.
 
Over the next few years, TBR believes the industry’s perception of FWA will shift from being viewed as an “intermediate, good enough” solution pending fiber rollout to a legitimate fiber alternative, and that ultimately up to 50% of global premises (residential dwellings and businesses) could be addressed with FWA, with the balance being served by Fiber-to-the-X (FTTx) and satellites.

Satellite industry enters the telecom hen house

Though there are significant benefits for mobile network operators (MNOs) (and their customers) to partner with satellite connectivity providers, there is also a growing undercurrent of concern. Led by Starlink, which had a strategically located booth in one of the prominent courtyards of the MWC venue, telco leaders are starting to realize that satellite operators pose a legitimate competitive threat.
 
Non-terrestrial networks (NTNs) are advancing quickly, with a broader range of smartphones now off-the-shelf compatible with satellite networks, just as they are with terrestrial networks. From a services perspective, satellite connectivity has advanced from basic SOS messaging services to full text support, with voice and data services on the road map for later this decade, all of which can be utilized by the latest popular smartphones.
 
Satellite broadband is even starting to compete against terrestrial broadband, especially xDSL and FWA, an inflection point made possible by the steady reduction in satellite CPE costs, which historically made satellite connectivity too expensive to be an economically feasible alternative to terrestrial broadband options, as well as significant increases in downlink speeds.
 
Ultimately, according to TBR’s research, it is conceivable that up to 100 million premises globally could be supported by satellite broadband providers, with Starlink likely to remain the frontrunner in the ecosystem.

Defense industry poised to become a major growth area for network vendors

The Russia-Ukraine and Israel-Hamas wars have demonstrated how warfighting has evolved with technology, prompting a reassessment of military strategy, assets and the production of military-related equipment, especially by the U.S. Department of Defense and NATO members in Europe. Additionally, with the U.S. now retreating from Ukraine, Europe is forced to revitalize its own military industrial complex. All of this incurs more spend on military and defense, with mobile technology set to be a prominent feature of new systems and solutions.
 
5G, 6G, private cellular networks, edge computing and AI will all be leveraged in some way in modernized military solutions. Of the more than $13 trillion that it is estimated will be spent on defense globally through the rest of this decade, TBR expects many billions of dollars of this amount to flow to the telecom industry, with Nokia, Ericsson and a broad range of other vendors as well as operators providing the bulk of this equipment and services.

Conclusion

Telecom operators remain unhealthy, and the prognosis is deteriorating. One of the first things the industry needs is a comprehensive reassessment of the regulatory environment to give telcos some breathing room and flexibility to accelerate their digital transformation journeys. A catalyzing event, which usually stems from crises, is needed to force the telecom ecosystem to change, and for regulators to create a friendlier economic and competitive environment.
 
TBR maintains that the telecom industry will look very different by the end of this decade and that significant consolidation will need to take place to create more financially healthy and sustainable telcos. It is possible that Trump and his unconventional policies will be the catalyzing agent to usher in this new phase of telecom industry evolution.

TBR attended two virtual Snowflake events in January, AI + Data Predictions 2025: How Operationalizing AI Will Drive Technical Advances and Leadership Challenges on Jan. 16, and Snowflake GenAI Day on Jan. 22. During the events we heard from Snowflake leaders, including Chase Ginther, principal architect AI/ML, and Caleb Baechtold, principal AI architect, Applied Field Engineering. These discussions, coupled with keynote speakers, breakout sessions, and TBR’s ongoing analysis of Snowflake’s strategy, underscore the company’s ongoing transformation from a data warehouse innovator to a leader in integrated data and AI platforms.

Snowflake in transition: Scaling AI through a data-first approach

Snowflake’s AI strategy is centered on a data-first approach that leverages the company’s data management strengths to drive development of advanced AI capabilities. Three key aspects of Snowflake’s strategy help it stand out in a highly competitive data and AI platform market.
 
First, the company is leveraging its origins as a data warehouse provider to offer a fully integrated data and AI platform. By prioritizing the management of structured and unstructured data, Snowflake enables AI-driven analytics, machine learning (ML) workflows and advanced processing within a unified ecosystem. Second, Snowflake is using advanced technologies to scale its AI capabilities, including GPUs to accelerate ML workloads; Snowflake Container Services (SCS) for efficient model deployment; and Snowpark, which enables seamless AI development using SQL, Python and Java. Third, Snowflake is enhancing its ecosystem through open-source AI collaborations via Cortex, integrating models from Meta, Hugging Face and Mistral to power natural language processing, predictive analytics and automation — all within a secure, data-centric framework. By prioritizing data as a foundation for AI, Snowflake enables efficient scaling while ensuring security, performance and governance within its ecosystem.
 
During the Snowflake events, TBR observed that customer demand for scalable, governed and actionable data remains a key driver of Snowflake’s evolution. The company’s ability to manage and harmonize disparate data types was repeatedly emphasized. For example, Ginther highlighted Nissan’s success in using Snowflake to analyze millions of customer profiles across multiple markets. This initiative showcased Snowflake’s ability to address complex, large-scale data challenges while delivering actionable insights for decision making.
 

Generative AI: Unleashing untapped potential beyond chatbots

Snowflake’s scalability is not just about performance; it also plays a critical role in empowering AI adoption through a favorable cost-to-value alignment. The platform’s pay-as-you-go pricing model adjusts to the dynamic demands of AI applications, particularly for resource-heavy use cases such as generative AI (GenAI) and predictive modeling. This flexible model enables organizations to efficiently grow their AI workloads and lowers the barrier to implement advanced AI solutions.
 
During Snowflake GenAI Day, the company showcased GenAI’s vast potential beyond traditional applications like chatbots and content generation. For example, Snowflake partner Sigma Computing demonstrated how Snowflake transformed raw Salesforce data into actionable insights. The AI-driven analytics not only improved decision making for Sigma’s business leaders but also reduced the time spent on manual data preparation, unlocking faster, more informed outcomes.
 
However, as enterprises scale their GenAI applications, they face challenges related to data bias, IP risks and ethical AI. To build trust with customers, vendors must design their AI solutions with governance, fairness and transparency in mind to ensure responsible AI deployment. Customers need to implement strong data governance practices that carefully monitor data to avoid perpetuating inaccurate or discriminatory outcomes.

Golden datasets and the future of AI development

One emerging trend highlighted during Snowflake GenAI Day was golden datasets — curated collections of structured and unstructured data optimized for GenAI use cases. These datasets, when enriched by Snowflake’s platform, empower organizations to drive more accurate and impactful AI outcomes. Moreover, Snowflake’s focus on text-to-language prompts, which simplify data interactions by reducing reliance on complex SQL queries, demonstrate its commitment to improving user experiences. Using Snowflake’s Universal Search offering, customers can identify datasets in their accounts based on data quality and usage within their workflows to create optimized — or golden — datasets. Universal Search ensures that users — regardless of their level of technical expertise — can effectively leverage Snowflake’s capabilities for AI development, analytics and decision making. However, building and maintaining golden datasets pose significant challenges. For many organizations, curating and cleaning data at scale require advanced governance frameworks and skilled teams to ensure data quality, relevance and accuracy. Organizations that lack these capabilities may struggle to derive meaningful insights from their AI models. Additionally, errors or inconsistencies in golden datasets can lead to biased outcomes, undermining trust in AI-driven decision making.

Simplifying user interactions

Another topic highlighted during the GenAI Day event was Snowflake’s focus on improving user accessibility. By incorporating text-to-language prompts into its data and AI platform, Snowflake has reduced the technical barrier for users who may lack expertise in SQL or other programming languages. This feature ensures that nontechnical users can interact with the platform effectively, making data-driven insights accessible across diverse teams.

Predictions for 2025: From experimentation to enterprise-grade AI

During the AI + Data Predictions 2025 event, Snowflake forecast a significant shift in AI adoption as enterprises transition from experimental pilots to fully realized, enterprise-grade AI solutions throughout 2025. However, TBR’s 2H24 Cloud Infrastructure & Platforms Customer Research survey results suggest that the adoption of GenAI solutions may progress more slowly than expected in 2025, primarily due to cost constraints and a lack of technical expertise with the emerging technology. Despite these challenges, Snowflake anticipates AI adoption will be driven by AI observability, as businesses increasingly need to prioritize ROI measurement, deployment reliability and regulatory compliance.
 
During the presentation, speakers discussed how Snowflake’s key AI advancements such as embedding models to enhance the performance of large language models, including GPT models, are enabling task-specific customizations, improving multilingual capabilities and optimizing overall model performance. Snowflake’s platform supports these efforts with containerized runtimes like Snowflake Notebooks and Snowflake Container Services (SCS), which provide scalable and efficient tools for AI development. Baechtold emphasized the critical role of robust datasets in supporting both GenAI and traditional ML models. Snowflake’s platform addresses key challenges, such as data security, governance and accessibility, ensuring enterprises can confidently deploy AI solutions across industries ranging from healthcare to manufacturing.
 

Deep dive into generative AI’s impact on the cloud market in 2025 in the below TBR Insights Live session

Security, governance and containerization: Building trust in AI

Throughout both events, security and governance emerged as central themes in Snowflake’s AI strategy. As enterprises increasingly integrate multiple platforms and environments, the risk of data breaches and compliance violations grows. Snowflake’s approach to governance includes developing best practices around securing cloud configurations, authenticating model access, and monitoring runtime environments to ensure its AI solutions are scalable, secure and compliant with evolving regulations. For example, OM1’s use of Snowflake demonstrated how containerized systems streamline governance processes and enhance scalability and efficiency. By leveraging these systems, Snowflake ensures that clients can deploy AI solutions with confidence, knowing their data and models are protected.
 
Despite Snowflake’s efforts, managing security and governance at scale is an ongoing challenge. Customers operating in highly regulated sectors, such as finance or healthcare, may require additional customizations to ensure they comply with stringent regulatory requirements. Additionally, scaling governance frameworks to accommodate rapidly evolving AI use cases could stretch Snowflake’s platform and resources. Providing consistent, enterprise-grade support while maintaining innovation will be essential for Snowflake to navigate these challenges.

Snowflake’s road map: Scaling innovation while meeting enterprise needs

Looking ahead, Snowflake will continue to focus on expanding its integrated data and AI platform while maintaining its core pillars of scalability, flexibility and observability. The company’s ability to bridge the gap between structured and unstructured data — combined with its investments in user experience, embedding models and AI observability —will place it among the leaders in the next wave of AI innovation.
 
However, Snowflake’s success will depend on its ability to balance innovation with governance, ensuring enterprises can address their unique data challenges while meeting compliance requirements. By focusing on empowering users, streamlining AI deployments and scaling advanced technologies, Snowflake will be well positioned to meet the demands of a rapidly evolving market.

Conclusion

Snowflake’s evolution reflects its commitment to advancing AI through a data-first approach. By addressing the complexities of modern data ecosystems and aligning its platform with emerging AI trends, Snowflake has established itself as a key player in the AI landscape. This strategic focus not only drives digital transformation but also shapes the competitive dynamics of the market, impacting partners, competitors and technology providers. The company has expanded its GenAI capabilities by integrating open-source models such as those from Hugging Face and Meta, enabling customers to deploy and customize AI models more easily.
 
Snowflake also emphasizes AI observability, providing businesses with tools to track performance, optimize outcomes and ensure ROI, while mitigating model drift. Its governance framework ensures regulatory compliance, safeguarding AI data and models across industries. Snowflake’s efforts to simplify the user experience and make AI more accessible to nontechnical users align with new industry standards. By lowering technical barriers, Snowflake is enabling a broader range of businesses to leverage AI and encouraging the market to innovate toward more user-friendly solutions. However, Snowflake faces challenges in integrating diverse data environments and maintaining data quality at scale. The need for significant infrastructure investments, such as GPUs, may also become a hurdle as AI adoption expands.
 
As GenAI and AI observability evolve, Snowflake’s integrated platform is positioned to support partners and stakeholders in navigating the next phase of industry transformation. By offering scalable and secure AI workflows, Snowflake is helping them tackle the challenges of adopting AI at scale across industries. TBR will continue monitoring Snowflake’s progress and its influence on AI-driven business strategies across sectors.

President Trump Announces Joint Venture with OpenAI, SoftBank and Oracle to Build $500B Worth of AI-dedicated Data Centers

On his second day in office, President Trump approved funding for the Stargate Project, a joint venture with OpenAI, SoftBank and Oracle to initially build a $100 billion data center in Texas. Over the next four years, the project aims to expand into additional large-scale data centers, with a total of $500 billion in funding, making it the largest centralized data center investment in history. The funding includes significant financial backing from the U.S. government  with contributions from SoftBank, a firm known for its long-term investment strategies. OpenAI, SoftBank, Oracle and MGX will be the initial equity investors, while Arm, Microsoft, NVIDIA and OpenAI have been named as technology partners and will have some involvement in Stargate.
 
The $500 billion expenditure is unprecedented. Most Tier 1 hyperscaler data center projects are valued in the single-digit billions, making Stargate’s Phase 1 cost more than 50 times higher than its closest comparable. This venture will rely heavily on U.S. government funding, as SoftBank’s Vision Fund, which manages assets worth less than $200 billion, cannot shoulder the full burden. This positions Stargate as a “Manhattan Project” for the AI era, as it represents one of the largest technological undertakings in modern times. The project is poised to reshape global dynamics if it can navigate the significant hurdles that lay ahead. Regardless, OpenAI, equipped with the world’s largest AI cluster, will pursue its goal of artificial general intelligence (AGI) while enjoying unparalleled access to the compute infrastructure needed to push parameter counts higher.

What Does This Mean for Cloud Vendors and Model Developers?

OpenAI Gains a Powerful Competitive Advantage Over Other Model Developers

Being the largest single technology endeavor in recent history, the Stargate Project will have a notable influence on the budding AI market. In TBR’s opinion, no company stands to benefit more than OpenAI. The company’s access to dedicated compute resources will be unmatched in the model developer industry, enabling OpenAI to push parameter counts further and faster compared to peers and supporting the company’s objective of differentiating based on large language model (LLM) performance. OpenAI’s ultimate goal is to reach AGI, which the company defines as “highly autonomous systems that outperform humans at most economically valuable work.” While estimates about when OpenAI will reach this goal vary widely, the Stargate Project fulfills a critical requirement in the pursuit of AGI, making OpenAI a top contender to reach AGI before other firms.

Another Win for OCI

Oracle’s reentry into the already highly saturated IaaS market with Gen2 OCI (Oracle Cloud Infrastructure) has been widely successful. Though initially designed to drive stickiness with enterprises already rooted in the Oracle ecosystem, OCI appears to be gaining traction among digital natives and ISVs for AI use cases. In many ways, this is a testament to Oracle’s decision to cozy up to NVIDIA for GPUs early on in the emergence of generative AI (GenAI) by hosting the company’s DGX software in its data centers, helping NVIDIA position as a software company and avoid becoming another piece of commoditized hardware locked into the hyperscalers’ stacks. Now, riding a wave of over $70 billion in cloud backlog, OCI is Oracle’s fastest-growing business and will soon become its largest.
 
Aside from the GPUs, the other factor fueling OCI growth and granting Oracle its status as one of the largest data center operators in the world is the company’s ability to bring new data centers online extremely quickly. This is because Oracle has generally adopted a strategy of building more but smaller-scale (approximately 145-kW) data centers, with a focus on ensuring that, outside of scale, all Oracle data centers are identical.
 
This scale can vary significantly, though, and with the current AI wave, we have seen Oracle prioritize larger data centers, some spanning 800MWs for AI customers, including OpenAI. There was perhaps no greater testimonial for OCI as a cloud credible enough for AI applications than OpenAI’s mid-2024 decision to leverage OCI for AI training via Azure.
 
To be fair, the existing Azure-Oracle relationship influenced that decision, but OpenAI has made it clear that the company not only needs IaaS services to push the boundaries of its models but also needs them quickly, regardless of who provides them. The Stargate Project would only advance the OCI-OpenAI connection, bringing new workloads to OCI and sending a message to the market that OCI is also in the game with Amazon Web Services (AWS), Microsoft Azure and Google Cloud when it comes to AI workloads.

Oracle Sets Its Sights on Healthcare as the Company Pursues AI Opportunities for Cerner

Oracle CTO Larry Ellison’s remarks at the White House on Jan. 21 were brief but telling of where Stargate could go, with the Oracle co-founder highlighting AI’s role in modern electronic health record (EHR) management and healthcare transformation at large. Of course, Oracle entered the healthcare market over two years ago with its acquisition of Cerner and has since modernized Cerner Millenium on OCI in hopes of delivering a new cloud-based system that will challenge decades-old EHR systems. This includes an EHR system that can support disease-specific AI models that, importantly, are developed not by Oracle but by medical professionals with expertise in said diseases. The details and timeline around Stargate are still vague but stand to advance Oracle’s push in AI, including within the healthcare vertical, which perhaps has among the most to gain from AI’s potential.
 
When the Cerner deal closed, Ellison was very clear about plans to have a standardized database that unifies fragmented data so medical professionals can instantly access EHR data, regardless of what type of EHR system it is, within a single database. At the time, we wrote about the obvious roadblocks to overcome, including the security & compliance concerns and need to obtain legislative backing. Since Ellison’s initial remarks, we have not heard much of an update on this vision, but Stargate and what seems to be Oracle’s rising role in the new administration (stay tuned as we track any potential Oracle-TikTok developments) could help move this vision along.

While Microsoft Has Been a Close Ally of OpenAI, the Bond That Was Forged in the First Year of GenAI’s Time in the Spotlight Has Weakened

So, how did we get here? Rumors of the Stargate Project date back to March 2024, when OpenAI CEO Sam Altman outlined ambitions for a $100 billion data center in partnership with Microsoft. At the time, the partnership seemed logical, given the companies’ long-standing relationship and Microsoft’s significant equity stake in OpenAI. However, the dynamics have shifted. In October 2024 Altman publicly criticized Microsoft for its slow progress in building AI-dedicated infrastructure, an issue corroborated by reports of persistent infrastructure shortages from Microsoft management. OpenAI’s latest announcement reflects the outcome of this strained relationship, as Azure’s exclusivity agreement with OpenAI has been officially amended, granting OpenAI the right to seek alternative delivery agreements if Microsoft fails to meet its compute demands. Oracle is the first cloud provider OpenAI has turned to, leveraging Oracle’s substantial capacity for AI workloads and an increasingly strategic relationship with Microsoft.
 
OpenAI’s shift toward Oracle is a setback for Microsoft but does not entirely diminish the hyperscaler’s leadership in AI. OpenAI remains a close partner, and Microsoft is well positioned to grow its AI-related IaaS revenue as the company continues expanding its infrastructure. Furthermore, Microsoft’s SaaS portfolio serves as a key delivery mechanism for OpenAI’s models, and the company retains a significant equity stake in OpenAI.
 
These factors are likely to sustain the strategic partnership between the two entities for the foreseeable future. Although Microsoft is not a member of the Stargate joint venture, it is listed as a strategic technology partner, and TBR expects its platforms and software to play a role in the project.
 
Additionally, while Microsoft may have less influence over OpenAI’s hosting decisions, Oracle and Azure remain deeply interconnected. For instance, Oracle now uses Azure data centers to house its database hardware through the Oracle Database@Azure Service. This setup could theoretically integrate Azure OpenAI into AI development as customers bring enterprise data from Oracle into the Azure cloud.

Stargate’s First Phase Includes the Construction of a Massive $100B Data Center, the Largest GPU Cluster in the World

Why Build an AI Megacluster?

OpenAI’s primary motivation for increasing computational resources is to meet the exponential demands of training models with higher parameter counts. Scaling up these parameters allows models to process larger quantities of data, often referred to as context windows. As a context window expands, model outputs improve in quality and accuracy. The prevailing belief is that pushing parameter counts far enough will enable models to exhibit the capabilities defined in OpenAI’s vision of AGI. With full financial backing from the U.S. government, OpenAI’s pursuit of AGI appears more achievable. The result would likely be a versatile GenAI back-end architecture capable of transforming process automation in SaaS workflows. However, in the short term, OpenAI’s focus on parameter scaling keeps its AI strategy centered on general-purpose LLMs, rather than more specialized small language models (SLMs). This approach makes OpenAI’s models particularly suited for productivity tools and customer service applications, while specialized models may dominate in niche workflow tasks.

Possible Stargate Constraints

While unparalleled access to GPUs and compute infrastructure is a major advantage for OpenAI’s model training strategy, there are still several factors that need to be addressed alongside the data center construction. First, the Stargate Project initially intends to absorb Oracle’s privately funded, in-progress data center in Abilene, Texas, yet TBR believes this could heighten power supply challenges. While power transmission constraints are widespread, Texas’ power grid has had issues in the past, such as in the winter of 2021, due to the fact that Texas’ power grid, unlike the other 47 states in the continental U.S., does not connect to the two major national grids. This prevents access to backup power generated outside the state and poses a risk of a significant outage. To mitigate these power concerns, TBR believes developing alternative power sources, namely nuclear power, will be a priority early in the Stargate Project.
 
Second, having access to effective training data remains a persistent need within the model developer market. While OpenAI has been forthcoming in striking deals with internet platforms and media sources, some speculate that the corpus of data available to train LLMs and large multimodal models (LMMs) could soon be completely used up. The use of synthetic data has often been proposed to overcome this hurdle, yet this path brings separate issues like greater AI hallucinations and model drift. Altogether, while securing project financing is the first step, working around these constraints will challenge OpenAI as it pursues AGI, and the innovations created in the Stargate Project will need to reach beyond simply building the largest AI-dedicated data centers in the world.
 
Financing could also prove to be a risk. In response to Trump’s executive action, Elon Musk, a Trump insider and co-head of the new Department of Government Efficiency, publicly shared his belief that the U.S. government does not have enough money to fund the project. Of course, Musk has a bias as the founder of startup xAI, but nevertheless, the Stargate Project does have a staggering price tag. Still, with the Republican Party’s control of the legislative and executive branches, there will be fewer political barriers to financing the Stargate Project based on the assumption that AI supremacy is of greater strategic importance.

Conclusion

The Stargate Project marks a significant development in AI infrastructure, with OpenAI, SoftBank, Oracle and the U.S. government collaborating to create a network of AI-dedicated data centers. With a planned $500 billion investment, this initiative seeks to address the increasing computational demands of AI model development, positioning OpenAI to advance toward its goal of achieving AGI.
 
Oracle’s involvement, bolstered by partnerships with NVIDIA and its advancements in cloud infrastructure, highlights its growing role in the AI ecosystem and could advance Oracle’s Cerner ambitions. However, the project faces notable challenges, including substantial energy requirements and concerns over the availability of high-quality training data, which will require innovative solutions to address.
 
As one of the largest technology projects in recent history, Stargate reflects the evolving priorities in AI development and the broader strategic implications for technological leadership.

Analyst Summit Boston: PwC positions trust and cybersecurity as pillars for success in AI and business transformation

PwC has unquestionably built a brand around trust, as reflected in the two main themes woven throughout the Boston PwC Analyst Summit: risk and cybersecurity. In TBR’s view, PwC’s fundamental value proposition around trust and client intimacy reflects the firm’s strong governance, risk and compliance (GRC), cybersecurity and technology capabilities. TBR views risk and cybersecurity offerings as natural enablers for client discussions around business model reinvention and — when complemented by credible customer zero use cases across multiple domains, including AI — an extension of trust throughout a client’s ecosystem.

Despite its traditionally risk-averse culture, PwC was relatively quick to roll out an internal version of ChatGPT to all employees, and TBR believes the firm likely uncovered substantial best practices in how to manage change and limit downside risk associated with generative AI (GenAI).

Recognizing that cyber risk is no longer solely a technology issue but also a businesswide concern with financial and reputational consequences, PwC leaders repeatedly stressed that governance, not technology, must take the lead in ensuring robust cybersecurity strategies, supported by an ecosystem of partners. During a managed services use-case discussion, a client noted that PwC brought specific technology expertise and experience working with other business customers on this client’s specific problem. PwC’s relevant experience offered the client distinct benefits around risk mitigation, as part of their larger managed services engagement.

Additional highlights from PwC’s Boston event:

• An emphasis on data sustainability and GenAI is central to PwC’s long-term investment strategy, forming the foundation of every business line. PwC’s role as what TBR calls a “technology orchestrator” reflects the firm’s commitment to navigating the intersection of renewable energy, AI and other emerging technologies to help clients adapt and grow.
• Geopolitical tensions, particularly between the U.S. and China, remain a critical concern, with bipartisan consensus in the U.S. about addressing these issues underscoring the urgency. Despite challenges with R&D expense rules and state-level regulatory complexities, PwC has been advising clients to embrace sustainability and prepare for scenario planning.
• Mike Thiessen, PwC’s U.S. Chief Clients and Markets leader, noted that PwC’s approach to GenAI focuses on building bespoke workflows, integrating technical development with legal safeguards, and prioritizing user-driven curation. PwC recognizes that each GenAI project is unique, requiring tailored approaches and experience in addition to technical features. Under these conditions, PwC ensures AI implementation is secure and effective, aligning with the firm’s broader AI strategy.
• C. Lapierre, one of PwC’s U.S. Sustainability leaders, noted that mandatory reporting on climate action can serve as a catalyst for enterprises to get their data and sustainability strategy in order. Many companies now understand the scope and depth of the work needed to meet net-zero commitments, which were often made before the necessary parties had a full understanding of the difficulties and opportunities involved.

Artificial intelligence: Big bets, massive change, and all comes back to trust

On artificial intelligence (AI), PwC leaders during the Boston event noted that AI is reshaping everything, from brand and market positioning to operational strategy. PwC committed to a $1.5 billion investment in AI, an increase from its original announcement of $1 billion, underscoring the technology’s importance to the firm’s future.

The vendor is also focused on delivery excellence, specifically enhancing systems like SBLC to make them more intelligent and efficient. PwC noted that the U.S. firm spends 17 million hours annually bringing ISV partners into the production stage of engagements, clearly an area ripe for AI-driven optimization. Additionally, PwC leaders said that new partnerships around developing language models are revolutionizing SBLC, creating a new foundation while refactoring older offerings. According to PwC, this shift reflects the broader evolution of AI from an emerging technology to a general-purpose one that is now central to business strategies.

PwC leaders elaborated on potential business model implications of wider AI adoption, including the erosion of scale as a differentiator as AI-driven agentic workflows allow small companies to simulate large-scale operations. In addition, faster adoption rates help businesses more quickly realize the efficiency AI brings to back-office operations.

In TBR’s view, trust continues to be the linchpin for AI’s success; absent trust, AI’s potential will remain unrealized. As noted above, in previous TBR reports, and by PwC leaders repeatedly during the Boston event, PwC’s brand is built around trust.

Demonstrating the criticality of AI to PwC, firm leaders noted nine high-stakes AI investments, each worth $50 million, aimed at driving either top-line growth or cost reduction. One of the standout initiatives is Elly, an AI-powered system with a digital worker equivalent (DWE) of 2,500 — with each DWE representing 2,000 hours of work. This demonstrates the firm’s bullish outlook on AI’s return on investment.

Further, PwC leaders believe AI’s impact extends to enterprise resource planning (ERP) systems. While earlier designs emphasized efficiency, the focus has now shifted to functionality and achieving the best outcomes. In building these systems, PwC is ensuring that both design and implementation align with the firm’s strategic objectives. TBR agrees with PwC’s assessment of the potential for AI to massively improve ERP systems, provided enterprises fully trust their AI platform to handle mission-critical and proprietary data. Again, the emphasis is on trust.

Analyst Summit London: PwC leaders highlight megatrends and business model reinvention in effort to navigate transformation

In London, PwC leaders shared the following megatrends and commentary from the firm’s perspective:

• Climate change is negatively affecting social stability.
• Increasing demand for silicon chips and power, combined with a limited supply of chips and GenAI, is compounding clients’ risk factors.
• There is a global need to rethink power, global food supplies, demographics and migration, and industrial processes.
• Increase for on-demand mobility is quickening the pace of change in the automotive and oil & gas verticals.

To address the megatrends and capture opportunities, PwC is investing in three key areas both globally and in EMEA: sustainability, trust and business model reinvention (BMR). BMR requires helping clients operate in new ecosystems to find future areas of growth and reconsider how products and services will change. According to PwC EMEA leaders, AI, data and technology cut across all three, and every enterprise must be able to operate and be successful in these areas.

Going deeper on BMR, PwC EMEA leaders noted that, according to PwC’s most recent Global CEO Survey, approximately 45% of CEOs do not believe their business will be viable in the next 10 years without reinvention, up from 39% of respondents in 2023. Not surprisingly, clients are asking for PwC’s advice on strategic planning and how to invest today to be successful tomorrow. PwC has a methodology to assist clients with their transformations, starting by sitting with clients and discussing their needs to gain a deep understanding of their design and implementation abilities and industry knowledge and co-creating an approach that is industry-led and industry-focused.

According to PwC EMEA leaders, the PwC BMR framework helps clients identify business growth areas, such as the development of new ecosystems and the creation of new products and services to pursue value and underpenetrated areas of revenue. For example, through a client session, PwC walked through a BMR transformation in which PwC helped create a new company from scratch following a sale of the business. Through the new business, the client sought to overcome rising cost pressures as well as crop and agricultural challenges that were disrupting its ability to deliver its products. Additionally, changing its primary delivery method to include a different product allowed the new company to focus on driving value and creating new revenue streams.

In a separate client example, PwC created a new platform to transform how a university engaged with prospective students. Through the platform, the university sought to advance its technology, position for the future, strengthen trust, and improve online engagement and opportunities. PwC used its BMR framework in both of these client examples, guiding the evolution of existing business environments to identify needs and pursue next steps to future-proof operations.

On a technology-specific note, PwC EMEA leaders highlighted the firm’s Industry Edge approach, explaining that PwC’s first step is building a differentiated way to enable transformation outcomes tailored for each industry. PwC applies data and tech assets, gathers use cases to understand the best way to make decisions, and establishes preconfigured solutions that support business transformation, all while leveraging technology alliances.

The key, according to PwC EMEA leaders, is that the firm provides not only a consulting approach but also all of PwC’s capabilities, including technology, regulatory, risk and even tax services. Critically, in TBR’s view, PwC is not going to clients and selling Industry Edge; instead, the firm is adapting elements of Industry Edge that are applicable to specific clients.

PwC EMEA leaders noted that “every day PwC follows two principles”: 1) emphasizing client centricity, which PwC and TBR both recognize sounds obvious and is not differentiating but, according to PwC, is a key to success; and 2) a one-firm approach: a global network of firms that come together to seamlessly deliver services to clients. Pursuing these initiatives enables PwC to deliver reinvention and transformation services through an industry play, leading with the right approach to drive value, cocreation and evolution services.

TBR’s expectations for PwC in 2025

In TBR’s view, PwC’s twin analyst events at the end of 2024 showed a firm shifting into a new gear, perhaps reflecting leadership changes or the changing environment for professional services as the GenAI age begins to mature and PwC’s strategic investments and its own business model reinvention begin to take shape.

PwC’s early epiphany around artificial intelligence centered on understanding both the necessity of accessing client data and the implication that if a client’s data were a mess, AI would be useless. The firm steadily invested in the expertise and capabilities needed to assist clients with their AI journeys, accelerating that investment when GenAI hit the market. Notably, PwC continued its deeply ingrained practice of investing substantially in its own people, bringing AI and then GenAI solutions to the firm’s professionals and leaning into the customer zero approach.

Now PwC is fine-tuning its own business model and looking to accelerate technology adoption, redefine (or at least continually improve) global operations and grow its Managed Services business. In TBR’s view, it is not a reinvention … yet. Critically, as PwC transforms itself the firm remains grounded in its core value to clients: trust.

2025 Predictions is a series of special reports examining market trends and business changes TBR expects in the coming year for AI PCs, cloud market share, digital transformation, GenAI, ecosystems and alliances, and 6G.

Top Predictions for GenAI in 2025

1. 1. GenAI will continue to revolutionize mission-critical functions and day-to-day operations at federal civilian, defense and intelligence agencies
   2. Cloud vendors will splurge on AI investments even as customers grow apprehensive
   3. Infrastructure vendors’ focus will shift from serving cloud companies to making a massive push in enterprise AI
   4. The energy problem is likely to slow the pace of AI market development significantly
   5. GenAI upends pyramids, even as enterprises slow their AI roll

The state of AI and GenAI in 2024

In 2024 the AI and generative AI (GenAI) landscape faced four key challenges: rising costs, driven by growing investments in data and infrastructure; talent and training gaps; regulatory uncertainty; and macroeconomic pressures. These obstacles will persist into 2025, with additional challenges in the GenAI space becoming increasingly evident.
 

According to TBR research, the waning GenAI hype has exposed underlying issues, including expensive cloud commitments and fragmented data strategies, creating opportunities for companies that emphasize ROI, complementary technologies and cost management. Adding to the complexity, rising energy costs and heightened awareness of GenAI-related security risks are further shaping this uncertain yet opportunity-filled environment.
 
But after two years of GenAI disruption, a clear trend is emerging across the ecosystem: strategic partnering is becoming essential. Companies such as McKinsey & Co, Wipro, Dell Technologies, Amazon Web Services (AWS) and NVIDIA are adopting this approach, recognizing that no single organization can deliver comprehensive GenAI-enabled solutions alone. Instead, success increasingly depends on leveraging the technology and expertise of ecosystem partners.
 
To read the entire 2025 GenAI Predictions special report, request your free copy today!