On Feb. 26, PwC Australia hosted more than a dozen analysts for a day of client stories and updates on the firm’s business in Australia and New Zealand. PwC’s leadership team included Rohit Antao, Advisory leader, PwC Australia; Dean Dimkin, Ecosystems & Alliances leader, PwC Australia; Karen Lonergan, chief people officer, PwC Australia; David Callaghan, CFO, PwC Australia; and Reggie Walker, PwC Global Account partner and Global Salesforce Alliance leader. The following reflects the discussions that day and TBR’s ongoing analysis of PwC, other Big Four firms, IT and professional services, and the overall technology ecosystem.
 
Throughout the day, analysts heard, in TBR’s view, a highly effective perspective from newly promoted PwC Australia Manager Saliha Rehanaz. In between sessions in which PwC partners and leaders presented client stories along with PwC clients and discussed the firm’s overall strategy, Rehanaz provided commentary from the perspective of someone doing the day-to-day hands-on work at the client site. She reflected on how she had experienced and worked through client challenges similar to those presented, and she added to the leaders’ strategy discussion, explaining how the high-level goals translated to everyday work at the firm. Rehanaz’s perspective helped TBR better understand how PwC Australia’s culture resonates with PwC professionals and clients. For a well-established firm with a brand rooted in trust and values, hearing a manager-level professional articulate PwC’s culture reflected exceptionally well on the firm as a whole.

“We are super match fit now”

Setting the stage for all the discussions to come, Antao acknowledged PwC Australia had “come through troubles” and undergone its own reinvention over the last few years, positioned now to “repair, rebuild and grow.” Although the firm previously had been spread too thin, by 2026 PwC had begun focusing on what the firm termed the “Right Segments, Right Clients, and Right Offerings,” with an emphasis on growing relationships and capabilities (and letting revenue growth result). Antao and other PwC leaders highlighted the firm’s culture, which enables curiosity, challenge (internally and with clients) and collaboration.
 
All told, PwC Australia heads into 2026, as the subhead notes, “super match fit” and ready for challenges, such as technology disruption and new competitors. Antao said the firm intends to be “Australia’s leading reinvention partner” by 2030 through executing on a strategy that emphasizes making bold choices (by client, by sector, by offerings) and playing to the firm’s strengths. PwC will change the way it delivers, especially with its technology alliances partners, and embed AI into “everything we do,” according to Antao.
 
In TBR’s view, all those strategies, elements and aspirations make sense. Execution comes next. PwC leaders, including Callaghan and Lonergan, raised a few execution and market challenges and provided insights into PwC’s approach. Antao noted that although PwC Australia’s Tax and Deals practices had well-established success with the country’s midmarket clients, Consulting had not traditionally pursued that segment. He intends to change that. Callaghan discussed shifting commercial and pricing models, acknowledging Australian clients are both more interested in and still nervous about engagements rooted in outcomes-based pricing.
 
Callaghan wondered aloud about how to “price in cost savings from AI in a three- or five-year deal” and suggested PwC’s shift to outcomes-based pricing would be with well-established clients and PwC capabilities. Lonergan and Antao both discussed talent challenges and strategies in the context of PwC’s 2030 aspirations, with Antao noting how newly aggressive, private-equity-backed competitors had created fresh challenges for PwC’s talent management. Lonergan said PwC’s brand centers on trust and the quality of PwC people.
 
In Australia, the firm has been adjusting the profiles of new hires, reflecting a dynamic market and changing client expectations. For example, previously most of the new hires in PwC Australia’s Assurance practice had backgrounds and skills in accounting. In the latest round of hiring, half of the recruits had no specific university-trained accounting skills but brought “other human skills.” Although Lonergan is unsure about the future shape of the talent pyramid, she reaffirmed new hires as “the lifeblood of professional services.”
 
Since ChatGPT’s explosion, TBR has asserted that AI equals transparency. PwC’s brand is trust, and clients can be assured that the firm consistently asks, “Is this the right thing to do? Is this the right tech?” PwC Australia unquestionably went through the fire and is now a more careful, thoughtful and purposeful firm at a time when many consultancies, IT services companies and technology vendors use AI to justify moving fast and maybe breaking things, just to keep pace. Trusted, purposeful and playing to strengths should be a more compelling approach. Antao said Australian CEOs’ “trust levels” around AI have grown significantly in the last two to three years. Even if PwC does not want to take any credit for shaping that change, the firm can benefit from being well positioned to take advantage of it.

Honesty, humor and adoption — three words rarely associated with banking

PwC highlighted client stories at the event, perhaps more than any previous event TBR has attended: seven client stories along with three PwC strategies and offerings sessions. Of the clients, four banks presented surprisingly different use cases for engaging with PwC:

• • One bank adopted a U.K.-based bank’s SaaS platform to launch a new, completely digital subsidiary bank and used PwC as the business and systems integration partner. PwC Australia and the client agreed on an “adopt, don’t adapt” mentality, which meant adopting Engine, U.K.-based Starling Bank’s SaaS platform, and not adapting the technology to common Australian practices or approaches, but did so to meet Australian regulations. If some Engine component had to be changed — adapted, rather than adopted — then PwC and the client team escalated that decision to the top level. Notably, PwC leveraged talent from PWC Australia, PwC UK and PwC Poland, but the client said colocating the bank’s people with PwC Australia’s professionals and some PwC UK professionals in the same building and on the same floor was critical to success.
  • A different bank used PwC for a Salesforce implementation, which was essentially a replatforming and simplification program to save the bank time and money. To win the work, PwC identified the specific people from within the bank and within PwC to work on the project — from both technology and business profiles — to align capabilities across all functions. PwC brought a “level of authenticity,” according to the lead PwC partner working with the bank, that others lacked. Further, PwC acknowledged there would be some pain in the project — as is true in every technology project — and addressed that expected pain with “honesty and humor.” So far, these two use cases are nice but maybe not noteworthy. What stood out for TBR was the lead PwC partner’s description of the transformation this engagement brought to the bank. Although it was not an immediate change in the bank’s business model, PwC considered the work “transformative” because success with this engagement led the bank to replicate the approach to technology stack modernization, to using out-of-the-box solutions, and to working with partners across other aspects of the bank. This was, according to the lead PwC partner, truly transformative and a cultural change as much as a technology one. PwC’s global Salesforce alliance leader, Walker, notably attended the analyst event, reinforcing the firm’s commitment to bringing global capabilities to bear in Australia.
  • A third bank client highlighted PwC’s ability to help adopt AI-enabled solutions in a highly regulated environment to scale quality assurance across cross-channel complaints while enhancing quality and compliance metrics. Among the lessons learned was the importance of delivery processes over AI process.
  • For a fourth bank, PwC Australia provided design, implementation, playbooks and the orchestration necessary to design the future operating model for an Agentic Security Operations Center. The bank’s chief information security officer explained his need for consolidation and simplification and said PwC’s help enabled the bank to “iteratively test, learn and build this out” and demonstrate value quarterly. Notably, this engagement included PwC India and PwC US working with PwC Australia.

Two themes from these banking client use cases stand out for TBR. First, at least two examples showcased PwC’s ability to deliver globally to local clients, bringing PwC assets and capabilities from outside the country to complement PwC Australia. More of this will be necessary for PwC to continue competing successfully with Big Four peers and become Australia’s leading reinvention partner. PwC separately briefed the gathered analysts about the evolution of the Concourse platform, which, according to Dimkin, “unlocks our global power.” TBR will provide analysis on that development in a separate report. Second, significant digital transformation has typically entailed business model reinvention, not simply adopting new technologies. PwC’s banking client use cases demonstrate meaningful transformation can be as simple as shifting an enterprise’s culture around how to adopt technology and how to leverage consultancies, if those cultural changes then enable broader business model evolution.

Simplification is all the rage

An additional client story highlighted another common theme through the event: simplification. In an AI era with seemingly relentless complexity dressed up as productivity gains, PwC Australia’s clients echoed each other in calling for, and getting from PwC, more simplification in the technology stack, in AI adoption and in understanding how to evolve their business models. An insurance company’s story began with a history of PwC delivering business-unit-specific platforms, which transformed the client’s technology stack and repeatedly returned value on the investment.
 
Capable delivery and consistent results made PwC the insurance company’s partner of choice, particularly when implementing Salesforce, Guidewire and MuleSoft solutions. Notably, the PwC-enabled tech stack transformation supported the insurance company’s organic growth and strategic acquisition strategy. According to the client’s COO (an uncommon role to be presenting at an analyst event), the IT environment and technology stack were explicitly part of the M&A due diligence and eventual ability to acquire. TBR has only very rarely heard similar case studies of IT environment alignment being a key acquisition factor.

Professional services remains rooted in people

Bringing people together across an enterprise into a successful technology change became a common theme in the client stories and PwC presentation. Although this is hardly unique to consultancies, a few examples showcased how fundamental this approach is to PwC Australia. As mentioned in the second banking client example, PwC proactively matched people from the client and within the firm prior to winning the work, demonstrating PwC’s approach would include both business and technology leads and stay rooted in the people aspects of technology change.
 
In another client example, the PwC team recognized the need to begin the engagement with business process subject matter experts, not AI or technology experts, to successfully bring along the affected business units. And in one final example, PwC helped the client empower internal AI evangelists to build their own agents and accelerate AI adoption throughout the enterprise. Across these examples and the other client stories, PwC clearly understood the firm’s strengths as a technology orchestrator and business model reinvention specialist — strengths made more resonant to clients based on PwC Australia’s own recent experience.

TBR perspective

Though AI was the overarching topic of discussion at Mobile World Congress 2026 (MWC26), sovereignty, resiliency, trust and governance also permeated conversations throughout the event. The rapid acceleration of AI — combined with mounting geopolitical uncertainty — is forcing governments and enterprises to reassess long-standing assumptions about global supply chains, labor markets, education systems and technological dependencies. As a result, sovereignty is emerging as a defining strategic priority.
 
While definitions of sovereignty vary, a common theme emerged throughout MWC: Nations increasingly want to own, operate or meaningfully influence critical components of the digital stack that underpin economic stability and national security, including connectivity infrastructure, data platforms, cloud environments and, increasingly, AI capabilities.
 
For telecom operators, this shift could represent one of the most significant structural growth opportunities for the industry in years. Telcos sit at the intersection of domestic technology infrastructure and government policy, positioning them as natural partners in sovereignty-driven initiatives such as secure networks, domestic data hosting, cloud services, resilient communications infrastructure and trusted AI deployment.
 
Governments are increasingly looking for national or regional champions that can anchor sovereign digital ecosystems, and telecom operators — given their existing infrastructure, regulatory relationships and role in critical communications — are well positioned to play that role. As sovereignty agendas translate into funding programs, regulatory support and public-private partnerships, telecom providers that align their strategies with national priorities could unlock new revenue streams while reinforcing their roles as strategic infrastructure providers in the AI era.
 

Impact and Opportunities

Sovereignty and resiliency may be the telecom industry’s next growth catalyst

Governments are beginning to put real action behind years of rhetoric around digital sovereignty, largely under the banner of national security. More than 90 countries now have formal sovereignty statements, signaling sustained policy momentum rather than a passing trend. Current geopolitical conflicts, including the war in the Middle East, are reinforcing the urgency of this shift. TBR expects governments with the financial capacity to allocate substantial funding and regulatory support as well as provide other favorable market conditions to domestic champions that can help advance sovereignty objectives. Telecom operators and their critical vendor ecosystems are particularly well positioned to play a central role.
 
Specifically, TBR expects telecom operators to receive unprecedented government backing to expand their roles across sovereign digital infrastructure. This includes owning and operating data centers, supporting data residency requirements, and investing in greater network resiliency through cybersecurity, redundant facilities and diversified backhaul routes. These investments are aimed at strengthening national security, protecting sensitive data and reducing reliance on non-domestic providers wherever possible. Telecom operators are also well positioned to integrate connectivity infrastructure and data centers with AI models and applications in ways that comply with domestic regulations. Canada, Europe, the developed Middle East and parts of Asia and Oceania are likely to lead this sovereignty push.

Governance becomes a prerequisite for success with AI

Governance was a recurring theme across content sessions and executive meetings at MWC26. As telecom operators move from experimentation to operational in AI, creating a corporatewide, centralized framework for data management, model oversight and regulatory compliance is becoming essential. Without clear governance, AI initiatives often remain fragmented across business units, leading to inconsistent outcomes, duplicated efforts and limited enterprise impact.
 
The challenge is that most telecom operators still lack a horizontal governance model for both AI and data. Data ownership is often siloed, policies vary by department and there is limited visibility into how models are trained, deployed and monitored. This fragmentation makes it difficult to scale AI beyond isolated pilots and increases operational, regulatory and reputational risk.
 
Telecom operators with strong C-suite sponsorship are best positioned to overcome these challenges. Executive backing helps enforce common standards, prioritize enterprisewide data initiatives and ensure AI programs are aligned with broader digital transformation objectives. Without this level of leadership support, governance efforts often stall as organizational silos resist change.
 
Leading telcos are beginning to formalize governance by creating centralized data offices and appointing chief data officers responsible for enterprisewide data strategy and governance. In practice, robust governance is quickly becoming a prerequisite for AI. Organizations that establish clear frameworks for data quality, access, security and accountability will be far better positioned to operationalize AI at scale and consistently generate business value.

Trust can be a CSP differentiator and revenue driver for telecom operators

Trust emerged as a central theme at MWC26, with many industry leaders warning that confidence in the digital ecosystem is eroding as scams and fraud proliferate across networks. Estimates suggest that roughly $500 billion is lost each year globally to fraud and scams, underscoring the magnitude of the challenge. With networks acting as a critical conduit — and often a chokepoint — for malicious actors, many speakers emphasized that telecom operators must play a more proactive role in addressing the issue. The industry issued a clear call to action for operators, technology vendors, regulators and other ecosystem participants to collaborate more aggressively to combat fraud and restore confidence in the digital world.
 
Several discussions at MWC26 framed trust as both a responsibility and a potential competitive advantage for telecom operators. Telcos sit at the center of digital connectivity and increasingly function as a protective layer for the broader digital economy. However, while technology innovation is accelerating rapidly, the mechanisms that ensure trust — security, verification, governance and consumer protections — are not evolving at the same pace. Fraud and scams not only cause financial harm but also risk eroding public confidence in digital networks and services. Because trust is fragile and can be quickly lost, operators must treat it as a strategic asset that requires ongoing investment and careful stewardship.
 
Some industry leaders suggested that trust could become a differentiator for telecom operators relative to hyperscalers and other digital-native companies. Customers often view telecom providers as more regulated and infrastructure-centric, and therefore inherently more accountable than large technology platforms. The open question for the industry is whether this trust advantage can be monetized, either through stronger customer loyalty, increased market share or the development of new trusted digital services. However, the inverse is also true: If trust erodes further, consumers and enterprises may alter their behavior, potentially bypassing traditional telecom channels.
 
Bharti Group Chairman Sunil Mittal pointed to roaming as a model for industry collaboration. Over the past two decades, operators have worked collectively to dramatically reduce the cost and friction associated with international roaming. Mittal suggested that a similar level of global coordination among operators, technology providers and regulators could be applied to tackling scams and fraud. In doing so, telecom operators could help society regain greater control over the digital environment — in addition to reinforcing their role as a trusted foundation of the global communications ecosystem.

The initial focus for AI-RAN is AI for RAN

Most of the AI-RAN-related announcements and discussions at MWC26 centered on AI for RAN, which entails applying AI models to improve the performance, efficiency and automation of radio networks. Several vendors demonstrated how specialized GPUs and AI accelerators can be integrated into the baseband to process RAN workloads more efficiently than traditional hardware, enabling operators to apply machine learning models to tasks such as network optimization, traffic management, and increasingly, radio signal processing. Key use cases include channel estimation, traffic prediction and beamforming optimization, each of which can help improve spectral efficiency and throughput while lowering power consumption. The first commercially available AI-RAN products will come to market in 2027, with 2026 as a development and proof-of-concept year. RAN vendors can expect to start realizing meaningful revenue growth from AI-RAN in 2028.

Agentic AI holds promise, but telco adoption remains slow

Agentic AI — systems capable of autonomously planning and executing complex tasks — featured prominently in discussions across MWC26, but adoption among telecom operators remains limited. While vendors and technology firms are aggressively advancing agent-based architectures, most telecom operators remain focused on foundational generative AI deployments such as copilots, knowledge assistants, customer service automation and language translation (T-Mobile’s Live Translation service is an example of an initial network use case for agentic AI). Moving from these assistive use cases to fully autonomous agents requires significantly higher levels of data quality, governance and system integration than most telecom operators currently possess.
 
Data fragmentation and the lack of enterprisewide governance frameworks are major barriers to scaling agentic AI initiatives for telecom operators. Telecom operators typically operate across dozens of siloed operational and business support systems, making it difficult for autonomous agents to reliably access and act on enterprise data. As a result, most agentic AI activity among telcos remains confined to pilot programs and proofs of concept, often focused on narrow operational workflows such as network troubleshooting or IT automation. Until operators establish stronger data governance models and centralized AI strategies, agentic AI will likely remain an experimental capability rather than a widely deployed operational tool.

AI agent marketplaces hint at the emergence of a digital labor market

One of the more intriguing ideas circulating at MWC26 was the notion of job postings for AI agents and the emergence of marketplaces where organizations can source them. As agentic AI evolves and becomes intertwined with the labor market, enterprises may increasingly seek ways to “hire” AI agents capable of executing discrete tasks or workflows.
 
Conceptually, this could resemble a new class of digital labor market in which organizations post tasks or roles that can be fulfilled by autonomous agents. Over time, this dynamic could give rise to a broader ecosystem of AI agents hosted on marketplaces that are effectively “looking for work” — something akin to a hybrid between hyperscalers’ cloud marketplaces and platforms such as Indeed.
 
Early examples of agentic AI are already demonstrating this model’s potential. Initial AI agents introduced by companies such as Anthropic are capable of autonomously handling tasks ranging from legal research to creative work, often with minimal human intervention. As these systems become more sophisticated and capable of executing multistep workflows, it is likely that enterprises will increasingly source specialized agents rather than build every capability internally. This shift could drive the emergence of marketplaces where companies — and eventually consumers — discover, benchmark and deploy AI agents that meet specific operational needs.
 
In practice, enterprises are unlikely to post job listings through traditional HR processes. Instead, AI agents will likely be sourced and orchestrated through software platforms and workflow systems that dynamically assign tasks to the most appropriate agents. This creates the foundation for a new digital economy centered on AI labor, with new markets emerging around agent distribution, infrastructure and trust. As organizations begin deploying agents in critical workflows, marketplaces will likely evolve to include performance benchmarking, compliance certifications and reputation systems that help enterprises determine which agents are trustworthy, secure and effective.

ISAC is here now

The mobile industry may not need to wait for 6G for integrated sensing and communications (ISAC) capabilities to begin emerging. While ISAC is widely viewed as a core feature of future 6G networks, several vendors are already demonstrating how sensing functionality can be enabled using existing LTE and 5G infrastructure. Startups such as Tiami Networks are developing solutions that leverage existing radio signals and machine learning algorithms to transform cellular networks into wide-area sensing systems capable of detecting objects such as drones and vehicles or human movement.
 
These early implementations sit largely outside formal 3GPP ISAC specifications, relying instead on clever signal processing, software and edge compute to extract sensing insights from existing RAN transmissions. Although still in the early stages of commercialization, these systems are already being tested and deployed in niche environments such as defense, critical infrastructure protection and public safety. As standards bodies continue developing native ISAC capabilities for 6G, these early deployments provide a glimpse into how mobile networks may increasingly double as large-scale sensing platforms.

Conclusion

MWC26 made clear that the telecom industry is entering a new phase shaped by the convergence of AI, geopolitics and digital infrastructure. While AI dominated the conversation, the broader narrative that emerged centered on control, resilience and trust in an increasingly complex digital environment.
 
Sovereignty initiatives are pushing governments to rethink how critical infrastructure is owned and operated. At the same time, telecom operators are grappling with the organizational and technical prerequisites needed to operationalize AI at scale, including governance, data management and system integration. Together, these forces are redefining the role telecom operators play in the global technology stack — not simply as connectivity providers, but as strategic infrastructure partners in the digital economy.
 
Ultimately, sovereignty, governance, trust and AI are deeply interconnected. Governments want trusted domestic infrastructure. Enterprises want secure and reliable AI-enabled services. And consumers increasingly expect digital environments that are safe and resilient. Telecom operators sit at the center of all three dynamics. If telecom operators can successfully align AI innovation with strong governance frameworks and trusted infrastructure, they have an opportunity to move beyond the traditional connectivity business and play a far more central role in shaping the next phase of the digital economy.

AI PC Ambitions Face an Unforgiving Reality of Memory Constraints and Budget Pressure

For the PC industry, 2025 was the year that the end of Windows 10 support would drive a massive PC refresh cycle. As part of this refresh, AI PCs, devices with neural processing units (NPUs) designed to execute AI and machine learning tasks, were expected to rise in popularity as businesses prepared their workforce for a new era of AI-infused productivity. The PC refresh cycle of 2025 did materialize to some extent, with the market growing about 5.3% year-to-year according to TBR’s estimates, but sellers ran into a snag in the commercial PC market: Many buyers were unwilling to pay 10% to 20% more for an AI PC that could future-proof their PC fleet but offer minimal added value today. This sentiment has driven ecosystem players such as Intel and Microsoft to invest in making the AI PC more compelling to and affordable for the commercial PC market. The beginning of 2026 ushers in the latest generation of AI PC processors from Intel, AMD and Qualcomm that is poised to attract customers with performance, efficiency and security enhancements. In many ways, 2026 is a critical year for the success of the AI PC due to the significant investment in the platforms that have been years in the making.
 
When vendors made these long-term investments in next-generation AI PCs, no one knew that these new models would launch just as insatiable demand in the AI server market caused an unprecedented shortage of memory. The shortage, which in TBR’s opinion could easily last for two to three years, has already caused significant price hikes in the PC market — with more on the way — and will result in longer lead times and lower availability. As such, PC market leaders’ desire to usher in a new era of more expensive AI computing now sits in direct odds with unavoidable component price increases and tight customer budgets.
 

Pressure to refresh drove 2025 growth, with the intrinsic value of AI PCs yet to be defined

In 2025 AI PCs capable of meeting Microsoft’s Copilot+ requirements began to hit the market in earnest, following their initial debut in the summer of 2024. On paper, the timing looked favorable: A Windows 11 refresh cycle was underway, and AI PCs were positioned to ride that momentum and steadily grow share within the broader commercial PC market. In practice, however, the refresh materialized unevenly and ultimately fell short of expectations. Although the market experienced solid growth, expanding an estimated 5.3% year-to-year in 2025, multiple structural and behavioral hurdles slowed adoption of AI PCs, preventing vendors from translating technical readiness into meaningful volume growth.
 
Pricing pressures emerged as a central inhibitor. Tariffs complicated OEM pricing strategies, and the incremental cost of NPU-enabled processors introduced a noticeable price increase that many commercial buyers were unprepared to absorb. This sticker shock was exacerbated by the lack of a compelling near-term value proposition for AI PCs. Vendors leaned heavily on the concept of future-proofing device fleets, a message that resonated poorly with budget-constrained IT buyers prioritizing immediate, tangible ROI. At the same time, commercial purchasing behavior has yet to normalize post-COVID. Some organizations are still digesting inventory acquired during earlier buying surges while others have permanently shifted from predictable refresh cycles to more opportunistic, as-needed purchases.
 
In response, OEMs and key ecosystem partners — notably Microsoft, Intel and AMD — adjusted their go-to-market strategies to reduce friction and keep AI PCs moving into commercial environments. OEMs refined system configurations where possible to lower bill-of-materials costs without undermining Copilot+ eligibility. Silicon providers increased front-end discounting, rebates and comarketing investments to narrow the price gap between traditional PCs and AI-capable systems. Together, these efforts drove down pricing of AI PCs in the commercial space and helped some customers make the transition to NPU-enabled silicon during their device refreshes.

Ecosystem investments years in the making underpin the future success of AI PCs

Despite increasing competition from AMD and Qualcomm, Intel remains the PC silicon market share leader due in large part to the robustness of the company’s partner and incentive programs. PC OEMs and the broader industry supplying PCs rely heavily on Intel to influence market direction and demand. Understanding that the AI PC market is somewhat nebulous due to ill-defined use cases, Intel has invested significantly to make its latest chips — Core Ultra Series 3 — a success, leaning into its heritage as an integrated device manufacturer and shifting manufacturing of the compute tile on the new system on a chip (SoC) in-house rather than outsourcing to Taiwan Semiconductor Manufacturing Co. as it did with the Core Ultra Series 2. Although the investment to bring 18A capacity online has been massive, in the long run Intel could benefit from a cost of sales perspective with the manufacturing based in the U.S. and improved yields as the process matures. In contrast, as fabless companies, AMD and Qualcomm rely solely on third-party manufacturing, which presents fewer risks but could prove more costly in the long run.
 
Silicon vendors’ investments in driving AI PC adoption have looked similar from company to company, with AMD, Intel and Qualcomm providing developer resources such as samples, guidance and reference applications to drive NPU-enabled application development. However, although AMD’s and Qualcomm’s resources are limited to software developer kits and other software stacks directly targeting the NPU, Intel’s play also leans into its reputation as a market maker with its AI PC Acceleration Program, incentivizing collaboration between ISVs and independent hardware vendors (IHVs) to create features that leverage the NPU. Additionally, with the engineering staff available through Intel Open Labs serving as a differentiator, Intel arguably provides more design resources, sales support and go-to-market funding than AMD and Qualcomm, echoing the company’s long-running strategy that has positioned it atop the Windows PC silicon market. Similar to what silicon vendors are providing, Microsoft has launched tools that will help ISVs refine their Windows-based apps to make the best use of CPU, GPU and NPU.
 
Qualcomm, more so than Intel and AMD, needs to prove itself in the commercial market and demonstrate not only that Arm can outperform x86 in commercial uses but also that the platform is a trustworthy investment. However, much of this weight falls on Microsoft’s shoulders as Windows on Arm is inherently its own initiative. Perhaps more important than its advances in performance in TOPS (trillions of operations per second), Qualcomm’s newly launched Snapdragon X2 Plus platform offers a competitive answer to Intel’s vPro platform with Snapdragon Guardian, which allows for advanced remote management and security features. However, Intel vPro’s Active Management Technology, which allows for out-of-band remote management, remains core to the company’s value proposition, is still unmatched by Qualcomm and AMD, and is a key reason noncloud-first organizations tend to choose Intel over its competition.

2025 Windows AI PC SoC Revenue by Covered Vendor [Intel, AMD, Qualcomm] (Source: TBR)

Massive scale AI infrastructure deployments are driving unprecedented fluctuations in memory costs, negatively impacting supply for the PC market

The PC market has weathered many component shortages that have had short-term impacts on product availability and pricing. In recent history, shortages have been caused by factors ranging from pandemic-induced buying changes and natural disasters to manufacturing industry consolidation and delays in next-generation technology rollouts. However, the current memory component shortage is unique due to its ties to structural AI demand versus a temporary shock to the market. Over the past two years, demand for AI infrastructure has shifted the slow-growing server market into hypergrowth, with TBR estimating that server spend nearly doubled from 2023 to 2025. Not only has the demand risen dramatically, but the AI server silicon, whether GPUs or AI ASICs, also requires significant amounts of high-bandwidth memory (HBM), unlike their traditional server counterparts.
 
As such, hyperscalers and neoclouds are absorbing a disproportionate share of DRAM and HBM as they scale AI training and inference infrastructure, and the same suppliers sell both HBM and conventional DRAM. As these memory suppliers prioritize higher-margin HBM, commodity DRAM output growth tightens as investments in new capacity are allocated more heavily toward supporting AI infrastructure rather than client devices.
 
This trend of elevated server demand combined with disproportionate consumption of high-performance memory will not abate anytime soon. OEMs’ total server order backlogs remain in the billions exiting 2025, and during AMD’s 4Q25 earnings call CEO Lisa Su reported the company expanded manufacturing capacity for server CPUs, confirming that component suppliers continue to shift their manufacturing lines in favor of servers rather than PCs.
 
As a result, PC DRAM supply has not grown enough to support increasing demand. The DRAM found in thin-and-light PCs is the same DRAM found in most smartphones, and the replaceable DRAM typically found in desktops and workstations is also used in traditional enterprise servers. Due to this supply-and-demand imbalance, prices for PC DRAM have skyrocketed and are likely to remain elevated through at least 2026 and into 2027. Already, contract prices for the DRAM found in desktops and workstations have increased between 10% and 30% year-to-year going into 2026 with the DRAM found in thin-and-light PCs increasing at an estimated rate of between 20% and 40%.
 

OEM Server Revenue in $USD Billions for 2020 through 2025 (Source: TBR)

Memory costs and longer lead times put AI PC momentum at risk

TBR expects the memory shortage will impact commercial PC buyers in three ways: pricing, availability and product assortment. Channel partners in contact with TBR have reported PC OEM prices have increased twice since the beginning of 2026, with more price hikes planned. Although price increases to date have been estimated between 10% and 20%, a partner noted a customer whose specific PC configuration price had increased by 40% since December 2025. Channel partners have also noted that delivery times have already become longer, meaning customers will have to accept longer lead times going forward.
 
These dynamics put AI PC models with higher-priced silicon in a precarious position. Customers with finite spend are already facing significant price increases on memory that offers no additional value. Many customers will face a choice of further delaying PC refresh, reallocating budget from other IT projects, or seeking watered-down configurations to reduce costs.
 
OEMs will need to maintain tight cost controls as much as possible through supply chain management and focus on product assortment. For example, OEMs will likely push more models with 16GB of RAM, which is the minimum requirement for Copilot+ PCs, over those with 32GB or 64GB. Ultimately, with longer lead times and a tight product assortment a likely outcome, TBR expects OEMs will focus on configurations and segments where they can maintain margins and best serve the customers that are willing to pay.
 
The cost burden will not fall solely on the OEMs. With all three AI PC silicon vendors launching new chips at CES in January, these vendors are motivated to make the new platforms a success. OEMs and silicon vendors already had to adjust pricing on NPU-enabled silicon in 2025 to reach a point where customers were willing to upgrade. TBR expects OEMs will pressure Intel, AMD and Qualcomm to provide additional discounting to ensure the success of the latest generation of chips.

AI PC Revenue by Big Three Windows OEMs [Dell Technologies, HP Inc., Lenovo] for 2025 and 2026 (Source: TBR)

2026 outlook: Increasing prices will stifle overall market growth and slow AI PC mix shift

Despite significant pricing impacts, TBR expects the PC market will grow in 2026. In fact, the early part of the year may perform better than expected if customers pull in buying to avoid additional price increases coming down the road.
 
Although the PC ecosystem has been investing in making NPU-enabled silicon more useful, the use cases for most AI PC buyers remain thin and focused on future innovation. The AI PC mix will continue to increase, but TBR believes the growth rate will be lower than we predicted in 2025. The increase in mix will be based more on the availability of chips and manufacturers’ emphasis on AI PCs than on organic customer demand.
 
Although TBR estimates the overall value of the PC market will grow 1.7% year-to-year in 2026, the increase will be driven by rising selling prices, not shipments, which are expected to decline as many buyers delay PC refreshes unless absolutely necessary. Meanwhile, TBR forecasts the overall value of the AI PC market will expand 33.4% in 2026 due in large part to a relatively small compare and PC OEMs’ (excluding Apple) growing AI PC revenue mix, which TBR predicts will increase from 23.9% in 2025 to 37.9% in 2026, a difference of 1,400 basis points. Although this mix shift is pronounced, TBR firmly believes that had PC DRAM prices remained the same, the increase would be more robust.
 

TBR’s AI PC Market Forecast for 2025 through 2030 (Source: TBR)

Conclusion

Rising demand for the types of DRAM found in PCs, in combination with memory suppliers focusing production capacity build-out on supporting massive-scale AI infrastructure deployments, has led to a supply-and-demand imbalance in the market, causing memory manufacturers and suppliers to rapidly increase PC DRAM prices. With margins already thin, PC OEMs are passing their rising build costs on to buyers in an attempt to protect profitability. AI PC prices were already higher than traditional PC prices, and with AI PCs typically configured with more memory, on average, AI PC prices are poised to continue increasing more than traditional PC prices, making the future-proofing-led sales proposition less compelling. Not only will these rising prices result in slower-than-anticipated adoption of AI PCs, but they will also cause more organizations to delay their PC refreshes. However, once PC DRAM prices stabilize and begin to fall, pent-up demand from delayed refreshes could yield a prosperous cycle for PC OEMs and silicon vendors alike.
 
Fortunately, although the rate at which memory prices are increasing is unprecedented, PC OEMs have weathered component shortages in the past. While increasing PC prices will slow the market’s transition to AI PCs in the near term, the proliferation of the AI PC over the next five years is almost guaranteed due to the alignment of ecosystem players, including PC silicon vendors, PC OEMs and Microsoft, which owns the operating system. As such, increasing memory costs and PC prices are less of a roadblock to AI PC adoption and more a speed bump impacting the momentum of adoption.