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365 Data Centers and Aphorio Carter, the critical infrastructure and data center division of Carter Funds, announced a strategic partnership on May 6 to develop approximately 200 megawatts of AI-ready data center capacity across six U.S. sites. The first letters of intent target Aurora, Colorado, and Simpsonville, Kentucky, with additional locations planned in Trumbull, Connecticut; Louisville, Kentucky; Harrisonburg, Virginia; and Columbus, Ohio.

The details matter here. These are not greenfield projects. 365 and Aphorio Carter plan to identify, convert, and develop existing properties into high-density facilities supporting 50 to over 200 kilowatts per cabinet, using liquid-to-chip cooling designed for AI and high-performance computing workloads. 365 will serve as long-term operator across the portfolio, with projects expected to come online within nine to 24 months.

“Through this partnership, we’re in an ideal position to create a new class of high-density infrastructure designed specifically for AI-era workloads,” said Derek Gillespie, CEO and CRO of 365 Data Centers. “Working with Aphorio Carter will allow us to create new value in existing assets while bringing new capacity online to support today’s demand.”

Nine to 24 months. In a market where greenfield data center projects now take three to five years from announcement to first megawatt, that timeline is the real story. And the conditions creating demand for this kind of speed are only intensifying.

A $725 Billion Spending Spree Collides with a 15 GW Grid

The capital flowing into AI infrastructure has reached a scale that’s difficult to contextualize. Google, Amazon, Microsoft, and Meta collectively plan to spend $725 billion on capex in 2026, a 77 percent increase over last year’s record $410 billion.

Fortune reported in late April that the buildout shows “no clear end in sight.” The Stargate Project, a government-backed initiative led by OpenAI, Microsoft, Oracle, and SoftBank, has committed $500 billion toward AI-first data centers by 2029.

The money is not the constraint. Power is.

The entire U.S. data center sector draws less than 15 gigawatts of electricity today. The announced project pipeline, if every facility were built, would pile 140 gigawatts of new load onto that grid. That’s roughly nine times the current draw. And the grid is nowhere near ready to absorb it.

Of 12 GW of data center capacity announced for 2026 delivery, only about 5 GW is under active construction. Roughly half of all planned 2026 builds have been delayed or canceled outright. The reasons are physical, not financial. Lead times for high-voltage transformers have ballooned from 12 to 18 months before 2020 to 36 to 48 months today, with some orders stretching to five years. Grid interconnection wait times exceed five years in many U.S. regions. Beyond what’s currently under construction, an additional 37 GW of planned infrastructure lacks firm completion dates, pushing the cumulative pipeline gap past 50 GW of announced-but-unbuilt capacity.

America’s investor-owned utilities have responded with $1.4 trillion in capital spending plans through 2030, up 27 percent from the prior year’s projection. But grid upgrades move on timelines measured in years, not quarters. The gap between committed capital and deliverable megawatts continues to widen.

This mismatch has created an opening for operators willing to think differently about how AI-ready capacity gets built.

The Case for Building Inside Existing Walls

The brownfield conversion model attacks the binding constraint directly. Existing properties with committed utility power can bypass the two longest delays in data center development: interconnection queues and greenfield construction timelines.

The approach is gaining traction faster than most industry observers expected. Goldman Sachs models assume brownfield accounts for 15 percent of required data center space in 2026, growing to 30 percent by 2031. More than 70 percent of global data center capacity already resides in existing buildings, according to Schneider Electric. And brownfield retrofits can cut facility capital expenditure by up to 30 percent compared to greenfield builds, per Data Center Dynamics.

The most dramatic proof of concept arrived in 2024, when xAI assembled Colossus, its 100,000-GPU supercomputer, inside a shuttered Electrolux factory in Memphis in just 122 days. That project moved from empty industrial space to operational AI training cluster faster than most greenfield developers can secure permits.

Three forces have converged to make this model viable at scale.

The first is the power bottleneck itself. When interconnection queues run five years deep, any operator who can deliver capacity on existing grid connections holds a structural advantage. Speed-to-megawatts has become a premium asset in its own right.

The second is liquid cooling. Two years ago, retrofitting an existing building for AI-grade power density was impractical. Air cooling hit its thermal ceiling around 30 to 40 kW per rack. Direct-to-chip liquid cooling has upended that calculus. Goldman Sachs projects the share of liquid-cooled AI servers will climb from 15 percent in 2024 to 76 percent in 2026. Schneider Electric published a technical framework in February 2026 focused on brownfield liquid cooling retrofits, highlighting localized, staged upgrades as a strong fit for colocation and service provider environments. Design loads exceeding 100 to 200 kW per rack are now standard in new AI builds, and the AI data center liquid cooling market is projected to expand from $6.6 billion in 2025 to $61.8 billion by 2034.

The third is the shift from training to inference. Centralized mega-campuses make sense for massive training clusters. But inference workloads, the production side of AI where models serve real-time predictions and outputs, benefit from geographic distribution. Smaller facilities deployed closer to end users reduce latency and spread the load across multiple grid connections. The conversion model maps well to this emerging architecture.

Not every building qualifies, of course. Viable candidates need structural load capacity for high-density racks, proximity to utility substations with available or committed capacity, adequate water supply for cooling loops, and fiber connectivity. The differentiator is finding properties where utility power is already allocated or can be secured without entering the back of a multi-year interconnection queue. That’s where real estate expertise becomes just as valuable as engineering capability.

How the 365/Aphorio Carter Partnership Works

The 365 Data Centers and Aphorio Carter partnership pairs two distinct competencies. Aphorio Carter brings real estate investment, development, and asset management experience, backed by a leadership team that has collectively invested in and managed over $6 billion in data center real estate. 365 brings operational depth: colocation, connectivity, managed cloud services, and a pipeline of enterprise customers who need high-density capacity.

The division of labor is clean. Aphorio Carter sources and develops the properties. 365 operates them long-term. Together, they can move on multiple sites simultaneously rather than sequencing projects one at a time.

“We’ve aligned the delivery of utility power with critical infrastructure allowing us to provide scalable, high-density infrastructure where it’s needed most,” said John Regan, President and COO at Aphorio Carter. “This is a great partnership where we’ve got the real estate and the ability to supply the data center infrastructure inline with available utility capacity while 365 has a highly reliable O&M track record along with a healthy pipeline of customers. Together, we’re creating a scalable supply of power-rich environments that can be delivered faster and perform at a higher level than traditional developments.”

The geographic strategy reveals a deliberate calculation. Aurora, Simpsonville, Trumbull, Louisville, Harrisonburg, Columbus. These are not Northern Virginia or Dallas or Phoenix, the traditional hyperscale corridors where grid capacity is most constrained and competition for power is fiercest. These are secondary markets where utility power remains accessible, where interconnection timelines are shorter because operators aren’t competing with multi-gigawatt campus developments for grid access.

The nine-to-24-month delivery window stands in sharp contrast to the three-to-five-year timelines that are now common for greenfield projects, particularly in regions where transformer shortages and grid congestion have slowed permitting and construction.

The Window Is Open, and the Clock Is Running

Brownfield conversions will not replace hyperscale campuses. Meta’s multi-gigawatt Hyperion project in Louisiana, the Stargate consortium’s 7 GW expansion across Ohio and Pennsylvania, Microsoft’s $7 billion Fairwater campus in Wisconsin: these projects exist at a scale and serve a purpose that conversions cannot replicate. The industry’s largest training clusters will continue to demand purpose-built facilities with dedicated power plants and custom electrical infrastructure.

But training clusters are only part of the picture. For mid-market operators serving enterprise AI deployments, for the growing wave of inference workloads that demand geographic distribution, and for any organization that needs AI-ready compute capacity in the next 12 to 18 months rather than the next three to five years, the conversion model fills a gap that greenfield development physically cannot address on the timelines the market demands.

The brownfield model carries a sustainability advantage, too. Reusing existing structures means a lower embodied carbon footprint than new construction, a factor that enterprise procurement teams are weighing more heavily in vendor selection as corporate climate commitments run headlong into the energy intensity of AI.

The competitive field for brownfield AI infrastructure is still forming. Few operators have assembled a repeatable, scalable conversion playbook. The 365 and Aphorio Carter partnership represents one early template, but the opportunity set extends well beyond six sites in Colorado and Kentucky. The window exists because the power wall is not temporary. Transformer supply chains, interconnection backlogs, and permitting processes will take years to normalize even with the $1.4 trillion in utility investment now committed.

The AI infrastructure race will not be decided solely by who writes the biggest check. In a market where half of planned builds stall before breaking ground, the ability to deliver megawatts fast, inside existing walls, on existing grid connections, in markets the hyperscalers haven’t yet saturated, may prove just as consequential as any $10 billion campus announcement.

Maya Kalyan’s career has always lived at the intersection of disciplines. With a background in biomedical engineering and more than a decade in life sciences, she now serves as a staff algorithms and AI engineer in the molecular diagnostics space at Thermo Fisher Scientific. In a recent TechArena Data Insights episode, Solidigm’s Jeniece Wnorowski and I heard Maya offer a practitioner’s view on where AI is genuinely delivering value in healthcare, and where significant work remains.

Four Problems, One Goal

The starting point for any meaningful discussion of innovation in diagnostics, Maya explained, is understanding what problems the industry is actually trying to solve. She identified four primary areas of focus: accuracy and reliability, turnaround time, cost reduction, and automation.

On both cost and turnaround time, she pointed to how the increasing demand for molecular testing is being met by innovations like multiplexing, which is the ability to detect multiple pathogens within a single sample. “We have respiratory virus tests that can detect COVID and flu and RSV viruses all within the same test,” she said. “That reduces the reagent use and lowers your consumable cost while also increasing throughput.” The broader goal, she noted, is building diagnostic systems that are simultaneously faster, more reliable, more affordable, and capable of handling the volume demands of high-throughput clinical and research environments.

Where AI Works, and Where It Doesn’t

Maya offered a measured perspective on AI’s current capabilities, drawing a clear line between where the technology performs well and where it has room for growth.

AI tends to be most effective, she said, when working with large, well-structured datasets toward a defined predictive outcome, such as pattern recognition in biological data, quality monitoring in experimental workflows, and domain-specific assistants that help researchers navigate documentation or troubleshoot instruments. The benefit, she noted, is improving the user experience and reducing manual touchpoints.

The limitations, however, are equally important for technology decision makers to understand. “When it comes to large language models, specifically the risk of hallucinations and its non-deterministic nature — where it can make up things or not say the same thing each time — can be a barrier to adoption in scientific or healthcare settings.” Her prescription is a hybrid approach: one that keeps human expertise in the loop by design, even as agentic AI systems grow more capable of autonomous workflows.

Regulatory Reality

Building AI-enabled diagnostic products is not simply a technical challenge. Maya outlined a layered set of constraints that shape every deployment decision, starting with data governance. Healthcare datasets often contain sensitive patient or genomic information. Considerations for privacy affect how data can be accessed, shared, and used in ways that go well beyond standard HIPAA compliance.

There are also practical deployment decisions with regulatory implications: whether AI systems run in the cloud or directly on an instrument, and how factors like connectivity and latency influence what’s feasible. And once a model is deployed, the work isn’t over. “Teams need some kind of post-market surveillance plan,” she said, “which requires a strong model observability service where they can monitor the performance of the model and identify any drifts.” In practice, applying AI in this space means balancing innovation against a set of strenuous operational and regulatory realities.

The Data Foundation Problem

Before AI can meaningfully contribute to product development or diagnostics, Maya emphasized that organizations need to get their data house in order. That begins with rigorous data curation, ensuring experimental data is well-annotated and collected consistently so models can learn real patterns rather than artifacts of poor methodology.

Accessibility is the other piece. In many research organizations, data is scattered across instruments, labs, and databases with no unified infrastructure to bring it together. Maya pointed to large open biomedical datasets such as the Cancer Genome Atlas curated by the National Institutes of Health as important resources the research community already relies on. Looking ahead, she sees federated data approaches, which enable collaboration without requiring the sharing of raw patient data, as critical to accelerating AI’s role in diagnostics at scale.

The TechArena Take

While grounded in biomedical engineering, Maya’s perspective reflects broadly applicable lessons: the most durable AI deployments are built on disciplined data practices, realistic expectations, and a clear-eyed understanding of evolving regulatory requirements. In a field where the stakes are measured in patient outcomes, the pressure to get it right is acute. If AI lives up to its potential in life sciences, the payoff won’t just be operational efficiency. It will be earlier diagnoses, more personalized treatments, and meaningfully better quality of life for patients facing some of the most challenging medical conditions.

A conversation with qBraid’s Kanav Setia on quantum computing’s evolution, developer challenges, and the role of software in shaping its future.

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Submer's Gabriel Lazar joins TechArena and Solidigm at OCP EMEA on data center sustainability, heat reuse, and lower-impact AI infrastructure.

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The enterprise AI landscape is littered with promising ideas that haven’t made it past the proof-of-concept stage. Companies are investing in AI capabilities, yet the path from controlled experiment to production-grade system remains one of the most persistent and costly bottlenecks in the industry. The gap demands more than technical talent. It requires the kind of operational and financial rigor that separates sustainable growth from expensive experimentation.

Following the recent launch of the TechArena Advisory, we are excited to highlight the exceptional operators bringing C-suite-grade strategic intelligence within reach of organizations at every stage of growth. The Advisory represents our commitment to providing a high-impact alternative to traditional consultants, offering the strategic blueprints of those who have already built and scaled multi-billion-dollar businesses.

Laura St. John has spent more than two decades in the trenches of finance, strategy, and operations, building the kind of pattern recognition that only comes from roles where the numbers had to hold up and the decisions carried real consequences. As co-founder of MisaLabs, she is now channeling that experience into solving the pilot-to-production problem head on, helping enterprises build AI systems designed to scale from day one. In this edition of our “5 Fast Facts” Q&A series, she discusses why speed without clarity is the biggest risk in enterprise AI today, what separates pilots that scale from those that stall, and how disciplined decision-making remains the most underrated competitive advantage in a market moving at full velocity.

Q1: What has changed in the tech landscape that made the Advisory a priority for you?

This shift is personal for me. After years inside large organizations, I stepped into a startup environment and started seeing how quickly the ground is moving for business leaders.

Everyone knows AI is changing the pace. That part isn’t news. What caught my attention was a pattern I kept running into: companies pouring energy into AI proof of concepts that never made it past the demo stage. Lots of experimentation, very little making it into how the business actually runs day-to-day.

That gap between “interesting pilot” and “operating at scale” is what pulled me into advisory work. The technology conversations are happening everywhere. The harder conversation, the one about whether your organization is actually set up to absorb and scale what you’re building, isn’t happening nearly enough.

Q2: What does your experience bring to this moment?

More than two decades across finance, strategy, and operations, mostly in roles where the numbers had to hold up and the decisions carried real weight. I’ve been on both sides of it. Building the story, and getting challenged when the data didn’t reconcile. Those moments stick with you. You develop a sharp instinct for separating signal from noise.  One that stands out was an investment in a new TV technology. The whole team was excited, the technology was compelling, and the financial projections looked great. Then the CFO asked a pointed question about the ASP, the average selling price we were projecting for the new product, and gave us some homework: could we prove that consumers would actually pay that much of a premium? After some digging, we found the ASP we had baked into our model was more than 5x the price jump the market absorbed when TVs went from black-and-white to color. Even adjusted for inflation, we were well beyond anything the market had ever supported. The technology was real. The business case wasn't.

That pattern recognition is what I lean on most right now. There’s no shortage of data or dashboards in any organization today. The question that trips people up isn’t “what can we see?” It’s “do we trust what we’re looking at, do we understand what’s behind it and do we know where it came from?”

Q3: What challenges are business leaders facing that align with your practice areas?

Speed without clarity. That’s the simplest way to put it.

Leaders are under enormous pressure to adopt tools, automate decisions, and scale quickly. In many cases, though, the underlying data isn’t connected and operating models aren’t ready for that velocity. The result is that companies end up scaling decisions without fully trusting the inputs behind them. Dashboards can create a false sense of certainty.

The other pattern I see consistently: strong top-line growth masking what’s underneath. Revenue climbs, and it hides margin pressure, operational drag, cost structures that haven’t been questioned in years. Then growth slows, and all of it surfaces at once.

The work I focus on is reconnecting speed with fundamentals. Understand the business model. Trust the data. Know what’s actually driving performance before you pour fuel on it.

Q4: Are there key areas you see as most pertinent right now?

The intersection of AI adoption and financial discipline. That’s where the tension is highest.

I see companies land in one of two places. Some stall because competing priorities create indecision and gridlock. Others rush into investment without a clear picture of usage or value. This creates pressure to run to a proof of concept.  They’re an easy way to show progress. They’re also controlled, narrow, and disconnected from enterprise complexity.

Here’s the part that doesn’t get said enough: pilot success rarely translates into production success. The requirements are fundamentally different. Integration, security, reuse, and governance. Those constraints don’t show up in a pilot, but they’re the reason most AI efforts stall at scale.

That is what led us to start MisaLabs. Most AI systems fail to scale for the same reason: enterprise requirements like security, compliance, and infrastructure are treated as afterthoughts.  We built MisaLabs to reverse that, embedding those constraints into the foundation so teams can move from pilot to production without starting over.

Q5: What outcomes are you targeting to drive?

Fewer blind spots. Faster decisions that people can stand behind.

In practice, that means narrowing in on what drives the business, aligning teams around those priorities, and stripping out the noise that slows execution. Not more dashboards. Better understanding of what the dashboards are telling you.

It also means building confidence in the system. Leaders should know where their data comes from, what’s behind a trend, and when something doesn’t add up. That’s when better decisions happen.

The long game is durable, profitable growth. Not just momentum or activity. If I can help teams connect strategy to execution and sidestep a few of the pitfalls I’ve seen play out over the years, that’s a good outcome.

There’s a conversation happening at the edges of the AI infrastructure world that hasn’t quite broken through to the mainstream yet. It’s not about which GPU cluster wins the benchmark race or which hyperscaler is adding the most capacity. It centers on something far more fundamental: the cost of moving data.

In a recent Data Insights episode, I sat down with Solidigm’s Jeniece Wnorowski and Nilesh Shah, VP of Business Development at ZeroPoint Technologies, to work through where this friction in modern AI systems lives.  

Ten Times More Expensive to Move Than to Compute

Nilesh began with an often-overlooked aspect of data storage: the amount of power moving data takes. Moving a single bit of data from storage, through high-bandwidth memory or low-power double data rate (LPDDR), and into the on-chip static random access memory (SRAM) where computation actually happens costs roughly ten times more in power than performing the computation itself. That ratio explains why inference chip innovators like Groq, Cerebras and SambaNova are focusing on data movement and memory hierarchies over compute.

Zero Point Technologies was founded on the premise that the need for data and memory is going to increase rapidly, and one of the ways to tackle that challenge is through lossless memory compression. By reducing the volume of data physically moving across the system, you increase the effective bandwidth and capacity of the compute engine.

Agentic AI Compounds the Problem

On the question of whether AI workflows were being constructed correctly for the management of data, and how this could change as enterprises start scaling inference into different parts of their business, Nilesh pointed out that the key problem to be solved is agentic AI entering the workflow.

A pattern seen at recent tech conferences was that chip designers were integrating multiple specialized AI agents into a single electronic design automation (EDA) workflow, each handling a distinct task, like error detection or chip verification. This would mean having domain-specific inference solutions for even EDA operations, fundamentally changing the way enterprises will need to think about data.  

Prefill vs Decode Stage

As data becomes a challenge, memory bandwidth could become a bottleneck. Nilesh pointed out that agentic workflows and inference takes place in two stages, prefill and decode. The prefill stage processes the input prompt and is genuinely compute intensive. Modern GPU clusters handle this part reasonably well. The decode stage, where the output is generated, is extremely memory intensive and is what’s really limiting tokens per second.

When it comes to responsiveness at enterprise scale, say 100,000 employees simultaneously interacting at that scale across multiple streams of data, the decode phase becomes a real bottleneck. At NVIDIA GTC 2026, a lot of the keynotes revolved around developing heterogenous architectures that can manage the decode phase more efficiently.

We talked about when quantum computing would enter the picture. “What is the ChatGPT moment for quantum computing? That’s the favourite question I like to ask,” said Nilesh. He predicted that it could make sense to attach quantum processing units to data centers to efficiently offload some of the compute work that quantum tends to do well. There are currently examples of banks deploying early quantum computers, and another use case could be encryption and creating more secure encryption protocols.  

Future Innovation in Memory  

When I asked Nilesh what he sees on the horizon for memory and storage technology, he outlined three distinct directions where investment and innovation are converging.  

The first is alternative memory technologies. Dynamic random-access memory (DRAM) is a decades-old architecture that hasn’t changed fundamentally, and its limitations are starting to bite at exactly the moment AI workloads are scaling fastest. The second is new interfaces between memory and compute that will transform how memory communicates with the compute engine.

The third is the most significant shift in perspective: the unit of infrastructure design is moving from the chip, to the server, to the rack, and now to the data center as a single coherent system. Organizations are thinking about AI infrastructure in terms of megawatts allocated to a data center, with memory, storage, and compute all traded off within that power budget.

The biggest misconception, he felt, was the assumption that scaling AI output will keep being built on a proportional increase in power. “I expect a breakthrough that someone will come up with an entirely new style of physics that will break that linear assumption that to go from 100 LLMs to a million or going from a million users to 100 million, we’ll just multiple the megawatts of power,” he said.  

The TechArena Take

My conversation with Nilesh clarified a change in direction I’ve noticed at many recent tech conferences. The 10x cost differential between moving data and computing on it is the reason the entire inference chip landscape looks the way it does. It’s a significant engineering constraint that companies like ZeroPoint are building directly against. The prefill-decode distinction matters because enterprises planning inference deployments at scale need to architect around the decode phase as a distinct bottleneck.

We’re excited to see what new innovations take place in the memory space, and if, as Nilesh believes, someone will eventually find a way to scale AI without the linear progression of more compute meaning more power.  

Watch the podcast here.

Quantum computing conversations tend to get pulled toward the exotic: superposition, quantum entanglement, and what a world with exponentially faster compute will look like. But at the Xcelerated Computing Conference in New York, Solidigm’s Jeniece Wnorowski and I spoke with Burns Healey, quantum infrastructure lead for Dell, who offered a grounded perspective. For quantum technology to matter, it first needs a place to land, and that place is working alongside the classical data center.

It’s a framing that shapes everything about how Dell is approaching the market and one with practical implications for technology decision makers weighing when and how quantum fits into their roadmap.

A Hybrid Future

Our conversation started with reconsidering the terms we use when we talk about quantum technology. “Quantum computers are almost a bit of a misnomer,” Burns said. “When you say quantum computer, I prefer to use the term quantum accelerator, because really that’s what they are. They’re an add-on to HPC or data center infrastructure that give you specialized options for computing specific workloads.”

This perspective that quantum technology is best considered as an extension of high-performance computing environments can be helpful to enterprise leaders who may feel pressure to engage with quantum. Organizations attempting to adopt quantum before they’ve pushed classical computing to its limits are, in his view, getting ahead of themselves.

“Going to a quantum computer before you’ve attempted to use classical HPC or large data center environments is a bit like trying to run before you’ve walked,” he said. “Only once you hit those limits in your data center, in your HPC environment, will you start to think about what quantum can do that you can’t currently do.”

Lowering the Barrier with Logical Qubits

Much of the early quantum conversation focused on physical qubit (quantum bit) counts and error rates, but recent conversations in quantum computing have shifted toward logical qubits and error correction as the field considers what usable quantum will look like.

Burns drew a direct analogy to classical computing. Just as error-correcting code allows applications to run more reliably, logical qubits aim to provide a stable, abstracted layer above the physical qubit substrate.

“The way we use them from a vendor and hardware supplier viewpoint is that we are going to aim to abstract away a lot of that physical layer complexity from the end user,” he said. “It’s a lowering the barrier to entry question in my mind, and the best way we can help onboard new people to the technology.”

Closing the Latency Gap With NVIDIA

When you think of quantum computers as quantum accelerators, the importance of the infrastructure that enables quantum and classical computing to work seamlessly becomes paramount. Rather than building quantum processing units (QPUs), Dell is helping produce the ecosystem and infrastructure appliances that will make quantum devices usable within real data center environments. A major challenge in that area is latency between quantum and classical systems. Burns pointed to Dell’s collaboration with NVIDIA as a current example of this work.

NVIDIA has developed a framework called NVQLink, designed to minimize the round-trip latency between QPUs and classical compute. Using NVQLink on Dell PowerEdge servers, the two companies recently demonstrated sub-4-microsecond latency, a result Burns described as meaningful progress toward the kind of tight integration that real quantum workloads will require.

“We’re really looking at what the technology needs in terms of specifications and hitting those targets to make this infrastructure usable for real quantum computing,” he said.

Dell is also engaged with quantum partners including QuEra and IQM, as well as a joint research initiative with Ernst & Young, all documented on Dell’s hybrid quantum-classical computing page.

The Path to Scale

When asked what needs to happen technically and operationally for quantum to move from research settings to deployable infrastructure, Burns identified two parallel tracks.

On the software side, progress is already underway. Frameworks like IBM’s open-source Qiskit are helping developers work with quantum gates and algorithms today. The next meaningful shift will come when developers can work at a Python-level abstraction, or eventually through application-specific tools that require no quantum expertise at all.

On the hardware side, cabling is one of the more pressing unsolved problems. Superconducting qubit systems require analog signals routed to each individual qubit. At 50 or 100 qubits, that is manageable. At thousands or millions of qubits, the cabling architecture becomes an issue. Ideas to address this include embedding classical components inside dilution refrigerators and more sophisticated multiplexing approaches, both of which introduce their own challenges.

The TechArena Take

Dell’s positioning in the quantum space is as perceptive as you would expect from one of the world’s classical computing giants. Rather than competing with QPU vendors, the company is focused on the infrastructure layer that will make quantum systems usable in real enterprise environments.

Burns’s framing of quantum as an accelerator, not a computer, is a useful corrective for organizations trying to calibrate their engagement with the technology. For most enterprises, the near-term question is not whether to adopt quantum, but how to ensure that classical infrastructure is ready when quantum workloads become viable. The organizations with the strongest HPC foundations will be best positioned to take advantage of it.

Listen to our conversation in the full podcast episode, and for more information about Dell’s hybrid quantum-classical computing work is available on Dell’s quantum computing site.

The Open Compute Project Foundation’s 2026 OCP EMEA Summit landed in Barcelona this week with more than 2,000 attendees, up from 1,100 in Dublin last year and 700 in Lisbon the year before that. Seventy percent of the crowd was European.

The organization turns 15 this year, and the milestone arrived not with nostalgia but with an agenda that felt like a sprint. OCP formalized its Open Data Center Ecosystem Vision last October, an expanded strategic framework stretching from the power grid to the silicon die. (We broke down what that vision includes heading into the summit.) The keynotes in Barcelona made clear just how fast that framework is converting into engineering.

The Fungible Data Center Takes Shape

Google’s keynote by Amber Huffman anchored the morning with a demand signal that explains why the room was full: Gemini customers now consume 16 billion tokens per minute through direct API use alone. All seven of Google’s two-billion-user products run Gemini models, as do all 15 of its half-billion-user products.

That volume drives Google’s push for what it calls the “fungible data center,” infrastructure built to accommodate TPUs, third-party GPUs, optical circuit switching, and liquid cooling interchangeably. The goal is a building that can absorb new hardware generations without a rebuild. When Google posed the question to the industry last October, more than 50 organizations signed on. Six months later, the collaborative has published a 0.5 specification and is working toward 1.0 with AMD, Meta, and NVIDIA contributing. Google framed OCP’s pace as “specs at product speed,” targeting the 80% of shared industry requirements and reaching alignment within three to nine months.

Sustainability ran through the presentation, too. Product category rules for carbon measurement, developed with AWS, Meta, and Microsoft, define physical scope down to individual PCBAs so that lifecycle comparisons are apples to apples. A clean backup power initiative through the Net Zero Innovation Hub covers PEM fuel cells, long-duration energy storage, and clean fuel generators. Google is also backing an evaluation framework for low-carbon concrete through the OCP Academy.

Power Architecture Hits an Inflection Point

NVIDIA’s Javier Peman put the forcing function on screen. Rack density sits at roughly 150 kW today. By year end, it climbs to 250 kW. Next year, 650 kW. Jensen Huang has already announced the 1 MW rack. That trajectory, measured in just three years, rewrites every assumption about how power reaches compute.

At 48 volts, converters and busbars consume so much rack space that accelerators get squeezed out. Google’s Mount Diablo project, built with Meta and Microsoft, addresses this with 800 VDC delivery through a sidecar rack. The sidecar handles AC-to-DC conversion, and every rack unit in the compute cabinet stays dedicated to GPUs or TPUs.

Schneider Electric’s Sebastien Cruz Mermy mapped the longer arc. Sidecar configurations handle up to roughly 1 MW and represent the immediate, deployable step. Centralized low-voltage DC converters cover the 1 to 5 MW range. Beyond 5 MW, solid-state transformer technology becomes the path forward, with centralized high-voltage DC distribution. His most important message: this is not just a conversion problem. Protection, distribution, and conversion must be engineered and qualified as a single system. That full-system thinking was the most consequential architectural point of the morning.

Liquid Cooling Is Now the Baseline

NVIDIA’s Peman left no ambiguity on cooling.

“We forget about air cooling. It’s gone,” he said. “If you ever want to work with NVIDIA, it’s going to have to be DLC.”

Google’s Project Deschutes hit its final 1.0 specification, incorporating the Redmond Hot Aisle containment system for high-density liquid-cooled rack deployments. Six vendors delivered prototypes within one quarter of the earlier 0.8 spec release.

The NeoCloud panel drove the point home with production deployments, not prototypes. Denvr Dataworks has shrunk an H200 SXM server from 6U to 3OU using immersion cooling, fitting 14 H200 GPUs per rack where traditional air-cooled configurations support two to four. They have been running those servers in immersion for two years. Crusoe, meanwhile, is standardizing on closed-loop, non-evaporative systems at every new site it develops.

NeoClouds Find Their Footing Through OCP

A panel featuring Scaleway, Denvr Dataworks, Crusoe, and FarmGPU revealed the NeoCloud as a distinct infrastructure tier. At GTC earlier this year, Jensen Huang noted that 60% of GPUs ship to hyperscalers and 40% to everyone else. The panelists represent that “everyone else,” and they are building with very different playbooks.

Scaleway bets on European sovereignty and deep customer co-design. Denvr controls the full vertical stack from facility to cloud portal. Crusoe operates as an energy-first company, integrated from electrons to tokens. FarmGPU is placing its chips on AI storage, predicting 16 TB per GPU in demand for inference workloads in the Rubin generation, alongside confidential compute using Intel TDX and AMD SEV.

When asked which OCP standard has had the most tangible impact, every panelist gave the same answer: ORV3. The open rack spec delivers copy-paste deployability across sites, multi-generation hardware lifecycle planning, and supply chain confidence through volume standardization. Crusoe’s Peter Sheh called standardization “key to speed” at scale. Scaleway’s Yann-Guirec Manac’h captured the simplicity: “One question. Is it open rack? Yes, no. That’s it.”

Manac’h surfaced the tension those growth metrics don’t capture.

“Our main constraint is access to power,” he said. Connecting to the European grid at tens-of-megawatt scale remains complicated and slow, and the OCP hardware supply chain is entirely import-dependent. “Nobody's building in Europe, so we have to import everything.” Manufacturing sovereignty is the gap no spec can close.

Europe’s Moment, and Its Gap

The European growth story at OCP is real. Twenty-three percent of the organization’s membership is now European, 38% of its startup members are based in the region, and IDC projects $15 billion in OCP-recognized equipment deployed across Europe by 2029. NVIDIA is pushing adoption through two tracks: AI factories that repurpose national research centers for applied AI work with startups and SMBs, and Giga factories, purpose-built AI hyperscale data centers. Five to seven Giga factories are planned for Europe, though timelines have slipped past their original 2025 targets.

The TechArena Take

Going into Barcelona, we described the ODCE Vision as OCP’s bid to become structurally relevant at every layer of the AI infrastructure stack, grid to chip. The keynotes confirmed the ambition is sticking.

What the stage added that the specs alone cannot convey is urgency. NVIDIA’s rack density roadmap compresses every planning timeline in the industry. The power architecture conversation shifted from white papers to multi-vendor engineering, with Mount Diablo and Schneider’s system-level framework both presenting concrete paths. The NeoCloud panel proved that OCP specifications are translating into real deployment velocity outside hyperscale walls.

One moment stood out. Google observed that the combined standards corpus across Ethernet, PCIe, NVMe Express, DDR5, and OCP’s 200-plus work streams now exceeds 10,000 pages. Reading it all would take 370 hours. Google’s proposed solution: use AI to read the full corpus, catch conflicts across specifications, and track every decision and version. AI-assisted standards development, the speaker argued, is “a substantial opportunity for our industry.” When the complexity of building AI infrastructure has itself become an AI problem, you know the scale of what this community is trying to manage.

The Open Compute Project Foundation turns 15 this year, and at the OCP EMEA Summit, the organization is marking the milestone with an expanding portfolio of work. The catalyst is straightforward: AI infrastructure has made the data center a first-order concern for utility planners, data center operators, and network architects. OCP’s response, formalized last October as the Open Data Center Ecosystem (ODCE) Vision, is an expanded strategic framework that takes its mandate from the power grid all the way down to the silicon die.

The ODCE vision defined three strategic domains — AI data centers, cloud data centers, and the AI computing continuum (focused moving AI inference closer to end users) — across a technology scope spanning facilities and physical infrastructure, IT infrastructure, and systems management. The six months since have produced concrete work across all three.

Rethinking the Facility: Power, Physical Design, and Secure Data Exchange

On the facilities side, three efforts address the physical and operational foundations of next-generation AI data centers. The first is a new white paper that examines how data center facilities can be updated for direct current (DC) power distribution and provides an overview different power distribution architectures. It examines not only key design concepts, but tradeoffs across power conversion, energy storage integration, and regulatory considerations. The work is designed to build shared vocabulary among data center operators, equipment manufacturers, utilities, and power authorities, stakeholders who have historically operated in separate technical communities.

Second, an Open Data Center Roadmap contributed by Google identifies design principles for next-generation machine learning (ML) infrastructure. The design details physical interface specifications based on Google’s tensor processing unit (TPU) deployments that are expandable to industry GPUs, starting a collaborative workstream. It covers hot aisle containment, cable conveyance, power diversity tiers, and thermal management to support mega-scale ML hardware deployments.

The third tackles a subtler but increasingly critical problem: secure data exchange between operational technology and IT systems. As AI workloads demand real-time coordination across power, thermal, and mechanical systems, data from building management and power monitoring infrastructure must cross sensitive boundaries. An IT↔OT telemetry contribution proposes a zero-trust framework and standardized protocol for that exchange, enabling coordinated load shedding, real-time incident response, and end-to-end power usage effectiveness (PUE) optimization that current methods cannot support.

Inside the Data Hall: Open Networking at Scale

Two initiatives that address distinct networking challenges in AI cluster infrastructure are among the key outputs the community is highlighting at this spring’s summit. ESUN (Ethernet for Scale-Up Networking) released a base specification designed to improve efficiency, latency, and reliability to meet the demands of scale-up GPU connectivity. The specification evolves standard Ethernet for large GPU domains by replacing IP headers with a compact ESUN header, using media access control security (MACSec) at Layer 2, and treating all accelerators in a domain as a single logical GPU. The result is reduced idle compute time and faster end-to-end training and distributed inference. Let by Meta and Microsoft, more than 40 companies contributed to the base specification, and discussion for the next version is already underway.

Open Cluster Design, meanwhile, addresses the challenge of scale-out networking through the lens of cluster design. This strategic initiative addresses the fact that the majority of AI network implementations today are one-off designs. It aims to produce complete, procurement-ready system architectures to give operators a reusable technical foundation. White papers and reference architectures released this spring introduce the concepts of open pods and open clusters composed of open pods, allowing for modular cluster design. Broadcom and Celestica are among early contributors.

Beyond the Data Hall: Bringing Inference to the Edge

Finally, OCP’s AI Computing Continuum initiative, focused on bringing inference closer to end users, has made meaningful progress on multiple fronts. The project has established an alliance with the IOWN (Innovative Optical and Wireless Networks) Global Forum. It has launched sub-projects examining how to bring AI-native servers to edge data centers and at wireless open hardware for 6G wireless access. An AI-enabled open radio unit (O-RU) Experience Center developed with NVIDIA, Lattice Semiconductor, and University of New Hampshire recently opened as well, with details available in OCP’s Marketplace.

The TechArena Take

OCP has historically been strongest at the rack. What the ODCE Vision represents is a deliberate move to make OCP structurally relevant at every layer of the AI infrastructure stack, and the EMEA Summit in Barcelona this week is where much of that work goes on display. The Summit brings together global technical leaders to tackle data center sustainability, energy efficiency, and heat reuse, themes that sit squarely at the intersection of OCP’s expanded mandate and the region’s energy priorities. A marketplace with more than 160 solution providers and the announcement of diverse new members, including OpenAI, Crusoe, ABB, and Trane Technologies, signal how broad the OCP tent has become. For anyone tracking where open infrastructure standards are headed, Barcelona this week is the place to be.

When I attended the Xcelerated conference in New York in March, I did not expect to hear so many comments about human capital and the importance of culture.

The AI talent war is well documented. Engineers who can build and optimize large language models command seven-figure packages. Cloud architects, platform engineers, and quantum physicists top every recruiter’s wish list. The bidding wars make headlines.

What doesn’t make headlines is what happens after you win that bidding war. The real risk for most growing tech companies isn’t failing to hire the right people. It’s losing them 14 months later because nobody paid attention to the environment they walked into.

The instinct is to throw money at retention. Match the counteroffer. Add another equity refresh. But the data tells a different story. In its 2022 Talent Imperative report, the Institute for Corporate Productivity found that high-performance organizations attract and retain talent by competing on the strength of their cultures, not their compensation packages. The companies winning the long game on talent aren’t outspending their competitors. They’re out-leading them.

The Signals You’re Reading Are Already Old

“Top line growth can hide a lot of sins,” observes Laura St. John, my fellow TechArena Advisor and former Intel finance leader who helped drive financial strategy for $25 billion in transformative transactions. When the revenue line is climbing, it’s tempting to assume the organization underneath is healthy. But the cracks tend to show up in lagging indicators: attrition spikes, Glassdoor reviews that read like warning shots, cross-functional projects that stall because nobody trusts the other team’s priorities.

By the time those signals are loud enough to trigger action, the underlying dynamics have been corroding your culture for months. The damage is embedded. The best people, the ones with options, have already started taking recruiter calls.

So the question becomes: how do you build a system of leading indicators that catches cultural drift before it calcifies? How do you keep your teams engaged and committed while the business is moving at a pace that strains every process you have?

For growing companies navigating this landscape, the operative word is strategic. Resist the knee-jerk reaction. Resist the gut instinct. Get some data first.

3 Strategic Moves That Create Leading Indicators

1. Go deep with the people who matter most.

Identify the roles most critical to your company’s success over the next 12 months, then invest 30 minutes in a one-on-one conversation with each person in those roles. This isn’t a performance review. It’s a listening session. Frame it that way: “I want to make sure we’re creating the right environment for you to do your best work and continue growing here.”

Then ask questions designed to surface what compensation surveys never capture:

• What parts of their work feel most energizing right now?

• What would make their impact even greater over the next six to twelve months?

• What skills, experiences, or opportunities would matter most to them next?  

• What helps them do their best work, and what gets in the way?

And if the relationship supports it, ask the question most leaders avoid: If a competitor tried to recruit you, what would they have to offer that would make you consider it?

What you’ll learn from these conversations goes far beyond individual preferences. Patterns will emerge that reveal your culture’s actual operating state: where energy is flowing, where friction is building, and where the gap between your stated values and your lived experience is widest.

2. Diagnose before you prescribe.

Before rolling out any people initiative or culture program, run a quick pulse check. Five questions, scored on a five-point scale from strongly agree to strongly disagree, can tell you whether your foundation is solid or whether you’re about to invest in the wrong focus area:

• Do your people understand the organization’s mission and how their work contributes to it?

• Do leaders and managers demonstrate your values in their day-to-day behavior?  

• Do people feel safe speaking up with ideas, questions, or concerns?  

• Do teams across the organization work together effectively?  

• Is information communicated clearly enough for people to do their jobs well?

These five dimensions, mission clarity, leadership alignment, psychological safety, collaboration, and communication, form the bedrock of a healthy culture. Low scores on any one of them tell you exactly where to focus before spending a dollar on engagement programs or offsite retreats.

3. Invest in your managers before you need to.

Managers are the face of your company to every person they lead. They translate strategy into daily work. They set the emotional tone of their teams. They are, in practical terms, the culture.

And right now, most of them are stretched past capacity. They’re managing through reorganizations, integrating new AI tooling into workflows, onboarding at velocity, and trying to retain people while their own support systems are thin. In fast-growth environments, the gap between what’s asked of managers and what they’re equipped to deliver widens quickly.

A focused investment in building consistent management skills across this population can pay outsized dividends. It lays the foundation you’ll need when growth accelerates and you have to scale the organization without breaking what made it work in the first place.

The Operator Advantage

Most growing companies don’t have the internal resources to build these systems from scratch. They know the talent market is unforgiving. They feel the cultural cracks forming. But the operational expertise to diagnose and address those issues, to build the management bench and cultural infrastructure required for the next phase of growth, that’s a capability most organizations don’t carry in-house.

This is where operators who have built and scaled world-class technology organizations bring a different kind of value than traditional consultants. They’ve sat in the seats their clients sit in. They’ve navigated the same talent pressures, the same scaling challenges, the same tension between moving fast and building something durable. That lived experience is what turns advice into action.

A single 8MP camera running at 30fps with raw output can saturate a 1 Gbps Ethernet link. Add lidar, multiple high-resolution cameras, radar, and the internal traffic between SoCs in a zonal gateway, and the math stops working. That is the problem facing every team designing next-generation E/E architectures for the software-defined vehicle.

Centralized E/E architectures today run almost exclusively on TSN Ethernet, and that foundation is reaching its limits. Time-Sensitive Networking delivered deterministic latency and domain consolidation, the big architectural win of the last decade. But TSN was designed for a vehicle that looked very different from what is currently on the drawing board. It was defined for distributed ECUs, domain controllers, and sensor payloads measured in megabits. The SDV does not look like that. It is a compute-centric architecture where raw sensor data floods into a centralized processing cluster, gets fused, processed, and acted upon in real time. That cluster needs bandwidth. Lots of it. And TSN, for all its elegance, tops out at 1 Gbps per link in most production implementations, with 10 Gbps still rare and expensive in automotive-qualified silicon. The growth in data and requisite processing is not incremental.

Why Ultra Ethernet

The Ultra Ethernet Consortium is not just pushing faster PHYs, though the 800 Gbps roadmap certainly gets attention. The architectural bet is on a fundamentally different way of moving data: congestion management that works at scale, packet spraying across multiple paths, and a transport layer that does not collapse under peak ingress traffic when every sensor decides to talk at once.

For automotive, the headline is not the top-line speed. It is the combination of high bandwidth with predictable latency under load. TSN delivers predictability by reserving slots and policing traffic. That works when the topology is controlled and the flows are relatively static. Ultra Ethernet delivers predictability by building a network that does not congest, one that supports dynamic load balancing, end-to-end flow control, and a fabric that behaves more like the data center networks running AI workloads than a traditional automotive bus.

That is the shift: from managed scarcity to abundant, well-behaved capacity.

Centralized Compute Needs a Different Pipe

The premise of the zonal or centralized architecture is that intelligence lives at the core and raw sensor data is forwarded to the centralized processing complex. This only works if the network between the edge and the core does not become a bottleneck.

With TSN, the design pattern forces a choice: either pre-allocate bandwidth for worst-case scenarios, wasting capacity in the average case, or accept that some traffic gets delayed or dropped when the network gets busy. Neither is acceptable for a safety-critical system. Sensor fusion relies on data that is continuously aligned in time from multiple sensor types with different rates, all feeding a coherent model of the vehicle's surroundings.

Ultra Ethernet's congestion signaling and adaptive routing mean the network can absorb bursts without deterministic guarantees degrading. The fabric itself becomes less of a constraint on the software architecture. Engineers can design the compute topology based on the performance that algorithms demand, rather than designing around what the network can carry.

The Software-Defined Part Depends on the Network

The vehicle may be called software-defined, but the degree of software-defined is bound by hardware flexibility. If the network is statically configured with TSN schedules and gated queues, the software is defined at build time, not run time. Every time a new sensor modality gets added, a fusion algorithm is updated, or compute load shifts between zones, there is potential for re-validating a TSN configuration that took months to certify.

Consider a concrete scenario: an OEM pushes an over-the-air update that shifts a parking-assist algorithm from one zone controller to another. Under TSN, that new traffic pattern may require a full re-certification cycle. The network becomes a gate on the speed of software iteration.

Ultra Ethernet's approach, with dynamic congestion management, flexible topologies, and a transport that does not require per-flow reservation, turns the network into a programmable resource rather than a fixed contract. That is what software-defined actually means: the ability to reconfigure, update, and evolve the vehicle's capabilities without touching the wiring harness.

TSN Is Not Going Away Overnight

TSN has a large installed base, mature tooling, and automotive certification that OEMs understand and require. Nobody is ripping out functioning TSN networks next year. The transition will be evolutionary, not revolutionary.

Ultra Ethernet will most likely appear first in the highest-bandwidth paths: camera and lidar feeds into central compute, chip-to-chip links within the compute cluster, and the backbone between zonal gateways. TSN will continue to handle lower-bandwidth, hard-real-time control traffic where the scheduling model is genuinely appropriate.

The smart architecture is not one or the other. It is a hierarchy where Ultra Ethernet carries the heavy, bursty, data-center-like traffic and TSN handles the deterministic control loops that do not need multi-gigabit throughput. But over time, the balance will shift. As sensor resolutions continue to grow, AI models expand, and over-the-air updates demand more internal bandwidth for validation and rollback, the Ultra Ethernet footprint will expand with them.

The Ecosystem Momentum Is Building

The Ultra Ethernet Consortium has pulled in the right players: semiconductor vendors, hyperscaler networking expertise, and increasingly, automotive-specific contributors who understand functional safety and ASIL requirements. Early automotive-qualified silicon is emerging that can bridge these worlds, pairing the raw performance of Ultra Ethernet with the safety mechanisms and qualification rigor that automotive demands.

This is not a science project. The standards are converging, the PHYs are maturing, and the first production programs targeting 2028 to 2029 vehicle platforms are already making architectural commitments. Teams in the early stages of defining next-generation E/E architectures need to be modeling Ultra Ethernet into their trade studies now, even if the initial implementation is hybrid.

The Bottom Line

TSN Ethernet was the right technology for the domain-controller era and has provided a solid on-ramp to centralized architectures. But the compute-centric, AI-driven SDV generates data at a scale and burstiness that TSN was never designed to handle. Ultra Ethernet is quickly being recognized as the networking layer this new era requires.

The question for architects is not whether to adopt it. The question is where to deploy it first and how to manage the transition without disrupting the safety-critical foundations that TSN has already established. The vehicles that win in the next decade will be the ones whose network fabrics do not artificially constrain overall vehicle safety performance, consumer satisfaction, or cost.

An enterprise AI assistant generated a polished recommendation in under two seconds. In practice, teams could still spend the next 40 minutes verifying whether that output is safe to use.

That delay was not caused by model latency or infrastructure failure. It came from the work that followed. Teams had to reconstruct where the answer came from, check whether the underlying records were still current, confirm that no restricted content had slipped into the response, and make sure the recommendation could be explained if compliance or operations asked questions later.

The model was fast. The trust was slow.

That gap is becoming one of the most important operational realities in enterprise AI, yet most organizations do not measure it directly.

For years, data platform metrics were built for systems humans read. Uptime showed whether the platform was available. Latency showed whether queries returned quickly. Throughput showed whether pipelines could keep up. Freshness showed whether the underlying data was recent. These metrics still matter, but they only tell us whether a system is functioning. They do not tell us whether its output can be acted on with confidence.

AI systems changed that equation. They do not just present information for a person to interpret. They summarize, recommend, rank, and influence decisions directly. In that environment, the more important question is no longer only how fast an answer arrives. It is how quickly that answer becomes trustworthy enough to use.

I think of that interval as time-to-trust: the time between an AI output being generated and that output becoming trustworthy enough to act on.

In practice, that trust usually depends on four checks:

• Provenance: where did this come from?
• Currency: is the underlying context still valid?
• Policy compliance: is this output allowed to leave the system?
• Explainability: can we reconstruct how it was assembled? Each of these checks introduces friction. Most organizations have never measured how much.

Why Existing KPIs No Longer Tell the Full Story

Traditional platform metrics were designed for an earlier operating model. In the dashboard era, a human analyst usually sat between the data and the decision. If something looked wrong, there was time to pause, investigate, cross-check, and add context before anyone acted.

AI compresses that distance.

An assistant can summarize customer history for a service representative. A copilot can suggest operational responses based on live events. A recommendation system can rank the next action for a relationship manager. In each case, the output is no longer passive. It enters a workflow quickly and creates pressure to move faster.

That is where the blind spot in traditional KPIs becomes obvious.

A system can have excellent uptime and still produce outputs no one is comfortable using. It can have low latency and still force teams into long validation loops. It can have fresh data and still fail because no one can explain how the answer was assembled or whether the response crossed a policy boundary.

The real delay is not always generation time. It is verification time.

Time-to-Trust Is an Architectural Outcome

Time-to-trust is not a reporting problem. It is an architectural one.

Organizations do not reduce trust delays by adding more dashboards after the fact. They reduce them by engineering systems that make verification faster and more reliable from the start. Low time-to-trust emerges from the design of the data platform, the context pipeline, and the runtime controls surrounding the model.

Consider the first trust question: where did this answer come from?

If lineage is incomplete, retrieval is opaque, or the output cannot be tied back to specific records or documents, that question becomes a manual investigation. Teams search logs, compare versions, and message multiple owners just to reconstruct provenance. What looks like an AI trust issue is really a metadata and observability issue.

Now consider the second question: is the context still current?

Many enterprise AI failures are not hallucinations in the usual sense. The model is often reasoning over information that is stale, incomplete, or out of sync with current policy and operations. If embedding refresh cycles are inconsistent, if context assembly is not versioned, or if source updates do not propagate cleanly, trust slows down because every output must be treated as potentially outdated.

The third question is policy. Is the output safe and appropriate to use?

That answer depends on runtime controls. If policy enforcement is scattered across prompts, informal conventions, and manual review, the burden falls back on the user to catch mistakes. But if the system includes policy-aware orchestration, redaction checks, scoped retrieval, and output controls, policy verification becomes faster because the platform has already narrowed the risk surface.

The fourth question is explainability. Can the organization reconstruct how the answer was assembled?

This is not about turning every AI interaction into a research paper. It is about having enough operational traceability to support real decisions. Which sources were retrieved? Which rules were applied? What version of context was used? Which guardrails were triggered? If those answers are available, trust moves faster. If they are missing, confidence slows to a crawl.

This is why time-to-trust belongs in the same conversation as lineage, ownership, freshness SLAs, metadata quality, contracts, and observability. It is not a soft metric. It is the visible outcome of infrastructure choices.

Why High Time-to-Trust Kills Adoption

When enterprise AI pilots stall, the explanation is often framed in terms of model quality. Leaders say the responses were inconsistent. Users say the system felt unreliable. Technical teams say they need more tuning, better prompts, or a stronger model.

Sometimes that is true. Often it is incomplete.

In many organizations, the real problem is simpler: trusting the output takes too long.

The system performs well in demos because the environment is controlled. The documents are curated. The use case is narrow. The audience is forgiving. Once the system enters a live workflow, the real world shows up. Data sources evolve. Permissions vary. Records conflict. Policies change. Edge cases multiply. Suddenly every meaningful answer comes with follow-up questions the platform cannot answer quickly.

At that point, adoption weakens for understandable reasons. Users do not reject the system because they dislike AI. They reject it because trusting it takes too long.

A slow trust loop turns every output into a follow-up exercise. Frontline users stop relying on the assistant. Managers hesitate to embed it into core workflows. Risk teams demand tighter controls. Engineering teams spend more time defending outputs than improving them.

Enterprise AI often fails quietly this way. Not with a crash. Not with a scandal. With hesitation.

Trust Friction Usually Points Back to the Data Layer

One reason this problem is easy to misdiagnose is that the symptom appears at the AI output layer while the root cause often lives in the data layer beneath it.

A recommendation is hard to trust because the source system has unclear ownership.

A summary is hard to trust because document refresh pipelines lag behind policy changes.

A generated response is hard to trust because the retrieval layer cannot show which source fragments were used.

A workflow suggestion is hard to trust because there is no contract defining which fields are authoritative and which are optional.

These are not model problems in the narrow sense. They are platform maturity problems.

For years, many data platforms could tolerate ambiguity. Business definitions drifted. Data products lacked clear ownership. Transformations accumulated without strong contracts. Reports still got delivered, and human analysts learned where the rough edges were. AI reduces the room for that kind of informal adaptation. When outputs are delivered directly into workflows, ambiguity becomes operational drag.

That is why time-to-trust is such a useful lens. Instead of asking only whether an AI response is impressive, it asks how much architectural friction surrounds that response before it can be used.

That is a far more revealing question.

How to Start Measuring It

Organizations do not need a large transformation program to begin. They can start with one workflow.

Pick a real AI-enabled use case that matters. It could be an internal copilot, a support assistant, an operations alerting system, or a retrieval-based knowledge tool. Then focus on the outputs that trigger the most scrutiny or require the highest confidence.

For those outputs, measure how long it takes to answer the four trust questions outlined above: provenance, currency, policy compliance, and explainability.

The time required to answer those questions is a practical proxy for time-to-trust.

What matters is not just the number. It is where the delay comes from.

In many environments, the biggest trust bottleneck is not policy review. It is provenance reconstruction. Teams discover they can answer whether an output is allowed faster than they can explain which records, documents, or retrieval steps produced it. That points directly to a lineage and observability gap, not a model gap.

Once those bottlenecks are visible, the work becomes concrete. Strengthen lineage capture. Version context pipelines. Clarify ownership. Add retrieval traces. Tighten contracts around critical fields. The goal is not perfection. It is reducing the time required to move from output to confident action.

The Executive Conversation Needs to Change

Enterprise leaders often ask whether their AI is accurate, safe, or ready for scale. Those are reasonable questions, but they are incomplete.

A more useful question is this: how long does it take before our AI outputs become trustworthy enough to use?

That question changes the conversation immediately.

It pushes teams beyond benchmark thinking and into operating discipline. It shifts focus from isolated model performance to the full system around the model. It makes trust concrete rather than abstract. And it creates a bridge between technical architecture and business adoption.

Accuracy matters. Speed matters. Cost matters. But in production environments, none of those alone determine whether AI becomes a reliable part of decision-making. Systems create value only when people and processes can use their outputs with confidence.

The next phase of enterprise AI will not be defined only by who produces the fastest answer. It will be shaped by who can make that answer trustworthy in the shortest time.

Because in the end, a system that responds instantly but takes forty minutes to trust is not really moving at AI speed at all.

Healthcare organizations are witnessing a transformation as AI agents, which are autonomous systems that reason, plan, and execute complex workflows, move from research labs into clinical settings. The demos are compelling. The potential is enormous. Yet there’s a gap that many organizations are discovering the hard way: the chasm between a successful pilot and sustainable production deployment. This gap isn’t about technology capability; it’s about governance.

The fundamental difference between agentic AI systems and traditional automation is: They operate with context-awareness, make decisions dynamically, and adapt to clinical realities in real-time. This autonomy is their strength, but it’s also what makes governance essential. A recent report found that 62% of healthcare leaders say fragmented data is blocking AI scaling. This fragmentation isn't just a technical problem; it's a governance problem. Agentic AI systems that work beautifully in isolated pilots often struggle when deployed across diverse clinical environments, data sources, and workflow patterns.

Here are three reasons why governance is the critical bridge that enables healthcare organizations to move from compelling AI demos to sustainable production value.

1. Governance ensures safety and compliance in high-stakes decisions

Healthcare operates under strict regulatory standards, particularly HIPAA, which mandates strict controls over Protected Health Information (PHI). When an AI agent makes a decision, whether it's flagging a critical lab result, generating a clinical note, or processing a prior authorization, that decision must be traceable, auditable, and correctable. Unlike traditional software that follows deterministic paths, agentic systems make probabilistic decisions based on context. Governance ensures these decisions remain within safe, clinically validated boundaries while controlling access to PHI through the principle of least privilege: agents only access the minimum data and tools necessary for their function.

To achieve this level of safety, a robust governance framework must provide:

• Complete audit trails for every agent action, including PHI access
• Human oversight at critical decision points
• Access controls that enforce the principle of least privilege for both data and tools
• PHI data handling compliant with the HIPAA Security Rule, including encryption, access logging, and data minimization
• Compliance with HIPAA, HITECH, and other healthcare standards

Consider a clinical documentation agent that synthesizes patient data into progress notes. Without governance, it might miss critical context, generate conflicting recommendations, or access PHI beyond what's necessary, violating HIPAA's minimum necessary standard. With proper governance, the same agent operates within defined guardrails, maintains complete audit trails of PHI access, enforces least-privilege access controls, and ensures human oversight at critical decision points, turning a potential liability into a trusted clinical tool.

2. Governance builds trust through transparency

Healthcare workers are rightfully skeptical of new technology. Years of experience with rigid, rule-based systems have taught them that automation often creates more work, not less. Agentic AI can break this pattern, but only if clinicians understand how it works and trust its outputs.

To bridge this trust gap, governance frameworks must prioritize transparency through:

• Decision transparency showing what data was accessed and how recommendations were derived
• Understanding of why agents flagged items for review, with confidence scores where applicable
• Override mechanisms allowing clinicians to reject or modify agent outputs

This transparency enables clinicians to make informed decisions about when to rely on the agent and when to override it. Organizations that invest in governance frameworks early find that their agents gain clinician trust and adoption more quickly, transforming skepticism into confidence.

3. Governance enables scale across fragmented healthcare environments

The scale challenge in healthcare AI isn't just about technology; it's about governance. While agentic AI systems may excel in controlled pilot environments, they face significant hurdles when deployed across diverse clinical settings. Governance frameworks address this by establishing consistent standards, defining clear escalation mechanisms, and creating monitoring systems that detect performance degradation before it impacts care.

Administrative burden consumes an estimated $265 billion annually in healthcare. Consider two examples: First, properly governed authorization agents can process routine cases automatically while flagging complex cases for human review, reducing processing time by 50-70% without increasing error rates. Second, when properly governed, documentation agents can reduce charting time by 30-40% while improving documentation quality and completeness. These gains only materialize when governance frameworks enable reliable scale across diverse clinical environments, proving that governance isn't a barrier to value, but the pathway to achieving it.

The ADDM Model: A Lifecycle Approach to Governance

Building effective governance requires a comprehensive framework built on three core pillars: Security & Compliance (protecting PHI through HIPAA-compliant access controls, implementing least privilege for data and tools, encrypting PHI per Security Rule requirements, and maintaining audit logs), Value-Driven Impact (anchoring initiatives to clear business objectives), and Accuracy & Reliability (ensuring consistent, predictable performance).

The key is to integrate governance at every stage of your agent's journey. Think of it as a continuous cycle rather than a linear process—what we call the ADDM model (Analyze, Develop, Deploy, Manage). Start by analyzing whether AI is the right solution and conduct risk assessments including data privacy and security per NIST guidelines. During development, establish evaluation metrics, test with diverse scenarios, implement guardrails, and design access controls enforcing least privilege for data and tools. Before deployment, validate load capacity, complete user acceptance testing, establish human-in-the-loop checkpoints, and verify HIPAA Security Rule compliance  including encryption, audit logging, and access controls. Once in production, manage through continuous monitoring, feedback collection, periodic access reviews to maintain least privilege, and iterative model updates.

This requires clear organizational structure with three tiers: strategic leadership to set vision and policy, operational teams to execute day-to-day compliance, and technical teams to monitor and validate agent performance. The most successful organizations treat governance not as a compliance burden, but as a strategic capability that enables innovation while managing risk.

Beyond Compliance: Governance as a Strategic Capability

Governance is the critical bridge that enables healthcare organizations to move from compelling AI demos to sustainable production value. It ensures safety and compliance in high-stakes decisions, builds trust through transparency, and enables scale across fragmented healthcare environments.

Without proper governance, organizations face systems that create new risks, clinician resistance, and regulatory issues requiring costly remediation.

Agentic AI represents a fundamental shift in how healthcare can leverage technology. These systems can augment human capabilities in ways that traditional automation cannot. But realizing this potential requires thoughtful governance. The organizations that succeed won't be those with the most advanced AI technology; they'll be those that recognize governance as a strategic capability, not a compliance burden.

The demos are compelling. The technology is ready. The question isn't whether agentic AI will transform healthcare; it's whether organizations will build the governance frameworks needed to make that transformation safe, trusted, and sustainable. Start building your governance framework today. The organizations that invest in governance early will be the ones that realize the full potential of agentic AI in healthcare.

A new computing paradigm is reshaping the foundations of technological innovation and industrial competitiveness. As AI adoption accelerates, researchers, academics, governments and policy makers, as well as forward-looking industry decision-makers are confronting the limitations of classical computing. Quantum computing, particularly when combined with high-performance computing (HPC) in hybrid systems and architectures, is gaining real momentum.

The expectation is that the economic impact will be significant. According to Qureca, global quantum research and development investments now surpass $55.7 billion, with projections placing the global quantum market at $106 billion by 2040. Finance, healthcare, manufacturing, energy, communications, defense, security, and space are among the sectors that can benefit from quantum advancements the first, as they rely on solving complex optimization, simulation, and high-risk problems where classical computing reaches its limits.

Although we are still far from demonstrating large-scale commercial value from quantum applications, organizations that want to lead quantum innovation in the near future should invest today in developing capabilities, building expertise, and positioning themselves as early adopters.

Data Centers: Where the Convergence Happens

Data centers sit at the center of this transition. They provide the infrastructure and system architecture for convergent technologies, including AI, HPC, supercomputers, and early quantum applications, that will underpin future technological economies.

Quantum computing cannot operate in isolation. It must be integrated into broader computing environments. This positions data centers as convergence hubs where HPC, AI, supercomputers, and quantum and hybrid systems can coexist and interact. The industry is already seeing some examples of this evolution: developed internally by large companies as IBM Quantum System One, Google Quantum AI, initiatives supported by the governments like EuroHPC Joint Undertaking, or collaborative projects such as French electric company EDF partnering with quantum startups Quandela, Alice & Bob, and Pascal to optimize energy management. Hybrid architectures are being tested in specialized facilities that combine classical supercomputers with quantum processors. Data centers are developing “quantum-as-a-service” models, enabling enterprises to access quantum capabilities without owning dedicated hardware (e.g. Scaleway’s Quantum as a service). Energy-efficient infrastructure is becoming a differentiator, as quantum systems often require highly controlled environments. For data center operators, this shift represents an opportunity to evolve from infrastructure providers to strategic partners in innovation, delivering quantum capabilities alongside traditional services.

The Near-Term Future Is Hybrid

The trajectory of classical computing development is slowing. We observe mainly incremental improvements in architectures and systems, while significant performance gains become more energy-consuming and less cost-effective.

Quantum computing offers a fundamentally different approach. Rather than processing information sequentially, it uses quantum states to explore multiple possibilities simultaneously, with multiple possible outcomes. In the near term, it’s more likely than not that we must rely on hybrid technologies. Hybrid computing combines the strengths of HPC systems with quantum processors for optimization and simulation. This model allows companies to explore with emerging technologies and create value today while preparing for more advanced quantum capabilities tomorrow.

Investments and Early Adopters

Current investments in quantum computing echo the early bets on cloud and artificial intelligence. Those were risky, uncertain projects at the time. They became transformative in the long run.

Governments are investing heavily. In 2025, the EU adopted a Quantum Strategy that leverages scientific excellence and R&D, quantum infrastructure, ecosystem strengthening, skills development, and the integration of sovereign quantum capabilities into space, security, and defense strategies and other sectors. The European Quantum Flagship, backed by a €1 billion budget, is accelerating the development of quantum technologies (quantum computing and simulation, sensing, metrology and quantum communication) across research, industry, and the public sector. In the United States, estimated investment in quantum exceeds $7 billion.

At the organizational level, early investment carries distinct strategic advantages. Early adopters build proprietary knowledge, secure scarce talent, and shape emerging ecosystems. When breakthroughs occur, these organizations have the ability to scale and adjust to market needs quickly. Companies that delay investing in quantum R&D risk technological dependency, loss of competitive advantage, and higher costs when quantum technologies do eventually enter the advanced technological readiness and commercialization phase.

Organizational Readiness as Differentiator

The real differentiator lies in organizational capacity to be prepared for the emerging technologies. Companies investing in quantum today are building a comparative advantage through the capabilities and skills essential for the future. They are developing talent and expertise, either by training internal teams or partnering with quantum research institutions, startups and industries. They are investing in innovative projects that demand experimentation, long-term thinking, cross-functional collaboration, and often academia-industry partnerships and collaborative projects. These organizations are gaining strategic positioning in the ecosystem by working alongside startups, universities, and technology providers.

These capabilities extend well beyond quantum technologies. They help organizations adopt emerging technologies broadly and stay competitive and innovative over the long term.

Investing Today to Compete Tomorrow

Quantum and hybrid computing are no longer a distant prospect. They are rapidly becoming the foundation for how complex problems could be solved across finance, pharmaceuticals, energy, defense, security, and aerospace, among others. This is not simply about technological leadership. It is about long-term competitiveness.

Companies that explore and invest today in quantum and hybrid computing are building the expertise, partnerships, and innovation capacities that will define future market leaders. Data centers play a pivotal role, enabling the convergence of HPC, AI, and quantum and hybrid systems while making advanced computing accessible and scalable across industries.

Organizations that failed to embrace past technological breakthroughs early found themselves struggling to remain competitive in the long term. Quantum computing appears to follow a similar trajectory, with potentially even greater impact. In an environment where innovation is the primary driver of competitive advantage, investing in quantum and hybrid computing is becoming a strategic necessity.

The chips powering the next wave of AI infrastructure are getting harder to design. And the tools used to create them are starting to think for themselves.

Synopsys today announced a broad expansion of its collaboration with TSMC, spanning AI-powered EDA flows, silicon-proven IP across advanced and specialty nodes, and new design enablement for co-packaged optics. The announcement, timed to TSMC’s 2026 Technology Symposium in North America, covers TSMC’s 3nm and 2nm families along with A16 (with Super Power Rail) and A14. But the real headline sits in a single word that keeps surfacing across the semiconductor design world: agentic.

Agentic EDA Hits the Design Floor

Synopsys is collaborating with TSMC on what it calls “agentic run assistance” inside its Fusion Compiler, targeting TSMC’s A14 process using the NanoFlex Pro architecture. In practice, that means the tool can now identify timing improvement opportunities at different stages of the design flow on its own, rather than waiting for an engineer to manually intervene at each checkpoint. The goal: better power, performance, and area results with fewer human-in-the-loop iterations.

This is a meaningful step beyond the optimization work Synopsys has done with its DSO.ai technology over the past several years. Where DSO.ai focused on tuning parameters within a defined design space, agentic run assistance implies the tool is making multi-stage decisions about where and when to act across the flow. AI-assisted physical verification in Synopsys IC Validator is also progressing, aimed at accelerating the identification and resolution of design rule violations for faster tapeout quality.

The multiphysics signoff portfolio is expanding in parallel. Synopsys RedHawk-SC for digital power integrity, Totem-SC for analog power integrity, and HFSS-IC Pro for electromagnetic extraction now span TSMC nodes from A16 through A14. Totem-SC provides ultrahigh-capacity analog power integrity signoff for large N2-based designs, while PathFinder-SC extends multi-die electrostatic discharge signoff coverage to N2. Cloud-based multiprocessor and GPU acceleration shortens turnaround for teams iterating across thermally constrained 3D assemblies.

Multi-Die Design and a Broader IP Portfolio

Chip architectures are fragmenting by design. Multi-die systems built on advanced packaging let designers mix process nodes, integrate heterogeneous functions, and scale beyond the limits of a single monolithic die. Synopsys is leaning into that shift across both its EDA tools and its IP catalog.

The company’s 3DIC Compiler platform now supports TSMC’s CoWoS packaging technology at 5.5x reticle interposer sizes, a scale that tracks with the massive interposers shipping inside today’s flagship AI accelerators. As a unified exploration-to-signoff platform, 3DIC Compiler integrates with RedHawk-SC, RedHawk-SC Electrothermal, and Ansys HFSS software to deliver multiphysics analysis for thermal, power, and high-speed signal integrity in one environment.

On the IP side, Synopsys announced several firsts. Its UCIe IP ASIL B solution on TSMC’s N5A process is the only end-to-end IP of its kind designed for safety-critical automotive multi-die systems, a category that barely existed two years ago but is gaining real traction as automakers adopt chiplet architectures. The company also completed silicon bring-up of the industry’s first low-power M-PHY v6.0 IP on TSMC’s N2P process, pushing next-generation storage connectivity forward for smartphones and mobile applications. Across TSMC’s N5, N3P, and N2P processes, Synopsys achieved first-silicon milestones on PCIe 7.0, HBM4, 224G, DDR5 MRDIMM Gen2, LPDDR6/5X/5, UCIe 64G, and M-PHY v6.0 IP.

Co-Packaged Optics Gets a Design Tool Chain

Perhaps the most forward-looking piece of the announcement is Synopsys’ multiphysics design enablement for COUPE, TSMC’s co-packaged optics platform. The enablement spans Ansys Zemax OpticStudio for optical path simulation, Ansys Lumerical for photonic device simulation, HFSS-IC Pro for electromagnetic extraction, and RedHawk-SC Electrothermal for thermal and electrical co-simulation.

Synopsys also introduced a 224G IP solution that supports co-packaged optical Ethernet and UALink, targeting the bandwidth demands of next-generation electro-optical systems in AI data centers.

When an EDA vendor starts building full simulation flows for a technology, it signals that commercialization is no longer theoretical. Co-packaged optics has been a conference-circuit favorite for years. Now it has a design tool chain.

The TechArena Take

Three threads in this announcement deserve attention beyond the product specifics.

First, the agentic language matters. The semiconductor industry is moving past AI-as-optimizer toward AI-as-collaborator in the design flow. Synopsys is not alone in this pursuit, but its depth of integration with TSMC’s most advanced nodes gives it a proving ground that few competitors can match. If agentic run assistance delivers measurable PPA gains on A14, expect the rest of the EDA ecosystem to accelerate their own autonomous workflow roadmaps.

Second, the Ansys acquisition is paying visible dividends. The multiphysics coverage in this announcement, spanning optical, electromagnetic, thermal, and electrical simulation, would not have been possible under one roof before the merger closed. That vertical integration from RTL to photonics simulation is becoming a genuine differentiator, particularly as chip designs grow more three-dimensional and multi-domain.

Third, the co-packaged optics enablement is a quiet signal worth watching. Bandwidth scaling in AI clusters is approaching the practical limits of electrical interconnects. The fact that Synopsys, TSMC, and the Ansys simulation stack are now aligned on COUPE design flows suggests the industry’s timeline for production co-packaged optics may be shorter than many assume. The 224G IP supporting both optical Ethernet and UALink adds a concrete building block to what has, until recently, been mostly a research narrative.

Taken together, this announcement reflects a broader truth about the AI infrastructure buildout: the tools that design AI chips are themselves becoming AI-driven, and the companies that control that feedback loop will shape how fast the next generation of silicon reaches production.

VAST Data announced today that it has closed a Series F financing round, including primary and secondary capital of approximately $1 billion. The financing round creates a $30 billion valuation for the AI operating system company, which more than triples its $9.1 billion Series E valuation from late 2023. The round was led by Drive Capital, with Access Industries as co-lead and participation from new and existing investors such as Fidelity Management & Research Company, NEA, and NVI DIA.  

This impressive headline number has the support of strong reported underlying financials. VAST has surpassed $4 billion in cumulative bookings and ended its most recent fiscal year with more than $500 million in committed annual recurring revenue, alongside positive operating margin and free cash flow.  

In a blog post accompanying the announcement, VAST Data Co-Founder Jeff Denworth said, “What excites investors about VAST is our unprecedented mix of growth and profitability, demonstrating to the world that a radically disruptive product and focused team can break fundamental business tradeoffs.” The company’s Rule of X score (calculated as the sum of revenue growth rate plus the last twelve months free cash flow margin) is 228%, a remarkable 5 times greater than the 40% typically considered healthy.

So why take the investment? Denworth cites two reasons:

1. The investment is a “signal to the world” of the strength of worldwide adoption of VAST’s AI Operating System.
2. The capital will support the company’s scaling ambitions and ability to make investments that can accelerate market adoption.

The Architecture Bet Is Paying Off

VAST’s business story traces back to a technical decision made in 2016: designing a distributed systems architecture known as DASE (for disaggregated shared everything) from scratch, specifically for the parallelism demands of deep learning. From that foundation, VAST has created a full-stack computing platform for deep learning, including the VAST DataStore, DataBase, DataEngine, and DataSpace. Earlier this year, the company announced new capabilities for the agentic AI era to build a “thinking machine,” or a system that governs, evaluates, and improves on AI pipelines automatically. VAST reports that today the AI factories that it supports have over 1 billion CUDA cores or over 1 million tensor cores, all accessing a single VAST data platform.

The TechArena Take

As Denworth explicitly said, this funding round is a market signal. The company’s valuation, its Rule of X score, and its underlying financials are proof that the company’s vision can translate into reality. The DASE architecture bet, made in a seemingly distant past when Sam Altman and Elon Musk were working side-by-side at OpenAI, is now paying off 10 years on as enterprises discover that legacy data infrastructure simply cannot keep pace with agentic AI demands. The company seems to have arrived at this moment with exactly the right product.

The open question is what VAST Data’s competition can offer. As Denworth noted in his blog, the company operates in an odd place: while it competes with companies up and down the data stack, it has no direct analog competitor outside of hyperscalers that put together many services to create an equivalent to VAST’s unified platform. For now, that position is difficult to replicate quickly. Unified architectures are not assembled overnight, and VAST’s decade-long head start shows in both the product and the financials. The gap may not last forever, but VAST’s financial strength gives it the runway to keep widening it.

Read more from VAST in their press release.

The tech landscape is accelerating faster than traditional consulting was designed to handle. As trillions of dollars flood into AI infrastructure and organizations race to define their positions in a shifting ecosystem, the distance between strategy and execution has become one of the costliest gaps in business.

With the recent launch of the TechArena Advisory, we are featuring a series of 5 Fast Facts Q&As to highlight the operators bringing C-suite-grade intelligence to this new function. We recently sat down with our Founder and CEO, Allyson Klein, who built TechArena on a conviction that has only sharpened with time: in a world redefining what intelligence means, human connection still matters. In this edition of our Q&A series, she discusses the collapse of traditional consulting models, the most acute pressure points facing business leaders right now, and what it means to drive disproportionate growth for clients.

Q1: What has changed in the tech landscape that made the Advisory a priority for you?

The pace changed. What used to be multi-year design cycles and simplified paths to market, has changed into a frenetic pace of innovation to serve the demand for AI. Organizations are racing to deploy trillions of dollars of capital equipment, making consequential decisions faster than at any point in history.

The pressure revealed that traditional consultancy models built on outside-in analysis were not designed for this moment. Outside-in frameworks with no clear integration path simply do not hold up when the stakes are this high and the clock is moving this fast. The Advisory practice is a direct response to that gap. We bring operators who have lived in these environments, made these calls, and steered the foundational companies that architected the modern tech stack.

Q2: What does your experience bring to this moment?

I spent my career at the friction point where plans meet P&L, in some of the most demanding environments in tech. I drove data center and edge marketing at Intel and led marketing and communications at Micron. Both roles put me at the table where decisions were made, where go-to-market battles were won or lost, and where the story you told about your technology not only determined your product success, but the industry’s trajectory.

When I founded TechArena in 2022, I carried all of that forward. We have collaborated with over 100 leading technology companies, helping them claim market advantage in a landscape that was not waiting for anyone to catch up. Our work crystallized my thinking about what businesses actually need right now: operating experience from someone who has sat in your seat and can help you move forward with confidence.

Q3: What challenges are business leaders facing that align with your practice areas?

The pressure is simultaneous and everywhere. Silicon design cycles are accelerating, and data center buildouts that once took five years are happening in 18 months. Leaders are being asked to get product strategy, competitive positioning, go-to-market, and financial governance right, all at once.

The executives I talk to are not short on ambition or know-how. They are short on the right kind of counsel, someone who has navigated this specific terrain at scale and can step in immediately, assess the situation, and turn potential into real business value. That kind of advisor changes the equation in ways that static analysis cannot replicate.

Q4: Are there key areas that you see most pertinent right now?

Go-to-market is probably the most acute pain point. Companies are launching products into markets that are still being defined, competing for mindshare with dozens of well-funded players, trying to build routes to market that did not exist two years ago. Getting that right matters enormously for where a company lands in the ecosystem hierarchy.

Competitive narrative is close behind. In a landscape where technical differentiation is hard to sustain, the story you tell about your position in the value chain can be the deciding factor in whether customers, partners, and investors align behind you.

Organizational readiness is moving up fast on the list too. Companies that scaled aggressively in recent years are now restructuring for AI-native operations. Leadership development and cultural transformation are real operational challenges, not soft-skills exercises, and that is an area where our advisors bring a depth of experience that is hard to find anywhere else.

Q5: What are the outcomes you're targeting to drive?

The advisors we have brought together have grown multi-billion dollar businesses and led organizations through the defining technology inflections of the last two decades. I can’t wait to see the impact that these proven operators can deliver to drive disproportionate growth for our clients.

Early results are already proving the model. For example, Axelera AI came to us with a specific market opportunity. The Advisory team researched their position, helped frame the opportunity clearly, and delivered an action plan they could execute. That is exactly what we are built to deliver, and it is the standard every engagement will be measured against.  

If the thought of accelerating your team’s ambitions resonates with you, come check us out.