I think non-profit restructuring shifts governance burden to the foundation board, not shareholders. My sense is enterprise leaders must verify if “public-benefit” status ensures long-term safety alignment. What concerns me is that microsoft’s diluted stake reduces direct control but retains significant economic upside. I think the $13B investment validates early bets on compute infrastructure over immediate revenue.

The recent convergence of Satya Nadella and Sam Altman marks a pivotal moment in enterprise AI governance. As OpenAI finalizes its “shareholding reform,” the burden of proof for safety and alignment now rests squarely on the newly structured non-profit entity, while Microsoft retains substantial economic leverage. In an extensive dialogue spanning over an hour, the two leaders addressed critical questions regarding capital allocation, partnership dynamics, and the future roadmap of artificial intelligence.

Key areas of scrutiny include how OpenAI plans to deploy its projected $13 billion in 2025 revenue toward a staggering $1.4 trillion computing power budget, what tangible assets Microsoft has secured through this alliance, and whether the anticipated IPO remains on hold. Nadella also highlighted a critical infrastructure constraint: he argues that energy scarcity poses a more immediate threat to AI progress than GPU shortages.

Below is the compiled full text of their discussion. For further details on the conversation, please see—

Microsoft and OpenAI’s Cooperation

Brad Gerstner: Microsoft began investing in 2019 and has invested approximately $13 to $14 billion in OpenAI so far, acquiring about a 27% stake (on a fully diluted basis), down from an initial one-third. With the new round of financing last year, your stake was somewhat diluted. Is this ratio correct?

Satya Nadella: Yes, roughly so. Before discussing our shareholding, I believe the most unique aspect of OpenAI is that one of the world’s largest non-profit organizations emerged from its restructuring process. Inside Microsoft, I often say we are proud to be associated with two of the world’s largest non-profit entities: the Bill & Melinda Gates Foundation and now the OpenAI Foundation. That is the real news. This was not the outcome we anticipated when we initially invested $1 billion; at that time, we did not view it as a potential 100x return investment case, but reality has proven otherwise. Nevertheless, we are very pleased to have been early investors and partners. Frankly, this fully demonstrates Sam’s vision and his team’s execution capabilities. They saw the potential of this technology early on and excelled in turning it into reality.

Sam Altman: I think this is truly an amazing partnership at every stage. As Satya said, when we started, we had no idea where things would lead. But I believe this will prove to be one of the greatest partnerships in tech history. Without Microsoft, and especially without Satya’s firm conviction and decisive action back then, we would not have reached today. At that time, hardly anyone else was willing to bet under such conditions. We knew nothing about the direction of technology then; we simply believed in one principle—continuously advancing deep learning. We believed that if we could achieve this, we would definitely find ways to create excellent products and generate immense value.

At the same time, as Satya mentioned, we also established a structure that we believe will become the world’s largest non-profit organization.

I really like this structure because it allows the value of the non-profit entity to continue growing while enabling its affiliated public-benefit company to obtain the capital needed for continued expansion. Without this structure and like-minded partners, the foundation’s value could not have reached its current scale.

It has been over six years since our initial cooperation. The achievements we have made in these six years are astonishing, and there will be even more in the future.

I sincerely hope Microsoft earns a trillion dollars from this investment, not just one hundred billion.

The Governance Architecture: Who Holds the Levers?

Brad Gerstner: In this restructuring, you mentioned an architecture where the upper layer is a non-profit organization and the lower layer is a Public Benefit Corporation (PBC).

The non-profit portion currently holds $130 billion worth of OpenAI stock, joining the ranks of the world’s largest non-profits upon its inception, with potential for further growth.

This $130 billion asset will be entirely used to ensure that AGI (Artificial General Intelligence) benefits all humanity. You also announced that the first $25 billion would be directed toward healthcare, AI safety, and resilience.

Can you talk about why you chose “healthcare” and “resilience” as these directions? And how do you ensure the foundation does not fall into the pitfalls of bias or inefficiency like many other non-profits?

Sam Altman: First, I believe the best way to create immense value for the world is what we are already doing—building powerful AI tools and making them accessible to everyone. I think corporate mechanisms are excellent. Many companies are bringing advanced AI to more people, creating astonishing results.

However, there are indeed areas where market mechanisms cannot fully drive outcomes aligned with humanity’s long-term interests; in these places, different approaches are needed to push progress forward.

At the same time, AI brings unprecedented new possibilities, such as accelerating scientific discovery at an extremely fast pace and achieving true automated research. Therefore, we decided that our primary investment areas would include healthcare: if AI can help cure numerous diseases and allow related data and knowledge to be widely shared, it will be a tremendous boon for all humanity.

Regarding AI “resilience”—I believe there will certainly be complex situations in future development processes where not all problems can be solved by enterprises alone.

Therefore, we hope to fund relevant work through the foundation, such as cybersecurity defense, AI safety research, and social impact studies, helping society navigate this period of technological change more smoothly.

We are very confident in the long-term positive impacts brought by AI, but we also clearly understand that the path ahead will not be entirely smooth.

My sense is the $130 billion endowment creates a massive governance center of gravity that demands rigorous audit trails. What concerns me is that enterprises must verify if this non-profit structure actually limits liability for downstream AI harms. I think “Resilience” funding is vague; I would look for specific regulatory compliance metrics in their reports.

My Read on the Strategic Pivot

I followed the release closely, and what stood out to me was the explicit admission that market mechanisms fail at long-term human interest alignment. This is a significant pivot from pure commercial logic to a stewardship model. However, as an editor focused on governance, I see a tension here: who holds the non-profit accountable for spending that $25 billion initial tranche? The filing shows a shift toward public goods like cybersecurity and AI safety research, but it does not detail independent oversight boards.

The distinction between “healthcare” benefits and “resilience” funding is critical. Healthcare offers tangible, measurable outcomes (curing diseases), whereas resilience—defined here as navigating complex societal changes—is harder to quantify for compliance purposes. I read this as an attempt to pre-empt regulatory backlash by positioning OpenAI as a public utility rather than just a tech vendor. But the burden of proof remains on the foundation to demonstrate that their “social impact studies” are not merely PR exercises.

Satya Nadella and Sam Altman Jointly Address Key Issues: Collaboration Details and OpenAI’s Future Roadmap Revealed

The governance of the AI era is being defined by contract law as much as code. In this latest exchange, Microsoft has secured a seven-year exclusivity window for GPT series models through 2032, shifting the burden of proof onto OpenAI to verify AGI before that term expires. This agreement dictates not just where these models live, but how revenue is recognized and who holds the keys to the next phase of intelligence.

Microsoft Secures 7-Year Exclusivity for GPT Series

Brad Gerstner: Let’s continue discussing cooperation details—regarding models and exclusivity. Sam, OpenAI’s frontier models are currently distributed via Azure, but for the next seven years until 2032, you cannot distribute these models on other major cloud platforms unless AGI is officially verified beforehand. However, you can still distribute open-source models, Sora, Agents, Codex, wearable device-related technologies, etc., on other platforms. In other words, ChatGPT or GPT-6 will not appear on Amazon’s or Google’s clouds, correct?

Sam Altman: That is not the case. First, we and Microsoft will continue to cooperate in many aspects to create value together. We want to help Microsoft create value, and we also hope Microsoft helps us create value—such cooperation has already unfolded at many levels. We retained a good concept Satya previously proposed—“stateless APIs.” These APIs run on Azure, and this part is not fully exclusive (the agreement is valid until 2030). For other products and models, we will release them on different platforms as well. This naturally aligns with Microsoft’s interests too. So our products will appear in many places—some on Azure, where users can access them, which is good for everyone.

My sense is the “stateless API” loophole suggests Azure dominance may be less absolute than the headline exclusivity implies. Enterprises should verify if their data residency requirements are met by these non-exclusive APIs.

Brad Gerstner: Then there is the revenue-sharing component. OpenAI still needs to pay a share of all revenues to Microsoft; this sharing agreement also lasts until 2032 or until AGI is verified. Assuming—for illustrative purposes—that the share ratio is 15%, if OpenAI’s revenue is $20 billion, it would pay Microsoft $3 billion, which counts as Azure revenue. Satya, is this understanding correct?

Satya Nadella: Yes, we do have a revenue-sharing agreement. As you said, this agreement will remain in effect until AGI emerges or expires. Honestly, i am not sure whether this share ultimately gets counted under Azure or another department—that’s a good question; perhaps Amy, our CFO, should be asked.

What concerns me is that nadella’s hesitation on accounting classification raises red flags for financial transparency and segment reporting accuracy. Investors need clear disclosure on how these inter-company flows impact Azure’s top-line growth metrics.

Brad Gerstner: Since both the exclusivity agreement and revenue sharing will terminate early once AGI is verified, it implies that recognizing AGI is a very significant matter. From what I understand, if OpenAI claims to have achieved AGI, an expert review committee would rule on it; you both would jointly select a “jury” to decide in a relatively short time whether AGI has indeed been realized. Satya, you said during yesterday’s earnings call that currently “no one is close to AGI,” and it won’t happen in the short term. You also mentioned the concept of “spikes and imbalances in intelligence.” But Sam, you seem more optimistic than he is. So the question is: Are you worried that we might really need to convene this “jury” within the next two or three years to judge whether we have reached AGI?

Sam Altman: I know you want to create some dramatic conflict between us. But I believe it is very necessary to establish a formal adjudication process for AGI. Future technological development will certainly see some unexpected twists; we will continue to maintain our good cooperative relationship and jointly understand and judge its direction of development.

I think a joint “jury” for AGI verification creates a unique governance model, but lacks independent regulatory oversight. This self-regulatory approach may face scrutiny from antitrust authorities concerned about market control.

Satya Nadella: Completely agree. This is also one of the reasons why we established this process. I have always firmly believed that intelligent capabilities will continuously improve, and our true goal is—how to put this intelligence into the hands of people and organizations so they can derive maximum benefit. This was also what initially attracted me to cooperate with OpenAI: their mission is to make intelligence benefit all humanity. We will continue down this path.

Sam Altman: Brad, even if we truly achieve “superintelligence” tomorrow, we still hope for Microsoft’s help in delivering products to people.

OpenAI’s $1.4 Trillion Bet: Governance, Accountability, and the Compute Gamble

By Priya Sharma, Enterprise AI & Governance Editor

The dynamic between Microsoft and OpenAI has shifted from a strategic partnership to a high-stakes financial wager that demands rigorous scrutiny. The burden of proof now lies with leadership to demonstrate how a $13 billion revenue base supports a $1.4 trillion compute commitment—a disparity that raises immediate questions about capital allocation, risk management, and shareholder accountability.

OpenAI’s $1.4 Trillion Compute Commitment

Brad Gerstner highlighted the stark contrast in scale: while Satya Nadella previously identified OpenAI as the “new Google” born from Microsoft’s early bet, external reports peg 2025 revenue at approximately $13 billion. Meanwhile, Sam Altman outlined a staggering infrastructure plan during his recent livestream: a commitment to invest $1.4 trillion in computing power over the next four to five years. This includes $500 billion for Nvidia, $300 billion each for AMD and Oracle, and $250 billion for Azure.

The market’s primary concern remains logical: How can a company with $13 billion in revenue sign a $1.4 trillion expenditure commitment?

Altman dismissed the skepticism not with financial audits, but with market mechanics. He asserted that OpenAI’s actual revenue is far more than $13 billion and suggested that critics are merely posturing because they cannot access the stock. “If you really want to sell your OpenAI shares, I can help find buyers,” Altman said, noting that those complaining on X (Twitter) would likely rush in if they could buy shares. He argued that the market’s anxiety is performative rather than substantive.

Altman framed the spending as a prudent forward-looking bet. He outlined three pillars of future value: rapid growth in ChatGPT revenue, expansion into significant AI cloud services, and a consumer device business. Additionally, he pointed to technology enabling AI-driven scientific research as a source of immense value. “We clearly know the direction of technological capability evolution… Of course, we might also mess up—that’s a risk we voluntarily assume,” Altman stated. He emphasized that without securing this compute power, OpenAI cannot produce its models or achieve the necessary revenue scale.

My sense is a $1.4 trillion capex plan against current revenues requires transparent milestone-based funding triggers to protect stakeholders. What concerns me is that altman’s dismissal of critics as “shouting on X” avoids addressing the fundamental solvency questions enterprises should ask. I think enterprises must verify if these compute commitments are backed by irrevocable contracts or speculative future cash flows.

OpenAI’s Execution Capability

Satya Nadella offered a robust defense of OpenAI’s operational discipline, stating he has never seen a business plan they failed to exceed in execution. “Their growth rate and business execution are frankly unbelievable,” Nadella said, noting that while public discourse focuses on usage metrics, their business performance is equally shocking.

Brad Gerstner pressed further, referencing comments from Greg Brockman about compute constraints. Brockman had suggested that a tenfold increase in compute would not yield linear revenue growth but would certainly drive significant expansion. Gerstner asked Altman to quantify the current constraint: Do they feel severely limited? Is there an endpoint where infrastructure completion in two to three years removes these bottlenecks?

The conversation underscores a critical governance gap. While leadership expresses confidence in execution and future value, the sheer magnitude of the financial commitment outpaces traditional enterprise risk frameworks. The reliance on “forward-looking bets” without clear interim financial safeguards places the burden of due diligence squarely on investors and partners.

My sense is nadella’s endorsement validates execution but does not mitigate the financial leverage risks inherent in the compute spend. What concerns me is that enterprises should demand clearer definitions of what “prudent planning” looks like when facing trillion-dollar commitments.

Satya Nadella and Sam Altman Jointly Address Key Issues: Collaboration Details and OpenAI’s Future Roadmap Revealed

The Economics of Compute Capacity

The burden of proof in AI infrastructure planning has shifted from simple capacity forecasting to a complex calculus of energy, cost, and capability. As enterprises align with major cloud providers, the question is no longer just “how many GPUs do we need?” but rather how the unit economics of intelligence will evolve under Jevons Paradox.

Sam Altman: We often discuss this issue—whether there is “enough” compute power. I believe the best way to understand it is to view it as “energy.” You can discuss energy demand at a certain price level, but you cannot talk about energy demand in isolation from price. If the cost of compute per unit of intelligence drops by 100 times tomorrow, usage would grow far more than 100-fold. There are currently many people who want to use compute for various tasks, but at current costs, it is not economically viable.

If compute becomes cheaper, entirely new demand will emerge. On the other hand, as models become smarter—if these models can cure cancer, discover new laws of physics, or drive vast numbers of humanoid robots to build space stations, no matter how crazy that sounds—people will also be willing to pay a higher price for “each unit of intelligence.” Therefore, when discussing compute capacity, one must consider the relationship between “unit cost” and “unit capability.” Discussing this without combining these two curves is essentially an ill-defined problem.

Satya Nadella: If the value of intelligence is logarithmically related to compute power, then we must continuously improve efficiency. This means maximizing the number of tokens generated per dollar and per watt, along with the resulting socioeconomic value, while reducing costs. From an economic perspective, this describes exactly what Jevons Paradox outlines: you continuously lower costs and commodify intelligence itself, turning it into a true driver of global GDP growth.

Sam Altman: However, I believe that currently, the situation is closer to “intelligence being a logarithmic function of compute,” rather than the other way around. But perhaps in the future we will find better Scaling Laws; this area is still under exploration.

Brad Gerstner: Yesterday, we heard Microsoft and Google both state that their cloud business growth could have been faster if not for GPU supply constraints.

I also asked Jensen Huang on this show whether compute oversupply might occur in the next five years. He replied that it is almost impossible in the next two to three years. I think you two would agree with this assessment—although we cannot predict what will happen in five to seven years, at least for the next two to three years, compute oversupply is highly unlikely.

I think enterprises must treat compute as a volatile energy commodity, not a static utility. My sense is budgeting models should account for Jevons Paradox when forecasting AI adoption rates. What concerns me is that verify supplier supply chain resilience against the two-to-three-year shortage window.

The Bottleneck Is Infrastructure, Not Chips

I think enterprises must audit their physical infrastructure readiness before committing to long-term AI contracts. My sense is regulatory bodies should scrutinize energy grid capacity as a critical component of compute compliance. What concerns me is that investors need to distinguish between hardware availability and actual deployable power in their risk models.

The conversation shifted away from the typical narrative of chip scarcity, focusing instead on the physical realities of deployment. Satya Nadella argued that the primary constraint is not the supply of silicon, but the speed at which power grids and data centers can be constructed. He noted that without rapid infrastructure development near energy sources, even abundant chips remain idle. This creates a complex, unpredictable supply chain where demand spikes outpace physical build-out capabilities.

“The true long-term trend is continuous growth… the biggest problem we face right now is not ‘compute oversupply,’ but rather the speed of power and infrastructure construction.”

Nadella emphasized that this is a global challenge, not limited to specific markets. He warned that predicting real demand is difficult because it changes drastically, making linear planning ineffective. The burden of proof lies with enterprises to verify their local infrastructure constraints before scaling.

Sam Altman countered with a different timeline, suggesting that compute oversupply is inevitable, though the timing remains uncertain. He highlighted the psychological and economic cycles inherent in this sector, noting that sudden shifts in energy costs or intelligence efficiency could severely impact long-term contracts.

“There will come a day when compute definitely becomes oversupplied—whether that’s in two or three years, or five or six, I can’t say for sure—but it will happen, and likely multiple times.”

Altman pointed to exponential trends, such as costs dropping by an average of 40 times annually, which he described as “terrifying” from an infrastructure perspective. He warned that if personal AI models become viable on local devices, the current centralized model could face disruption, potentially hurting those who invested heavily in large-scale infrastructure.

Brad Gerstner drew parallels to the dot-com bubble, suggesting that while cycles are volatile, the underlying value of technology often exceeds initial predictions. Nadella added that software optimizations, particularly for inference stacks on GPUs, offer exponential efficiency gains that hardware improvements alone cannot match. This distinction is crucial for governance teams evaluating total cost of ownership and performance metrics.

The Shift to Distributed Compute

I think governance frameworks must address data sovereignty when compute moves from central clouds to edge devices. My sense is enterprises should verify vendor claims about local model capabilities against actual power consumption limits.

The discussion turned toward consumer hardware, with Altman envisioning a future where devices can run models comparable to GPT-5 or GPT-6 locally with low power usage. Gerstner noted that this shift would likely unsettle providers of large centralized compute centers.

“compute needs to be distributed both at the edge and for globally distributed inference.”

Nadella clarified that his focus is on creating interchangeable compute resources across cloud infrastructure. He identified two critical requirements: efficient “token factories” and high utilization rates. Achieving this requires scheduling diverse AI workloads, including pre-training, intermediate training, post-training, and reinforcement learning. The goal for all cloud providers is to make these resources fungible, ensuring flexibility in handling varied computational demands.

OpenAI’s IPO Trajectory: Governance and Valuation Realities

The burden of proof regarding OpenAI’s transition from a private entity to a public one rests squarely on its board and executive leadership. As Satya Nadella and Sam Altman navigate this potential shift, the question is not just about market timing, but about who bears the responsibility for ensuring that public markets can accurately price the company’s complex governance structure and long-term roadmap.

Clarifying the Timeline and Strategic Intent

When pressed by Brad Gerstner on whether OpenAI intends to go public in late 2026 or 2027, Sam Altman was unequivocal. He stated that there are no specific plans or timelines currently in place. While media narratives often speculate on imminent listings, Altman clarified that the company has not set a date nor made an IPO decision. His stance is one of long-term possibility rather than immediate action; he believes going public might be a natural step eventually, but it remains speculative at this stage.

What concerns me is that the lack of a timeline suggests governance uncertainty is still being resolved internally. I think enterprises should not price their strategies around an assumed 2026–2027 listing window.

Valuation Mechanics and Capital Requirements

Gerstner challenged the narrative by pointing to financial projections, noting that if OpenAI’s revenue exceeds $100 billion by 2028 or 2029, it would likely meet standard conditions for an IPO. When Gerstner suggested 2027 as a more viable target, he outlined a scenario based on a rumored $1 trillion valuation.

The math presented was straightforward: assuming $100 billion in revenue listed at a 10x multiple, the valuation aligns with historical precedents like Facebook’s listing and other large consumer companies. Gerstner argued that offering only 10% to 20% of shares could raise between $100 billion and $200 billion. This capital injection would be sufficient to support OpenAI’s expansion and R&D plans without diluting control excessively.

My sense is a $1 trillion valuation requires rigorous third-party audit readiness before any public filing. What concerns me is that the 10x revenue multiple assumes sustained growth, which is a high-risk assumption for investors.

Leadership Stance on Public Markets

Altman’s response to the financial modeling was pragmatic. He acknowledged that while he prefers the company to pursue an IPO based on strong, verified revenue growth, going public is certainly a direction worth considering. This marks a subtle but significant shift from previous ambiguity; it signals openness to capital markets if the fundamentals support it.

Gerstner added a personal dimension to the discussion, noting that his own children use ChatGPT daily and that he hopes ordinary investors get the opportunity to buy shares in such an influential company. Altman agreed, admitting that allowing everyday people to invest in a technology they interact with might be the most attractive reason for him personally regarding an IPO.

I think retail investor access raises significant consumer protection and disclosure compliance issues. My sense is boards must distinguish between personal sentiment and fiduciary duty when timing public offerings.

Satya Nadella and Sam Altman Jointly Address Key Issues: Collaboration Details and OpenAI’s Future Roadmap Revealed

Regarding Breakthroughs in 2026

Brad Gerstner: Recently, your team has been talking about future new directions: larger-scale compute, ChatGPT-6 and versions further out, robotics, physical devices, scientific research.

Sam Altman: This year, I find the development of Codex (the AI coding model) most interesting. Next year, it might leap from handling “hours-long tasks” to being able to handle “days-long tasks,” allowing humans to create software at unprecedented speeds and in entirely new ways. I am very excited about this. And I believe this trend will also expand into other industries. I am more familiar with code, so changes there are easier for me to see, but this will truly reshape the boundaries of human creativity.

I hope that by 2026, AI can bring even a tiny scientific discovery. If we can start with small breakthroughs, we can gradually accumulate toward larger achievements in the future. It sounds crazy, but if AI can make an original scientific discovery in 2026, no matter how minor, it will be a major moment for human civilization. I am very much looking forward to this. Of course, robotics and entirely new forms of computing devices are also important. But my personal preference is: letting AI truly participate in scientific research. That means allowing intelligent systems to begin expanding the total amount of human knowledge—this matter is too important.

Satya Nadella: Yes, taking Codex as an example, the key lies in combining model capabilities with interaction interfaces. ChatGPT’s “magical” explosion occurred because a suitable UI met a sufficiently powerful intelligent model. The current “Coding Agent” is forming a new paradigm: AI can autonomously execute long-duration tasks, and then humans provide “fine-tuning” at critical nodes. We internally call this macro-delegation and micro-steering. When this new type of intelligence combines with a brand-new UI, it creates an entirely new form of human-computer interaction, which I believe may have an impact even greater than ChatGPT.

Sam Altman: This is also why I am excited about the new computing device forms we are developing. Because current computer architectures are simply not suited for this workflow. A UI like ChatGPT is actually imperfect. Imagine: you possess a device that stays by your side, it can complete tasks independently, receiving your “micro-guidance” when necessary, while deeply understanding your context and flow of life. That would be very cool.

Brad Gerstner: And neither of you has mentioned consumer-side use cases yet. I often think about how we search through hundreds of apps in our devices every day and fill out various forms—these interaction methods have hardly changed in 20 years. But if AI allows us to truly have an almost free personal assistant that improves life for billions globally, whether helping order diapers for a child, booking hotels, or modifying schedules, it would be the most mundane yet revolutionary change. When we move from “answering” to “remembering” and “acting,” interacting naturally with AI through earbuds or other devices rather than staring at a glass screen—that is truly a stunning future.

Satya Nadella: I think this is exactly what Sam was just hinting at.

(Altman logs off)

My read:

The shift to macro-delegation places liability on the human for final verification, not the AI. Enterprises must audit these “days-long” autonomous workflows for compliance drift. We need clear governance boundaries before deploying such agents in regulated sectors.

Regarding Breakthroughs in 2026

Brad Gerstner: In 2019, you brought the idea of “investing $1 billion in OpenAI” to the board. Was it an instant agreement? Did you need to spend some effort convincing everyone?

Satya Nadella: Yes, looking back now, that journey was interesting. Actually, our relationship with OpenAI started much earlier—around 2016, Azure was one of OpenAI’s earliest sponsors.

At that time, they were primarily focused on reinforcement learning. I still remember that Dota 2 match running on Azure. Later, they shifted to other directions. At the time, I was quite interested in reinforcement learning, but honestly, this also validates your concept of a “prepared mind.” Since 1995, Microsoft has been obsessed with “natural language”—this was a core direction pushed internally by Bill Gates. After all, we are a company centered around coding and information work.

So, when Sam started talking about “text,” “natural language,” “Transformers,” and “Scaling Laws” in 2019, I thought: “Wow, this is really interesting.” The team’s direction aligned highly with our interests, so from that perspective, it was a “no-brainer” investment.

Of course, when you go to the boardroom and say, “I plan to put $1

Satya Nadella and Sam Altman Jointly Address Key Issues: Collaboration Details and OpenAI’s Future Roadmap Revealed

The financial architecture surrounding OpenAI remains a point of contention, with critics noting that billions have been poured into an entity we don’t fully understand—neither a profitable company nor a traditional non-profit. This ambiguity guarantees ongoing debates regarding accountability and governance structures.

Bill Gates was initially skeptical of this model, a stance I view as reasonable given the lack of precedent. However, after witnessing the GPT-4 demonstration, his perspective shifted entirely. He later publicly described it as the most impressive Demo he had seen since Charles Simonyi showed him demos at Xerox PARC.

For me, the thought at the time was: “Let’s give it a try.” Later, when we saw early results of Codex in GitHub Copilot—

Code completion worked seamlessly—that was the moment I knew we could scale this from “1” to “10.” To be honest, that first step was controversial, but it was truly what launched the entire AI era.

Afterward, whether it was OpenAI’s team execution or our own productization efforts on our side, both were astonishing.

If you look at the current portfolio—GitHub Copilot, ChatGPT, Microsoft 365 Copilot, and our consumer-facing Copilot—together these four constitute the largest AI product ecosystem in the world today. This is precisely what enables us to keep moving forward.

What concerns me is that the “largest ecosystem” claim consolidates market power but raises significant antitrust scrutiny risks for enterprises relying on a single vendor stack.

Brad Gerstner: I think many people don’t realize that your CTO, Kevin Scott—a former Google engineer—is actually based in Silicon Valley.

Keep in mind that at the time, Microsoft had missed search and the mobile era. When you became CEO, cloud computing was nearly next on the list of things we were about to miss. You described it as “catching the last train out of the station.” So I imagine you were determined to keep your “eyes and ears” in Silicon Valley so you wouldn’t miss the next wave.

Kevin must have helped you significantly with this, right?

Satya Nadella: Absolutely correct. In fact, I would say Kevin’s conviction was decisive. He started as a skeptic—which is exactly the kind of person I pay attention to: “those who didn’t believe but then changed their minds and became excited.” That shift itself is a signal because it makes you ask, “Why? What changed your mind?” Kevin initially held back but eventually became a staunch supporter. Many of us were actually taught to believe that “there must be some algorithm that solves everything,” rather than “breakthroughs come from scaling and computing power.” But it turned out that Kevin’s firm belief—that “this is worth doing”—was one of the key driving forces behind all of this.

I think relying on internal conviction to drive capital allocation bypasses traditional risk assessments, leaving boards with less visibility into technical feasibility.

My sense is the narrative of “scaling and computing power” as the primary driver oversimplifies the complex governance challenges inherent in proprietary AI development.

Satya Nadella and Sam Altman Jointly Address Key Issues: Collaboration Details and OpenAI’s Future Roadmap Revealed

On the Value of Collaboration

Brad Gerstner: Today, that initial $1 billion investment is valued at approximately $130 billion, and as Sam mentioned, it could potentially reach $1 trillion in the future. However, this still underestimates the true value of Microsoft’s partnership with OpenAI. Beyond equity gains, Microsoft earns billions in profit annually from revenue sharing with OpenAI and benefits from Azure’s $250 billion commitment to compute capacity.

Furthermore, your exclusive distribution of APIs has generated massive sales—drawing many customers who were previously on AWS over to Azure. Can you talk about how you view these value dimensions? Particularly the strategic significance of exclusivity for Microsoft?

Satya Nadella: Of course. Setting aside the equity portion, the most critical strategic synergy is that OpenAI’s stateless API runs exclusively on Azure. This is a win-win for OpenAI, for us, and for customers. Enterprise clients building AI applications want APIs to be stateless, which they then combine with underlying compute, storage, and databases to form complete workloads. This is exactly where Azure integrates with OpenAI.

We are now even integrating Foundry (our AI application hosting platform). Suppose you are building an AI application; the key question becomes: “How do you ensure that AI evolution aligns with application logic?” This requires a full application server layer, which is precisely what we provide in Foundry.

On the other hand, another source of value for Microsoft is that we not only have exclusive access but also rights to use intellectual property (IP). Our agreement with OpenAI allows Microsoft to use frontier models royalty-free for the next seven years. In other words, if you are a Microsoft shareholder, it means we essentially get a state-of-the-art large model “for free.”

We can embed this model into products like GitHub, Microsoft 365, and Copilot, then fine-tune it using our own data to merge proprietary knowledge at the weight level. Therefore, we are very confident in the value creation AI brings—whether at the infrastructure (Azure) level or in high-value areas such as healthcare, knowledge work, programming, and security.

Brad Gerstner: Microsoft recently consolidated OpenAI’s losses in its financial reports; reportedly, it consolidated about $4 billion in losses last quarter. Do you think investors misunderstood this? It’s possible they were penalized in their valuations because these losses affect earnings per share multiples. In reality, the long-term benefits and potential market cap growth from the OpenAI partnership far exceed these short-term figures. What is your take on this?

Satya Nadella: That’s a good question. Our CFO Amy Hood adopts a “fully transparent” approach to handling this. Honestly, i am not an accounting expert, so I believe the best course of action is to disclose all information openly. This is why we now distinguish between GAAP and Non-GAAP financial data. At least in this way, investors can clearly see the actual earnings per share (EPS) and understand the full picture.

Because in my view, the matter is actually quite simple. Suppose you invest $13.5 billion; naturally, you might lose that $13.5 billion, right? But at least to my knowledge, you won’t lose more than $13.5 billion—that is your maximum risk exposure.

Of course, one could argue that our equity value is now around $135 billion. While this asset is liquid, we do not intend to sell it, so it carries a certain degree of risk as well.

However, I think what you are really asking about is something else—what is happening outside of these investments. For example, Azure’s growth. Would Azure have grown like this without the partnership with OpenAI? As you mentioned, how many customers migrated to Azure from other cloud platforms for the first time because of this?

This is where we truly benefit. And it’s not just reflected in Azure; it’s also evident in Microsoft 365. In fact, we used to wonder: after E5, what would be the next major growth driver for Microsoft 365? We have now found it: Copilot.

Its scale has surpassed any office suite we have ever launched. Whether in terms of penetration rate, adoption speed, or growth momentum, Copilot exceeds all of Microsoft’s achievements in digital office productivity over the past few decades.

So, we are currently very confident in the opportunity to create long-term value for shareholders. At the s

What concerns me is that the seven-year royalty-free IP license is a massive competitive moat that enterprises must scrutinize for vendor lock-in risks. I think consolidating losses shifts financial accountability directly onto Microsoft’s balance sheet, raising questions about capital allocation discipline. My sense is exclusive API distribution creates a dependency chain that competitors cannot easily replicate without significant infrastructure investment.

Satya Nadella and Sam Altman Jointly Address Key Issues: Collaboration Details and OpenAI’s Future Roadmap Revealed

At a time when transparency is often treated as an afterthought, Microsoft has doubled down on public disclosure. We remain fully transparent so that outsiders can clearly see—whether it’s losses or investment situations. We follow accounting rules as prescribed and disclose all data publicly so everyone understands the actual situation. This commitment to visibility places the burden of proof squarely on management to justify capital allocation in an era of intense scrutiny over AI infrastructure spending.

Brad Gerstner: About a year ago, many headlines claimed that Microsoft was cutting back on AI infrastructure investments. Do you think this is a fair assessment, or perhaps a misunderstanding? These reports certainly existed at the time. Perhaps you were indeed more conservative and cautious then. However, during last night’s earnings call, Amy mentioned that Microsoft has actually been short on compute capacity and infrastructure for several quarters. She originally thought you would catch up, but you didn’t—because demand continued to grow. So my question is: Looking back now, was it too conservative? Now that you know this, how do you plan your roadmap going forward?

Satya Nadella: That’s a very good question. In fact, we realized something at the time—and I am glad we did—that we must build compute clusters capable of flexible scheduling (fungibility) throughout the entire AI lifecycle. This flexibility needs to apply not only across different regions but also across different chip generations.

Take Jensen Huang and the NVIDIA team as an example; their update speed can be described as “moving at light speed.” We are now introducing GB300 chips. You certainly don’t want to deploy a large batch of GB200s only to find that GB300s have already gone into full mass production.

Therefore, you must continuously modernize your clusters, distribute them globally, and ensure they can flexibly schedule resources for different workloads. At the same time, we are constantly optimizing at the software level.

That was the decision we made back then. Sometimes we had to say “no” to certain demands, including some from OpenAI. For instance, Sam might say, “Please help me build a dedicated training data center with thousands of megawatts in this location.” While that might make sense for OpenAI, it doesn’t align with Microsoft’s long-term global infrastructure layout.

So we chose to give them the flexibility to purchase compute resources from other vendors. Meanwhile, we maintained significant cooperation with OpenAI—more importantly, this allowed us to preserve flexibility and balance with other customers (including Microsoft’s own first-party businesses).

You need to understand that we do not want to face a shortage of compute capacity. Many investors focus too heavily on Azure’s growth numbers. But for me, the high-margin business is actually the Copilot series, including Security Copilot, GitHub Copilot, Healthcare Copilot, and so on.

We hope to achieve long-term returns in a balanced manner, rather than being driven by short-term Azure growth rates. I think this point has been misunderstood among investors—it’s quite interesting. After all, they hold Microsoft stock because of its broad business portfolio, not solely due to Azure’s growth curve.

What concerns me is that prioritizing first-party Copilot products mitigates single-customer dependency risk for enterprise clients. I think the shift from pure infrastructure play to high-margin software services changes the valuation narrative. My sense is enterprises must verify if OpenAI’s access to Azure is now secondary to Microsoft’s internal needs.

Balancing Supply Constraints and Shareholder Interests

Brad Gerstner: Speaking of Azure, it grew 39% this quarter, with annualized revenue reaching $93 billion—quite impressive. In comparison, Google Cloud grew by 32%, and Amazon by only about 20%. However, based on what you just said, because you allocate compute capacity to first-party (1P) projects and research initiatives, Azure could potentially have grown by 41% or 42% if you had more capacity available at the time, right?

Satya Nadella: That is exactly where we balance internally—finding equilibrium between long-term shareholder interests, customer service quality, and risk diversification (avoiding concentration of compute power with just OpenAI).

After all, our current situation is not demand-constrained but supply-constrained. Therefore, we must strategically “shape” demand to ensure it optimally matches our compute capacity supply in the long run.

What concerns me is that supply constraints are no longer a temporary glitch; they are a strategic lever for resource allocation. I think governance teams should monitor how “shaping demand” impacts SLA compliance for external partners.

The $400 Billion Backlog: Diversification and Conversion Risk

Brad Gerstner: You mentioned $400 billion in remaining performance obligations (RPO), a staggering figure. Last night you noted that this represents your current booked business volume. As sales continue, this number will undoubtedly increase tomorrow. You also mentioned that to fulfill these backlog orders, you must expand capacity on a massive scale. I want to ask: How diversified are these backlog orders? How confident are you that this $400 billion will truly convert into revenue in the coming years?

Satya Nadella: Yes, regarding the $400 billion in remaining

Satya Nadella and Sam Altman Jointly Address Key Issues: Collaboration Details and OpenAI’s Future Roadmap Revealed

The burden of proof now shifts to investors and auditors to verify the substance behind these massive capital commitments. As Microsoft and OpenAI navigate a complex web of equity stakes, deferred revenue, and infrastructure scaling, the distinction between genuine consumption growth and financial engineering becomes critical for enterprise governance. We must ask who bears the risk if those “large-scale workloads” fail to materialize as projected.

The Reality of Backlog and Diversification

The performance obligations tied to these agreements have an average duration of approximately two years—a relatively short horizon that underscores the urgency of clearing backlog orders. This timeline is a primary driver for our investment in large-scale capacity; we are certain that fulfilling these commitments requires immediate, aggressive infrastructure deployment.

Regarding diversification, this demand is split between Microsoft’s direct operations (1P) and third-party customers. Our internal demand remains extremely high, but we also observe more external companies building truly scalable workloads. This dual engine provides a layer of security. The strong planability inherent in these arrangements allows us to feel confident about future capacity construction, even as we account for the $250 billion in long-term orders that will grow gradually according to plan.

My sense is short obligation durations increase execution risk; enterprises should monitor backlog clearance rates closely. What concerns me is that diversification between internal and third-party demand reduces reliance on a single customer segment.

Maintaining Margin Amidst Commoditization

In the compute infrastructure race, new entrants like Oracle, CoreWeave, and Crusoe are proliferating. Typically, such competition compresses profit margins. Yet, Microsoft has expanded Azure capacity rapidly while maintaining healthy operating profits. The question is how we sustain this advantage when competitors might leverage their positions to suppress profitability or trigger bull-bear cycles.

The reality is that compute resources and storage are commoditized. Scale is the only defense against margin erosion. As I have noted, unless you achieve scale, you cannot be profitable; competition inevitably drives everything toward commoditization. Therefore, we must maintain an efficient cost structure through continuous supply chain efficiency and software optimization.

Our partnership with OpenAI provides a distinct advantage: it delivers large-scale workloads. Hosting the largest-scale workloads on the cloud allows us to learn faster how to operate massive systems and lowers our cost structure, making our pricing more competitive. This scale is key to maintaining profit margins.

I think scale is no longer just an advantage; it is a prerequisite for survival in commoditized infrastructure markets. My sense is software optimization must keep pace with hardware scaling to prevent margin compression.

Holistic Capital Allocation and Blended Returns

Microsoft’s capital allocation strategy cannot be viewed through the lens of any single segment. When I disclosed Azure figures, it was because we do not treat capital expenditures in isolation. Spend on Xbox Cloud Gaming, Microsoft 365, or Azure is considered holistically. From a Microsoft-wide perspective, our focus is on whether our blended average return matches the operating profit margin required by the company.

We are not a conglomerate with a single platform; rather, we amplify the overall returns of cloud and AI investments through the synergy of five or six different businesses. This diversified portfolio provides a buffer that allows us to maintain profitability even in highly competitive environments. The synergy across these segments ensures that capital deployed in one area supports the efficiency and growth of others.

What concerns me is that investors should evaluate Microsoft’s blended returns rather than isolating Azure performance metrics. I think cross-segment synergy reduces the risk associated with heavy AI infrastructure investments.

Equity Stakes vs. Revenue Recognition

There is significant market discussion around transactions similar to AMD’s exchange of 10% equity for deals, or Nvidia’s strategic partnerships. These cross-deals raise questions about the sustainability and stability of AI revenue. However, it is crucial to distinguish between investment activities and operational revenue.

Our $13.5 billion investment in OpenAI is entirely for training costs and does not count as revenue. This is why we hold an equity stake (27% or valued at $13.5 billion). These funds do not flow into Azure revenue. In fact, Azure revenue consists purely of consumption-based charges from ChatGPT and other API usage, which we monitor closely.

Vendor financing has always existed; it is not a new concept. When one company is building infrastructure while its customers are also expanding but need financing, unconventional forms may be adopted. These obviously require careful scrutiny from the investment community. Interestingly, Microsoft had no necessity to engage in such practices for OpenAI. Our approach was straightforward: invest in OpenAI and acquire equity, or support their launch through direct operational partnerships rather than complex financial engineering.

My sense is equity investments should never be conflated with recurring cloud revenue streams. What concerns me is that transparency around vendor financing is essential to assess the true sustainability of AI-driven growth.

Satya Nadella and Sam Altman Jointly Address Key Issues: Collaboration Details and OpenAI’s Future Roadmap Revealed

The conversation shifted from infrastructure economics to the structural integrity of enterprise software. Brad Gerstner highlighted that recurring revenue hinges on demand for final outputs, noting that while Microsoft offers discounted compute pricing, other providers may adopt different models. The underlying assumption remains simple: as long as there is demand for the output, the model holds.

I think compute discounts are a wedge; true stickiness comes from workflow integration. My sense is “Recurring revenue” is only recurring if the agent’s output stays reliable and compliant. What concerns me is that enterprises must audit which logic is being offloaded to opaque agent systems.

The End of the “Thin Layer”?

Gerstner pressed Nadella on his previous assertion that most application software is merely a “thin layer” over messy databases—a statement that had previously sparked significant debate in the industry. This framing suggests a fundamental shift in how we view legacy enterprise stacks.

Nadella stood by his characterization, arguing that intelligent agents are poised to replace traditional business applications. His rationale is structural: these applications are essentially “grouped databases” with embedded business logic. In an agent-centric future, that static logic will be superseded by dynamic, autonomous decision-making processes.

I think if agents replace the logic layer, accountability for errors moves from code to model behavior. My sense is governance frameworks must evolve to monitor agent decisions, not just application inputs.

Satya Nadella and Sam Altman Jointly Address Key Issues: Collaboration Details and OpenAI’s Future Roadmap Revealed

On the Success of Microsoft 365 Copilot

Brad Gerstner: Today, the forward price-to-sales ratio for listed software companies is approximately 5.2x, below its historical average of 7x, even though the market is at historic highs. Many are concerned that SaaS subscriptions and profit margins may be impacted by AI.

So, what impact does AI currently have on the growth rates of your core software products (such as databases, Fabric, security, Office 365)? How do you ensure that software is not disrupted but rather enhanced through AI?

Satya Nadella: Yes, as I mentioned last time, SaaS application architectures are changing because the intelligent agent layer is replacing the old business logic layer. In the past, our SaaS applications had tightly coupled data layers, logic layers, and UIs. AI does not adhere to this coupling; it requires decoupling, and context engineering becomes crucial.

Take Office 365 as an example. I appreciate its low ARPU (Average Revenue Per User) combined with high usage rates. Outlook, Teams, SharePoint, Word, and Excel are used almost constantly by users, generating massive amounts of data input into Graph. The combination of low ARPU and high usage gives me confidence that we can fully leverage this data through the AI layer.

Interestingly, GitHub and Microsoft 365 have seen record-high data inputs due to AI. Generated code, PowerPoint presentations, Excel models, chat logs, and new documents are all entering Graph, forming vector embeddings that provide a semantic foundation for intelligent agents.

Next-generation SaaS applications must be intelligent. High ARPU with low usage might pose problems, but we operate on low ARPU with high usage. By accelerating deployment through AI, products like M365 Copilot command higher prices but achieve faster deployment and greater utilization.

The situation at GitHub is also clear: the results accumulated over the past 10–15 years saw major growth last year. Code is no longer just a tool; it has become a means of substituting labor, representing a fundamentally different business model.

Brad Gerstner: In the past, cloud primarily ran pre-compiled software that didn’t require many GPUs, with most value concentrated in databases and application layers.

But in the future, interfaces will only have value if they are intelligent. Software must be able to think, act, and provide recommendations, which requires generating large volumes of tokens and processing constantly changing contexts. In this scenario, AI factories (hardware and models) might capture more value than software or agents. What is your view?

Satya Nadella: Two things determine the value of AI:

• Token Factory: Hardware and system software are optimized to run at maximum utilization. The role of hyperscalers is to operate this token factory efficiently while managing heterogeneous hardware.
• Agent Factory: Modern SaaS drives business outcomes. It knows how to use tokens most effectively to create value. GitHub Copilot is an example: in auto-mode, it selects the best model based on prompts to complete tasks. Intelligent SaaS applications optimize token usage through feedback loops and data cycles to achieve optimal business results.

Overall, software has real marginal costs—a reality that existed in the cloud era but is now more pronounced. Business models need to adjust, optimizing Agent Factories and Token Factories separately.

What concerns me is that enterprises must verify their own data governance before feeding sensitive inputs into these decoupled agent layers. I think the shift from logic layers to context engineering changes who bears the liability for AI errors. My sense is investors should watch how Microsoft separates token costs from agent value in future earnings calls.

On Search Profitability and Future Models

Brad Gerstner: Microsoft has an obscure search business that is very profitable because of the huge volume of searches, with each search costing only a fraction of a cent. In contrast, chat interactions are more expensive. Do you think chat can eventually reach the profitability levels of search in the future?

Satya Nadella: The profitability model for search is magical: indexing is a fixed cost that can be amortized efficiently. Chat, however, requires more GPU cycles per interaction, resulting in a different cost structure. Therefore, early chat models adopted freemium or subscription models. We are still exploring advertising or agent-based business models.

Meanwhile, I personally still use search for specific navigation tasks, while commercial search is gradually shifting toward the Copilot model. In the future, there will be a redistribution process similar to the restructuring seen in SaaS during its early days.

Brad Gerstner: This is a multi-trillion-dollar market. When the search business model shifts to

What concerns me is that consumer agent monetization remains an unresolved puzzle for platforms. Enterprise models offer a clearer path to profitability through workflow integration. We must verify if seat-based replacement truly scales without churn risks. Governance frameworks need to address who owns the agent’s decision liability.

The Economics of Agency: Consumer vs. Enterprise Realities

The conversation shifted from technical capabilities to the fundamental economics of artificial intelligence, specifically regarding how value is captured and sustained. Satya Nadella emphasized that consumer attention is a zero-sum game; time spent with one assistant precludes engagement with another. This scarcity creates significant headwinds for profitability in the consumer sector, where the business model lacks clarity.

In contrast, Nadella argued that the enterprise landscape operates differently. It is not a winner-take-all market but rather one defined by diverse workflows and integration needs. Here, AI agents do not merely compete for attention; they replace traditional seat-based billing models with outcome-based value. This shift makes profitability more tangible in B2B contexts compared to the ambiguous consumer market.

The implication is that while indexing costs were once fixed, the move toward personal assistants changes the cost structure entirely. The potential value of these agents may surpass traditional search, but only if enterprises can justify the transition from per-seat licensing to agent-driven productivity gains.

Satya Nadella and Sam Altman Jointly Address Key Issues: Collaboration Details and OpenAI’s Future Roadmap Revealed

On AI and Productivity

The burden of proof for AI’s economic impact has shifted from theoretical promise to operational reality. As enterprises grapple with headcount stability versus revenue growth, the accountability lies with leadership to demonstrate how intelligent agents translate into tangible leverage rather than mere cost-cutting.

Brad Gerstner: Recently, we saw Amazon lay off staff on a large scale, while the “Magnificent Seven” have seen limited growth over the past three years.

Microsoft’s headcount remained almost unchanged at around 225,000 in 2024–2025. Many believe this is post-pandemic efficiency optimization. But does AI also play a role? Will AI be a net job creator? In the long term, will it improve Microsoft’s productivity?

Satya Nadella: I firmly believe that the productivity curve will rise due to AI tools. Task-level work will be completed more efficiently with AI. Internally at Microsoft, we are ensuring every employee is equipped with M365 and GitHub Copilot to boost efficiency.

At the same time, we are learning a new way of working: collaborating with intelligent agents, much like how early Office tools changed workflows.

Planning and execution now start with AI: research, thinking, sharing, and generating new work products and workflows. Organizations that master this capability will achieve the greatest productivity gains, whether within Microsoft or across industries and the real world.

I think enterprises must verify if their current workforce is actually trained on these agents or just given access to them.

Brad Gerstner: So will Microsoft benefit from this? Let’s assume that at current growth rates—in five years—your revenue would be roughly double today’s. Satya, if revenue grows at this pace, how many additional employees would you hire?

Satya Nadella: The best part is the examples I see daily from our employees. For instance, our Head of Network Operations oversees the fiber optic network for our 2-gigawatt data center just built in Fairwater. The deployment of AI has made tasks such as fiber laying and operations extremely demanding. In fact, we need to coordinate with about 400 fiber operators globally; whenever issues arise, complex DevOps processes must be handled.

She said that even with budget approval, there were not enough people to hire to complete these tasks. So she made the second-best choice: she built her own intelligent agents to automate the DevOps process. This is an example of a team leveraging AI tools to significantly boost productivity.

So, we will certainly add employees, but the leverage provided by new hires is far higher than before.

You can view this as a structural adjustment—people need to relearn how to work. It’s not just about “what” to do, but “how” to do it. The learning and “learning-to-learn” process will last about a year, after which new employees can achieve maximum leverage effects.

My sense is a one-year reskilling cycle is a significant operational risk that boards need to budget for explicitly.

Brad Gerstner: I feel we are on the verge of a significant productivity leap. When speaking with you or Michael Dell, I sense that most companies have not even begun to restructure their workflows to extract maximum leverage from intelligent agents.

But in the next two to three years, this will yield substantial benefits. I am also optimistic that it will create net jobs, although enterprise employee growth may lag behind revenue growth—that is the manifestation of improved productivity. Accumulating these efficiencies creates incremental value derived from productivity gains, which can be invested in creating things that did not exist before.

Satya Nadella: Absolutely correct. This applies even to software development.

Every organization has a large backlog of IT tasks; these intelligent agents will help us manage this backlog and realize the vision of “evergreen software.”

At the same time, the level of abstraction for knowledge work will change, workflows will adjust accordingly, and this will meet the changing demands of industrial products.

What concerns me is that the shift to “evergreen software” requires rigorous governance to prevent automated technical debt from accumulating silently.