Revisionist History: IBM CEO Arvind Krishna — Creating Smarter Business with AI and Quantum
Podcast: Revisionist History
Host: Malcolm Gladwell
Guest: Arvind Krishna, CEO and Chairman of IBM
Date: November 27, 2025
Episode Overview
In this engaging live conversation recorded at IBM’s New York office, Malcolm Gladwell interviews Arvind Krishna, IBM’s CEO and chairman. The pair discuss IBM’s unique approach to solving business problems, Krishna’s personal path from engineer to executive, his views on the evolution— and hype—of AI, and his deep passion for quantum computing. Throughout, Krishna shares candid insights into decision-making at IBM, the importance of blending technology and business strategy, and why quantum computing is poised to be as transformative as the semiconductor.
Key Discussion Points & Insights
The Essence of IBM (01:20–02:48)
- What does IBM do?
- Krishna positions IBM as a partner helping clients improve their businesses by deploying relevant technology, not being bound to one product or even one technological approach.
- IBM is currently focused on hybrid cloud and artificial intelligence, with quantum computing on the horizon.
"IBM's role is to help our clients improve their business by deploying technology... But it is about improving their business, not just giving them a commodity." — Arvind Krishna (01:20)
- Product-Agnostic, Not Technology-Agnostic:
- IBM’s solutions evolve with time; in 25 years, IBM might look unrecognizable today, perhaps being all-quantum, all-open-source.
A Culture of Innovation: Barcodes and Beyond (02:27–03:12)
- The story of the barcode’s invention (by an IBM field engineer) epitomizes how IBM solves practical problems at the highest technical level.
- The capability to transform industries (e.g., inventory management) with inventive minds at any level.
"...he came up with the idea of the barcode and that changed inventory management forever." — Arvind Krishna (02:49)
Krishna’s Early Career and Predicting the Future (03:25–07:00)
- Early work (1990s) on networking and what would become Wi-Fi.
- Predictions from the 1990s:
- Foresaw convergence of computing and networking into the Internet.
- Anticipated video streaming and on-demand movies (but underestimated how long adoption would take).
- The gap between invention and market adoption—how technical innovations get stymied by business imagination.
"One of the reasons I do what I do today... is because of what I saw happen with Wi-Fi... The business looks at it and says: We think the market is confined to warehouse workers doing inventory... I became convinced that I can't just help invent it. I got to think about now, how do you market it?" — Arvind Krishna (07:09)
The Blind Spot Between Tech Creation and Social Change (08:30–10:55)
- The delay in societal understanding and adoption of new technologies is fundamental; many breakthroughs require creating new markets, not just new products.
- Learning the language of business (balance sheets, stock value) was a personal evolution for Krishna, broadening his perspective beyond engineering.
What Business Leadership Requires (11:06–11:52)
- Integrate technical expertise with business intuition; be curious and build up a holistic view.
- Trust in your own evolving intuition and lean on trusted networks for skills outside one’s core discipline.
Reflecting on Past Mistakes (12:01–14:10)
- Incorrect past assumptions: believed that telecoms (comm companies) would own networking due to existing business models, but they lost out because of their pricing paradigms (charging by the minute vs. flat rate) and design philosophy (smart network, dumb devices vs. the "stupid" Internet).
"The winners in networking were those who set flat price... and the current Internet is completely dumb on the inside. All the intelligence is the computer at the end." — Arvind Krishna (12:59)
Key Decisions as CEO: Red Hat and Strategic Persistence
The Red Hat Acquisition (14:14–18:23)
- The 2018 acquisition was initially seen as highly controversial—IBM buying a major open source company.
- The logic was to become the ‘best partner’ for cloud companies, not compete head-to-head, carving a unique space in hybrid and multi-cloud.
- Persistence was critical:
- Took 6–9 months to convince key stakeholders.
- Four to five years for the acquisition to be widely vindicated.
"2018, I proposed... we should buy a company called Red Hat. IBM does proprietary, but that was open source. The stock fell 15% on the day we announced it... And today most people will say this is the most successful acquisition IBM has ever done." — Arvind Krishna (14:23)
Leadership Style and Decision-Making (16:38–17:21)
- Persistent, patient, and unwilling to easily give up, but not prone to anger.
- Stubbornness as a defining personal trait; recognized both by himself and his family.
The Nature of Hype, Investment, and Innovation Cycles
Lessons from the Internet Era (23:45–25:48)
- Comparison between the current AI hype and the dot-com bubble.
- Overinvestment is a feature: most startups fail, but the few that succeed (like Amazon, Google) create enough value for all.
- The dynamism and “creative destruction” of the US model accelerates innovation.
AI: Hype, Promise, and Where We Miss the Point (26:19–29:23)
- AI is not underestimated but is being misapplied in many enterprises—focused on “shiny objects” instead of scalable, core business improvements.
- Underutilization of AI to improve areas like customer service or software development productivity.
"There’s a lot of basic things you can do to use AI to improve the business today... I think only maybe 5% of the enterprises on both those metrics today." — Arvind Krishna (27:29)
AI's Role in the Developing World (27:59–30:32)
- The greatest ROI from AI may come in countries where it can radically leapfrog traditional solutions (e.g., environmental planning, health care, agriculture).
- In developed world, AI is essential for maintaining quality of life in the face of shrinking workforces.
IBM’s Approach to AI (30:52–33:13)
- Focused on enterprise users, not consumers—models are kept smaller, more efficient, domain-specific, and easier to customize than massive consumer LLMs.
- IBM eats its own dog food: applies AI to its own operations and shares learnings with clients.
"If you're focusing on the enterprise, that actually takes away the focus of having to go to extremely large models, which by definition are going to be computationally expensive..." — Arvind Krishna (31:44)
AI’s Bottlenecks and Our Wrong Turn with LLMs (34:29–36:02)
- Krishna is skeptical that current LLMs alone will bring breakthroughs like AGI.
- Next advances require fusing external structured knowledge with statistical models and, crucially, improving computational and algorithmic efficiency by orders of magnitude.
"On LLMs alone... I think we can get a thousand x efficiency in power and cost and compute from today. So if you make something a thousand times cheaper, would people use more of it? Yes..." — Arvind Krishna (35:20)
- The field’s FOMO and winner-take-all mentality drives overinvestment in brute force methods, not fundamental improvements. “First win, then become efficient.”
Quantum Computing: The Next Big Revolution
Why Krishna Loves Quantum (37:06–38:48)
- Quantum is viewed as the third great paradigm in computing:
- Classical: operates on bits (basic arithmetic).
- AI/GPUs: leverages linear algebra, good for perception-like problems.
- Quantum: brings a third kind of math—abstract algebra—changing which problems are tractable.
"Quantum adds a third kind of math... If you can do a third kind of math, which algorithms are suited to that third kind of math? So it excites me because we can now approach algorithms that you just could never do on the other two." — Arvind Krishna (37:06)
Quantum is Close to “Its Internet Moment” (39:01–45:13)
- Three-to-five years from a breakthrough that will "shock people."
- Example: HSBC achieved 34% better accuracy in bond trading using quantum computation (vs. prior methods, where 1% improvements are huge).
- Potential in material science: e.g., modeling solid-state batteries in seconds versus years.
- Logistic optimization: Quantum could save millions of gallons of fuel with slightly improved routing, with knock-on climate impacts.
Quantum’s Transformational Potential (44:31–45:52)
- Quantum computing could be as significant as the semiconductor for civilization.
- The field is under-discussed because its advances are abstract and difficult for most people to grasp—waiting for its "Netscape browser" moment to popularize it.
"Quantum... equal to semiconductor. And I think that if semiconductors vanished, modern life would stop. Like just stop." — Arvind Krishna (44:38)
IBM’s Internal Journey on Quantum (46:08–49:16)
- Krishna began investing IBM research resources in quantum back in 2015; it was nurtured over years before betting big as CEO.
- Deciding how much to invest relies on:
- Scarcity of specialized talent.
- Balancing ambitious goals with realistic timelines to avoid failure through excessive pressure.
- Open, candid debate between leaders and technical experts—calibrating the “Goldilocks pressure” for innovation.
Notable Quotes & Memorable Moments
- On Innovation and Market Creation:
"Many have created massive value and time have all created markets, meaning they've all created new markets... you've got to get all three pieces going." — Arvind Krishna (08:41)
- On the Red Hat Deal:
"The stock fell 15% on the day we announced it. And today... the most successful acquisition IBM has done in all time..." — Arvind Krishna (14:23)
- On Quantum’s Promise:
"Quantum today is where GPUs and AI were in 2015. And I bet you every AI person is thinking and hoping, 'I wish I had started doing more in 2015...'" — Arvind Krishna (27:29)
- On Transformational Impact:
"Quantum... equal to semiconductor. And I think that if semiconductors vanished, modern life would stop..." — Arvind Krishna (44:38)
- On Leadership Style:
"I'm very persistent and I'm very patient. I'm also probably very impatient, but I'm not a yeller and screamer... if I think we're going to do something, I can be remarkably stubborn..." — Arvind Krishna (16:47)
Important Timestamps & Segments
- 01:20 — What is IBM? Krishna’s vision
- 03:25 — Early IBM career, predictions for networking/internet
- 07:09 — Lessons from Wi-Fi invention and importance of business sense
- 14:23 — The Red Hat acquisition: logic and vindication
- 23:45 — Investment cycles: Parallels with the Internet age
- 27:29 — AI: Misapplications, wasted potential, and untapped opportunity
- 29:23 — AI’s impact in developing vs. developed world
- 31:44 — IBM’s approach to AI versus the “shiny” consumer LLMs
- 34:29 — The LLM problem: hallucination and limitations to progress
- 37:06 — Why Quantum is the next big deal—and how it differs from AI
- 39:13 — Real-world impact of quantum: HSBC bond trading example
- 44:38 — Quantum’s significance compared to the semiconductor
- 46:08 — IBM’s internal path and decision-making in nurturing quantum
- 48:42 — Leadership, technical pressure, and open disagreement
Tone and Flow
The conversation is intellectually lively, candid, sometimes irreverent, with Gladwell blending curiosity and humor. Krishna is direct, humble about past mistakes, confident in convictions, and passionate about both technological potential and business realities.
Summary Takeaways
- IBM defines itself not by a product, but by helping clients solve complex business problems using the right tech mix.
- True leadership in innovation comes from blending technical vision, business sense, and relentless curiosity—not just invention, but market creation.
- Enormous hype (and overinvestment) is necessary to create world-changing tech, but learning from failure and scaling the right bets is key.
- Quantum computing is poised—within a few years—to spark a transformation as profound as the original Internet moment.
- Krishna’s story exemplifies how persistent, open-minded leadership and willingness to challenge both oneself and one's organization can steer a technological giant into the future.