Having spent 2 decades in senior technical leadership at IBM, I have a firsthand and nuanced understanding of how the company thinks, innovates, and reinvents itself. That perspective, despite dramatic headlines, gives me confidence in its long-term future.
Curator’s Note: As a former IBMer with two decades of senior technical leadership, the author, Dr Mehmet Yildiz, reflects on the company’s resilience despite recent challenges, including a significant market decline. He emphasizes that IBM’s sustainable strength is in its ability to adapt and innovate amid technological shifts, much like its historical transitions during the mainframe and Internet eras. The author asserts that recent revenue shortfalls result from temporary execution issues rather than structural declines, as markets often misinterpret short-term signals. By focusing on deep technical expertise and customer trust, IBM remains poised for recovery. The author encourages investors to differentiate between execution challenges and long-term viability, advocating for a balanced perspective on IBM’s future.
Although IBM Had “Its Worst Day in 115 Years,” I Still Believe in Its Future.
When I saw IBM lose roughly a quarter of its market value in a single trading session, my first reaction was not disbelief but perspective earned through experience. During more than four decades in enterprise and business computing, including over two decades in senior technical leadership at IBM until my retirement, I watched the company navigate technological revolutions that fundamentally changed our industry.
I also witnessed commentators repeatedly declare that IBM had reached the end of its relevance. Yet the company continued to evolve, sometimes slowly, sometimes imperfectly, but it kept evolving and still remains relevant for thousands of customers globally.
Among thousands of articles in the news in the last few days, this observation from The Wall Street Journal was interesting: “International Business Machines, a company that helped send humans to the moon, worked on America’s Social Security system and built a supercomputer that beat contestants at “Jeopardy!” is reckoning with the punishing realities of the AI boom.” The ironic part is that IBM started AI with Watson much earlier than many competitors. I will cover that story in another article as it is a highly complex technical and business matter.
I joined IBM in the 1990s when undertaking my doctoral degree as an ethnographic researcher in cognitive science because I admired its culture of scientific curiosity and disciplined engineering. Throughout my years there, I worked with some of the brightest inventors, scientists, architects, researchers, business consultants, and technologists in our industry. I also had the privilege of being mentored by the late Dr. Benoit Mandelbrot.
While no organization is perfect, that experience taught me that IBM’s greatest asset has never been a particular product or business unit. It has always been its ability to bring together deep technical expertise, enterprise thinking, and customer trust to solve complex business and technology problems that few others were prepared to tackle.
The headlines were dramatic, reflecting the scale of the market reaction. IBM experienced the largest single-day percentage decline in its 115-year history, erasing approximately US$67 billion in market value after warning that second-quarter revenue would fall short of expectations. For many observers, this appeared to confirm a familiar narrative that artificial intelligence had finally left another technology giant behind.
After studying the company’s announcement, reading the CEO’s (Mr Arvind Krishna) letter to investors, and comparing it with broader developments across the technology industry, I reached a different conclusion.
IBM certainly has execution challenges. However, I do not believe this event represents the collapse of IBM’s strategy or its long-term relevance. Instead, it reflects a significant but temporary realignment in enterprise AI technology spending during one of the fastest infrastructure transitions our industry has experienced.
The distinction matters because markets usually react to headlines long before they evaluate the underlying business fundamentals. The most important observation is that enterprise customers did not suddenly abandon technology investments. Instead, they reallocated their budgets.
Many organizations accelerated purchases of AI servers, high-bandwidth memory, advanced networking equipment, and specialized computing infrastructure ahead of anticipated price increases and continuing supply constraints. Those investments inevitably consumed capital that might otherwise have been allocated to software upgrades, consulting engagements, and traditional infrastructure projects.
This pattern is neither surprising nor unprecedented. It is both predictable and historically familiar. Every major computing revolution has redirected enterprise spending before creating new opportunities further up the technology stack.
We observed similar cycles during the transition from mainframes to client-server computing, from proprietary systems to the Internet, from on-premises infrastructure to cloud computing, and later from virtualization to hyperscale platforms.
Infrastructure almost always receives investment first because nothing else operates without it. Applications, optimization, governance, cybersecurity, and business transformation generally follow the establishment of the foundational layer.
Viewed through that historical lens, IBM’s revenue shortfall appears less like a structural failure than a timing issue within a much broader industry transition. The market, however, responded with extraordinary speed.
A revenue disappointment measured in hundreds of millions of dollars translated into tens of billions of dollars disappearing from IBM’s market capitalization within hours. The magnitude of that reaction deserves careful examination.
Markets frequently extrapolate short-term signals into long-term assumptions. Investors understandably dislike uncertainty, particularly during periods of rapid technological disruption.
History demonstrates that financial markets overshoot in both directions. Excessive optimism inflates valuations during periods of excitement, while excessive pessimism can temporarily undervalue companies confronting transitional challenges.
Neither extreme necessarily reflects intrinsic value. History offers an important reminder. IBM has already survived what many once considered an existential crisis. For example:
When late Louis V. Gerstner Jr. became CEO in 1993, the company was losing billions of dollars, many respected analysts believed IBM should be dismantled, and confidence in its future had nearly disappeared.
Rather than breaking IBM into smaller companies, Gerstner preserved it as an integrated enterprise, redirected its focus from internal processes to customer outcomes, expanded its services business, and introduced the vision of e-business, which helped redefine enterprise computing during the Internet era.
His leadership demonstrated that enduring technology companies recover not simply by inventing new products, but by understanding where customers are heading and aligning the entire organization behind that future.
Today’s AI transition presents a very different technological challenge, yet the underlying leadership principles remain remarkably similar. Both periods demand disciplined execution, strategic clarity, and the willingness to rethink business models before competitors force the change.
Having worked within IBM during much of that period, I observed a culture that encouraged engineers and architects to think in decades rather than quarters. We certainly debated products, technologies, and strategy, but our conversations consistently returned to one central question: how would this help enterprise clients solve complex business problems? That long-term customer-oriented mindset remains one of IBM’s greatest competitive strengths.
Another important lesson from IBM’s recent announcement concerns execution rather than technology. Arvind Krishna did not argue that artificial intelligence had made IBM’s software obsolete. Instead, he acknowledged that the company faltered in execution as customers delayed some purchasing decisions while evaluating emerging AI capabilities alongside new cybersecurity and governance considerations.
Those are fundamentally different problems. Execution issues can generally be corrected through stronger products, clearer customer engagement, improved operational discipline, and better commercial execution. Structural technological irrelevance is far more difficult to overcome. Investors should avoid confusing the two.
Long-term investors generate their greatest returns by distinguishing temporary execution challenges from permanent strategic decline. History shows that markets frequently blur that distinction during periods of technological disruption.
The broader market reaction also affected Salesforce, Adobe, Workday, and ServiceNow. That suggests investors were responding not merely to IBM’s performance but to a broader belief that AI infrastructure spending is temporarily consuming budgets previously allocated to enterprise software.
There is some truth in that observation. Organizations cannot purchase everything simultaneously. Capital allocation always involves trade-offs. During periods of technological transition, the immediate priority becomes acquiring foundational infrastructure.
Once those investments mature, organizations typically redirect their attention toward software, integration, governance, cybersecurity, productivity, and business optimization.
Working in enterprise architecture taught me long ago that technology evolves in layers rather than isolated products. Hardware enables platforms. Platforms enable applications. Applications enable business capabilities. Business capabilities ultimately generate economic value.
Judging the future of enterprise software while organizations are still constructing their AI infrastructure is rather like judging skyscraper occupancy while the foundations are still being poured. The episode also highlights a broader question that every enterprise will soon confront: the economics of artificial intelligence.
Several respected industry leaders have compared AI models to different grades of fuel. Premium frontier models deliver exceptional performance, but at a considerably higher cost. Meanwhile, smaller open models continue improving rapidly while becoming dramatically less expensive.
This comparison highlights an important reality. Artificial intelligence is rapidly becoming an economic optimization challenge rather than simply a technical one.
As leading models continue converging in capability while declining in cost, competitive advantage will come from selecting the right model for the right workload rather than automatically choosing the most powerful one.
Much like cloud computing, AI is gradually becoming an exercise in intelligent resource allocation. Organizations that optimize cost, performance, governance, and business value simultaneously are likely to outperform those pursuing technical superiority alone.
Organizations will ask whether a particular business activity genuinely requires the most expensive frontier model or whether a smaller, faster, and significantly less expensive model can deliver comparable business value. Most everyday enterprise workloads probably do not require the highest-priced intelligence available.
Just as enterprises learned to govern cloud consumption with discipline, they will eventually manage AI consumption with the same financial rigor.
The organizations that succeed will not necessarily purchase the most expensive intelligence. They will purchase the most appropriate intelligence for each business objective. Ironically, this emerging discipline aligns remarkably well with IBM’s historical strengths.
For more than a century, IBM has built its reputation by helping enterprises operate complex, mission-critical environments where reliability, governance, security, compliance, scalability, and long-term operational stability matter far more than technological novelty.
While consumer technologies usually capture the headlines, enterprise computing is ultimately judged by trust, resilience, and measurable business outcomes.
Banks, governments, insurers, manufacturers, healthcare providers, and large global enterprises do not make technology decisions based solely on benchmark performance or the latest AI model rankings. They evaluate operational risk, regulatory obligations, cybersecurity, integration complexity, lifecycle management, vendor stability, and return on investment over many years.
Those considerations remain just as important in the age of artificial intelligence. This is precisely where IBM has historically differentiated itself. Its greatest strengths have not been consumer technology or short-lived market excitement. Instead, they have been in designing, integrating, governing, and operating complex enterprise systems that organizations depend on every hour of every day.
IBM also possesses capabilities that receive relatively little attention during periods of market volatility. Hybrid cloud, enterprise security, automation, quantum computing, cognitive computing (Watson), AI governance, consulting expertise, and decades of scientific research continue to provide strategic assets that few organizations can replicate quickly.
These businesses may not generate the same level of excitement as consumer AI applications, but they solve problems that the world’s largest enterprises must address every day.
Having worked with IBM technologies throughout half of my professional career, I have learned that the company’s greatest competitive advantage has never been chasing fashionable technology trends. Instead, it has consistently translated emerging technologies into practical enterprise capabilities that organizations can trust, govern, integrate, and operate at global scale.
That approach does not produce spectacular quarterly headlines. It does, however, explain why IBM has remained relevant through multiple generations of computing while many once-celebrated technology companies have disappeared, merged, or faded into history.
None of this suggests that IBM should be immune from criticism. Execution matters. Innovation matters. Product quality matters. And customer experience matters.
Markets appropriately reward companies that execute consistently and penalize those that do not. IBM must continue demonstrating that its AI strategy translates into measurable customer outcomes, sustainable financial performance, and long-term shareholder value.
Healthy skepticism remains entirely appropriate. At the same time, investors should distinguish temporary operational setbacks from permanent strategic decline. Those are fundamentally different concepts, and history repeatedly reminds us that markets often confuse them during periods of technological disruption.
The history of technology also teaches another valuable lesson. Companies that endure for more than a century do not follow smooth or predictable trajectories. They experience reinvention, restructuring, missed opportunities, remarkable recoveries, and occasional disappointments. Their longevity depends less on avoiding disruption than on learning, adapting, and executing more effectively than competitors over successive generations of technology.
IBM’s recent decline represents one of the most painful trading days in its remarkable history. Yet history also reminds us that defining a century-old institution by a single quarter or even a single year is remarkably misleading. Markets price uncertainty every day. Sustainable companies create value over decades.
As both an enterprise architect and a long-term observer of the technology industry, I remain optimistic, not because IBM is guaranteed success, but because the challenges it currently faces are fundamentally solvable. For example, budget cycles change, infrastructure investments mature, enterprise priorities evolve, and business execution improves when customer demand returns.
In my opinion, one mistake investors might make is assuming that technological leadership belongs exclusively to the newest companies. Innovation certainly creates new winners, but business history also shows that established organizations capable of continuous reinvention remain influential far longer than critics expect.
IBM has already reinvented itself through the mainframe era, personal computing, the client-server revolution, the Internet, open systems, services, cloud computing, and now the age of artificial intelligence. Each transition demanded difficult decisions, new capabilities, and a willingness to challenge long-held assumptions.
I believe this transition will be no different. One disappointing quarter does not erase more than a century of engineering excellence, scientific discovery, enterprise innovation, and organizational resilience. Nor does one difficult trading day determine the future of an institution that has repeatedly demonstrated its capacity to learn, adapt, and create value across successive generations of computing.
If four decades in enterprise technology and more than two decades inside IBM have taught me anything, it is this: companies built on disciplined innovation, deep technical expertise, customer trust, and continual reinvention should not be underestimated.
Markets measure performance in quarters. Institutions like IBM are built over generations. Confusing the two has been an expensive mistake for investors. After watching IBM reinvent itself through multiple computing revolutions during my career, I would not underestimate its ability to do so once again.
For technology leaders and business investors navigating this AI inflection point, the real question is not whether disruption will challenge established companies. It is which organizations can learn, adapt, and execute faster than their competitors. History suggests IBM has demonstrated that capability multiple times.
Related to this story, I recently wrote about 2 companies, NVIDIA and Illumina, and shared my research and perspectives on them. I link them here for interested readers on this topic:
I Can’t Tell You to Buy NVIDIA Shares Now, But I Can Guide You on How to Evaluate Tech Stocks
Why and How Should Long-Term Investors Evaluate the Companies Building the Infrastructure of Entirely New Economies for…medium.com
Why Illumina May Teach Us More About the Architecture of Future Medicine Than About Today’s…
As part of this series, I covered NVIDIA in a previous story and will discuss Illumina in this one using the same…dr-mehmet-yildiz.medium.com
Thank you for reading my perspectives. I wish you a healthy and happy life.
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