The South Park Commons hosts welcome Microsoft CEO Satya Nadella to their podcast, where they explore the "minus one to zero phase" – that period where entrepreneurs wander through the desert until they build conviction about what they want to work on for the next 5-10 years.

The hosts frame the conversation around exploring pivotal moments in Satya's career at Microsoft, examining what the "minus one" phase looked like during those critical junctures, and how he navigated challenges throughout his meteoric rise at the company.

"This minus one to zero is a great sort of framing," acknowledges Satya as he joins the conversation with hosts Aditya Agarwal and Ruchi Sanghvi.

Timestamp: [01:03-01:53]

In 1992, Satya Nadella was working at Sun Microsystems – a company he describes as "pound for pound, a stunning company" that made its own chips, operating systems, and was at the core of distributed systems. While at Sun, he attended a developer conference at Moscone Center in 1991 where Windows NT and Win32 were first introduced.

Satya had a pivotal realization that would shape his career trajectory: he believed x86 architecture was going to win in the computing space. Despite not being a Windows user or thinking much of DOS, he saw that Microsoft was positioned to capitalize on this architecture shift.

"I just felt like x86 was going to win and that's what got me to go to Microsoft... I felt like this is the company that is going to be able to capitalize on that, but more importantly, truly be that democratizing force."

The Microsoft interview process was intense and challenging, featuring coding problems on whiteboards and a final "as appropriate" interview – the last hurdle with someone whose name wasn't on the schedule. Satya's final interviewer posed an unexpected question:

"You're crossing the street and a baby falls. What are you going to do?"

Satya answered that he would call 911, after which the interviewer walked him to the door and said, "You know what you should do when a baby falls? You should pick them up and hug them." Satya thought he had failed, but ended up getting the job – a moment he considers a life lesson.

Timestamp: [02:07-06:38]

Aditya observes that Microsoft has played such an important role in technology history that it means different things to different people, and asks Satya about the culture during the '90s when Microsoft was building a diverse set of bleeding-edge technologies.

Satya frames his response in the context of Microsoft's upcoming 50th anniversary on April 4th. He explains that founders Bill Gates and Paul Allen initially conceptualized Microsoft as a "software factory" – a novel concept when there was no established software industry.

"Bill and Paul had was that they were building a software factory... there was no software industry before Microsoft."

The company didn't limit itself to specific categories but went wherever software could be applied. From creating the BASIC interpreter for the Altair to developing Flight Simulator (which predates Windows), Microsoft built a talented team dedicated to creating the best software products needed in the world.

Satya finds this framing powerful for founders because it avoided the trap of falling in love with a single product, technology, or business model – all of which eventually run out of gas. By focusing on being a "software factory," Microsoft maintained a growth mindset and learning culture that could adapt to new innovations.

"Longevity by itself is not sort of a goal, right? Relevance is. And therefore, what is it that gives you the courage to be relevant tomorrow? That's the thing that I'm trying to kind of really study, learn, push myself, and even articulate for myself."

As Microsoft looks toward the future with AI and an "agentic world," Satya contemplates what it means to be a software factory in this new era – seeking to reinterpret Microsoft's history to maintain relevance in changing times.

Timestamp: [06:39-11:20]

• Satya Nadella identified a pivotal technology trend (x86 architecture winning) that guided his career decision to join Microsoft from Sun Microsystems in 1992
• Microsoft's original concept as a "software factory" rather than being defined by any single product or technology gave it the flexibility to evolve and remain relevant
• Even in high-pressure interview situations, authentic human responses (like Satya's 911 answer) can reveal character and values
• Technology platform shifts may take time but can become "inevitable" – recognizing these shifts early is crucial for career and company positioning
• For companies to achieve longevity, maintaining relevance is more important than the longevity itself – requires constant learning and reinterpretation
• Microsoft's early culture encouraged looking at where software could be applied next, rather than being wedded to existing products
• In today's AI era, Satya is actively working to redefine what it means to be a "software factory" in an intelligent, agentic world

Timestamp: [01:03-11:20]

Aditya asks Satya about Microsoft's journey from dominating the tech landscape for 25 years to experiencing a challenging period between 2000-2010, described as "wandering in the desert" - going from "winning everything" to "not winning for a while."

Satya acknowledges the stark reality of Microsoft's dominance in the 90s, especially the late 90s, when there was "real daylight" between Microsoft and its competition. He contrasts this with today's vibrant tech ecosystem where multiple major companies compete and where "anybody can come from anywhere" to create a generational company, citing OpenAI as an example.

"There was Microsoft and there was competition, but it is not like today, right? Where you have whatever this seven, eight companies plus pretty much in any given day anybody can come from anywhere like as OpenAI has even demonstrated to sort of create what is a generational company."

Satya reflects on how success itself created challenges, as the company began to forget what made it successful in the first place. The struggles of the 2000s weren't from a lack of ideas or initiatives, but rather from being too attached to existing products and business models, unwilling to venture into "inconvenient" territories.

"You miss stuff because you're too much in love with sort of what you have today or your business model of today and are not willing to go somewhere which is inconvenient, or just don't have a complete thought."

He shares one of the greatest pieces of advice he received from Steve Ballmer: the importance of having a "complete thought" – considering all aspects of a product from technology to go-to-market strategy. The most dangerous times for a company, Satya observes, are during peak success when people who joined during growth periods believe they caused the success rather than recognizing they "rode the wave" and now need to "refound" the company.

Timestamp: [11:25-14:47]

Aditya reflects on his experience working with Microsoft in 2006 when he was developing Facebook search and collaborating with Satya, who was then running Bing search. He recalls monthly visits to Seattle exploring how to incorporate Facebook data into Bing search results.

"Despite being a big company, we could actually make progress, which had surprised me at that point. Microsoft took a lot of shots on goal during that time, and I think that's to your credit."

Satya acknowledges Microsoft's ability to persist with initiatives, sometimes "stubbornly persist," and emphasizes that taking shots on goal is necessary in the tech industry. He points out the unforgiving nature of network effects in technology – they're beneficial when you have them but terrible when you're on the wrong side of them.

"You got to take shots on goal and just get... I mean the reality is the network effects of tech are unforgiving. They're great when you get them and they're terrible when you're on the wrong side of them, but therefore you have to take shots on goal is probably the way to stay relevant."

This philosophy applies not just to startups but even to companies at Microsoft's scale. Satya offers a sobering perspective on the tech industry, noting that there's no true "franchise value" in tech if measured by relevance rather than earnings per share. To stay relevant, companies must continually build and invent, not just keep up.

"I don't think there's any franchise value in Tech. It absolutely is a pretty scary industry because if you measure it by relevance... you want to be relevant, you do need to actually build something that is not just keeping up but it's about inventing."

The fundamental bias of the tech industry, according to Satya, is that there will always be disruptors, creating both challenges and opportunities.

Timestamp: [14:47-17:40]

Ruchi notes that she views Microsoft as a comeback story that began when Satya became CEO, transforming what seemed like another fading tech giant into a revitalized innovator. She asks about his "minus one days" of becoming CEO and what initial steps he took to drive that turnaround.

Satya begins by giving full credit to his predecessor Steve Ballmer for Microsoft's success in the cloud era, emphasizing that he worked under Steve's leadership and learned crucial lessons about effective CEO leadership from him.

"A lot of what I learned on how to be an effective CEO is actually from Steve Ballmer, watching him give me permission as a direct report to go spend the money even when the street was not giving permission. That's what a real CEO does - defy conventional wisdom and empower leaders and people inside the organization."

As the first non-founder CEO (noting that while Steve technically wasn't a founder, he had "founder status" in the company), Satya realized he couldn't simply try to fill his predecessor's shoes. Instead, he needed to adopt what he now calls a "refounding" mindset, drawing on Reid Hoffman's terminology.

"I came into that seat knowing that I just can't go in and try and fill his shoes. So I had to, as I describe it, as a moral CEO... that refounding mindset I had."

Satya recognized that as CEO, he needed to make explicit what founders take for granted through their "founding moral authority" – things like mission and culture that are implicitly understood when following a founder.

His first priority was regrounding the company in its mission. Microsoft's original mission of "a PC in every home and every desk" had largely been achieved in the developed world by the end of the '90s, leaving the company somewhat directionless. Satya went back to the company's roots – the idea behind the BASIC interpreter for the Altair was putting software in others' hands so they could create more software.

"When we sort of what was the thing behind the BASIC interpreter for the Altair, it was about putting software in the hands of others so that they can create more software. I said if that was relevant in 1975, in 2014 when I became CEO, it is going to be more relevant."

This led to the mission of "empowering people and organizations," recognizing Microsoft's unique position in creating software that helps build institutions that outlast individuals. This reframing helped answer the question of whether Microsoft was a consumer or commercial company – they were "a software company that wants to serve and empower people and organizations."

Satya's second focus was creating new language around Microsoft's culture, embracing Carol Dweck's concept of "growth mindset." This approach wasn't seen as new dogma from a new CEO but as established work in child psychology that appealed to people.

"Thank God for Carol Dweck's work. That was super helpful to me because it was not considered new dogma from a new CEO, but it is work in child psychology that appeal to people."

These two elements – regrounding the mission and establishing a growth mindset culture – helped galvanize energy to execute strategies that were actually quite consistent with what Satya had been advocating before becoming CEO.

"The strategy - I've written the same stuff, I was talking the same stuff before. It's just nobody paid attention to it. Then after becoming CEO, somehow things happened a lot faster."

Timestamp: [17:40-22:21]

• The most dangerous time for successful companies is during peak success – when they forget what made them successful and people who "rode the wave" believe they created the success
• Having a "complete thought" about products (considering technology, go-to-market strategy, etc.) is essential for success – something founders naturally do but organizations can lose
• There is no true "franchise value" in tech if measured by relevance rather than earnings – companies must continually build and invent to stay relevant
• Network effects in technology are unforgiving – beneficial when you have them but terrible when you're on the wrong side
• As a non-founder CEO, Satya had to make explicit what founders take for granted through their "founding moral authority" – particularly mission and culture
• Microsoft's resurgence under Satya began by regrounding the company in its original mission of empowering others to create with software
• Adopting Carol Dweck's "growth mindset" framework provided Microsoft with an evidence-based approach to cultural transformation that wasn't seen as mere "new dogma"
• The concept of "refounding" a company – revisiting core principles while adapting to new realities – is critical for established organizations to remain relevant
• Often, the same strategies that weren't gaining traction before new leadership can suddenly accelerate once leadership changes – execution and authority matter

Timestamp: [11:25-22:21]

When asked about a contrarian leadership belief that founders should consider, Satya offers a perspective that challenges the common "hero narrative" many leaders adopt.

"One of the things about sort of I don't know whether it's a Founder thing, but a lot of leaders have it right, which is 'oh everything was broken before and so therefore I rescued it.' I'm very suspect of that."

Satya expresses skepticism toward leaders who claim they "saved the day" by fixing everything that was broken before them. While such narratives might make the leader feel better, they often miss the deeper measure of leadership success.

Instead, Satya proposes a more long-term view of leadership effectiveness: the organization's performance after your departure is the true test of your impact.

"If the place falls apart after you leave, then you built nothing. So in some sense, even as a CEO, I kind of look at it and say, you know, it's the fourth CEO, the fifth CEO of Microsoft and their success that will be the true story of whether I did my job or not."

This perspective shifts the focus from personal glory to building sustainable systems, processes, and cultures that outlast any individual leader – a particularly relevant insight for founders who often deeply identify with their creations.

Timestamp: [22:32-23:36]

Ruchi shifts the conversation to Microsoft's investments in startups, recalling a memorable story from 2007 when Microsoft's deal team came to Facebook's offices at 8 PM while the engineering team was coding with electronic music blasting through the office. The deal team got so excited that they pulled an all-nighter (something the Facebook team did regularly) and worked out deal terms in a conference room, culminating in a $240 million investment in Facebook that week.

She also notes Microsoft's more recent investments in AI companies like OpenAI and Mistral, asking how startups should think about partnering with Microsoft and pitching to their fund, M12, given how intimidating such a large organization can be.

Satya clarifies that while collaboration with startups is important to Microsoft, they're "not an investor" in the traditional sense. Their investments are strategically motivated rather than focused on financial returns.

"In Facebook, we wanted to invest not that it happened, but we wanted to invest because of what we thought could be a commercial relationship around advertising at that time... The intent was not an investment return, but it was about saying 'hey, can we in fact be a sponsor at that time of Facebook in order to be able to make some commercial advantage in an area that we were interested in.'"

With OpenAI, Satya notes that when Microsoft first backed them, they were a research lab. As OpenAI evolved into a successful product company, Microsoft continued supporting them because they wanted to be supportive of a partner, not merely as an investor but more importantly as a commercial partner.

Satya emphasizes that Microsoft's identity as both a platform company and a partner company drives their investment strategy:

"A platform is not stable without partners, and if you don't have partners, you can't be a platform."

This philosophy guides Microsoft's approach to startups and established companies alike. Satya references his early days working with SAP, where Microsoft built their relational database SQL to work underneath SAP's products. He looks for "long-term stable sort of win-win constructs" between Microsoft as a platform company and partners who may themselves be building platforms.

Timestamp: [23:36-27:36]

Aditya raises a provocative question about Microsoft's AI strategy, noting that competitors like Google and Meta have developed their own foundation models. He asks if Satya feels "left out" and if he believes that "real companies have foundation models nowadays," drawing a parallel to the old hardware industry saying that "real people have fabs."

Satya approaches this question by first emphasizing Microsoft's identity as a "full stack systems company" that wants full stack systems capability, acknowledging that foundation models are indeed important. He then explains the rationale behind Microsoft's partnership with OpenAI rather than building their own competing models.

When Microsoft initially invested in OpenAI, it was betting on the conviction that Sam Altman, Ilya Sutskever, Greg Brockman, and the OpenAI team had regarding scaling laws in AI. Satya mentions reading a paper on scaling laws that influenced his decision-making:

"It was interesting I think it was Dario who wrote the paper that I first read, which was the scaling laws paper, and that was the bet I placed."

Particularly important was OpenAI's focus on natural language processing, which aligned with Microsoft's historical interests as a knowledge worker company:

"I don't think we would have done what we did if it was not natural language, because of being a knowledge worker company we were obsessed about natural language. That was Bill's thing all the years."

Satya contrasts this approach with Microsoft's previous attempts to "schematize the world" – building ontologies and schemas in the belief that intelligence would emerge from structured data. He admits feeling uncomfortable with this approach because "the world is too messy for SQL to work," and saw in OpenAI's work a different path to semantic understanding.

As the partnership evolved and showed promising results, Microsoft developed a comprehensive strategy around OpenAI's models:

"We built a system underneath it, we built tools around it, we're building products about it. So to some degree I have a long-term stable relationship with OpenAI and we have the IP rights."

Satya observes that models themselves are becoming commoditized, noting that "OpenAI is not a model company, it's a product company that happens to have fantastic models," which creates a mutually beneficial partnership. He believes the emerging industry structure requires not just models, but "a full system stack and great successful products" – areas where Microsoft is now focusing.

Timestamp: [27:36-31:05]

• True leadership success should be measured by an organization's performance after you leave, not by claiming you "rescued" a broken situation
• Microsoft's investments in startups are strategically motivated around commercial partnerships rather than seeking financial returns
• The dual identity as both a platform company and a partner company drives Microsoft's approach to ecosystem relationships
• Long-term stable "win-win constructs" between platforms and partners create more sustainable value than purely financial investments
• Microsoft's partnership with OpenAI was initially a bet on scaling laws in AI, particularly as applied to natural language processing
• The focus on natural language aligned with Microsoft's historical interests as a knowledge worker company
• Previous approaches to AI through structured data and ontologies failed because "the world is too messy for SQL to work"
• Models themselves are becoming commoditized; the real value lies in building complete systems and successful products around them
• Microsoft's strategy involves building systems underneath AI models, tools around them, and products on top of them
• OpenAI has evolved from a research lab to "a product company that happens to have fantastic models"

Timestamp: [22:32-31:05]

Aditya notes the incredible "tsunami of activity" in AI since ChatGPT entered public consciousness just two and a half years ago, spanning chips, data centers, frameworks, foundation models, vertical AI companies, consumer hardware/software, agentic software, and modern enterprise software. He asks Satya to identify overlooked areas that might present interesting opportunities for founders in the next five years.

Satya highlights three parallel developments in AI:

1. Knowledge workers and language models
2. Real-world action models
3. Models for science (chemistry, biology)

Beyond these obvious areas, Satya points to two overlooked opportunities:

First, the need for a fundamental redesign of system architecture to support AI workloads:

"I've been thinking about it even as a hyperscaler - what's the system architecture that truly will support this new [AI paradigm]... I don't think we've done a first principles re-architecture of the system. I mean, we fall in love with the AI accelerators, we threw a lot of them into existing data centers and said 'go forth and prosper,' and we've done quite a bit of it. But I think time has come to say: 'Hey, what is the next generation hyper-converged infrastructure? How should I think about compute, storage, and the AI accelerator together?'"

He suggests the time has come for "v2" of the data center as computer concept, reconsidering how distributed synchronous training jobs with reinforcement learning should be structured.

Second, Satya is surprised by the lack of distributed model architecture at runtime (not just for training):

"Why is it that I can't split between whatever is running on my 4 TFLOP NPU on my Copilot PC and the cloud? I'm assuming there has to be a model architecture and ML math breakthrough for that."

He acknowledges the tremendous work by the open source community, particularly DeepSeek, in making AI more efficient on existing infrastructure, but wonders why no one has fully leveraged the significant computational power available at the edge.

Aditya observes that this reflects Satya's earlier point about "complete thoughts" - many are building small parts of AI systems, but there isn't yet an overarching architecture.

Satya agrees and adds that even systems like GitHub Copilot or Microsoft's knowledge worker Copilot aren't yet complete systems:

"It's not like... it's not yet had the 'stored program' moment yet. In other words, there is no architecture, there's no memory system that is robust, that is multimodal. The tools use is a little too artisanal at least right now."

Despite these gaps, Satya emphasizes that great work is happening, with numerous companies developing both foundation models and systems to support them, suggesting we're in the "very early innings" of building more robust AI systems.

Timestamp: [31:11-36:36]

Ruchi asks Satya to define what true agentic behavior would look like beyond the current world of copilots, and how it might impact his day-to-day life in 5 years.

Satya offers a thoughtful reflection, beginning with a humorous observation about how the PC revolution changed knowledge work:

"If somebody had said to me pre-PC that what I would be doing is mostly in life for 33 years at Microsoft be a typist... you know, it would have felt like 'what, no way!' Computers are going to change my life, they're going to be unbelievable. And except what I do is get up in the morning and start typing and then hopefully get to some sleep in the night."

He speculates that in a truly agentic AI world, his daily routine might involve "triaging my agent inbox," as even with delegation to AI agents, there will still be a need for human oversight, permission-granting, and instruction-giving – assuming we avoid "AI takeoff problems" and maintain alignment and control.

"Even the more I delegate, at some point if we don't have some AI takeoff problem, then it is aligned and it's controlled, it's coming back to me, it's notifying me, hopefully better quality notifications than the ones we have today, and getting permissions, getting instructions, helping me with what I want."

Satya envisions a UI layer that gives humans "massive, massive leverage" through two metaphors:

1. In consumer life: "A friend, a coach, an advisor"
2. At work: "A chief of staff, a researcher, a consultant... working with me on everything I do"

He makes an important distinction between "knowledge work" and "knowledge worker," predicting that while knowledge workers will still exist, the nature of their work will change:

"I think we conflate knowledge work and knowledge worker. I think tomorrow there will be a knowledge worker, except the knowledge work they'll be doing will be at a different level of abstraction."

This evolution parallels how Word, Excel, and PowerPoint were tools for knowledge workers in the 90s, with Copilots being version 1.0 of new tools, "Copilot plus agent" as version 2.0, and something yet to be fully contemplated as version 3.0.

When Aditya asks about the interface for this agentic future, noting that current chatbots and copilots feel unimaginative, Satya highlights multimodal interfaces, particularly voice:

"One of my favorite things to do is this Copilot voice interface... I've even set it up to say my action button on the iPhone with CarPlay. Like even take podcasts - the best way for me to consume podcasts is not to actually go listen to it, but to have a conversation with the transcript on my commute using my Copilot."

Satya emphasizes that the ability to have a "full duplex conversation" - speaking to AI, interrupting it, and having it respond naturally - represents a breakthrough modality from which "there's no going back," comparing it to how autocomplete text suggestions became permanently integrated into our digital experience.

Timestamp: [36:36-40:35]

• AI development is occurring in three parallel tracks: knowledge workers/language models, real-world action models, and models for scientific domains like chemistry and biology
• Current AI infrastructure is not optimized from first principles; there's an opportunity to rethink the entire system architecture for AI workloads
• No one has yet developed a truly distributed model architecture that can effectively split processing between edge devices and the cloud at runtime
• Current AI systems like Copilot lack the equivalent of a "stored program" moment - they need more robust architectures, memory systems, and multimodal capabilities
• In the future, humans may shift from directly performing tasks to managing AI agents - "triaging an agent inbox" rather than doing all the work themselves
• The relationship with AI will likely evolve into dual roles: as "friend, coach, advisor" in personal life and "chief of staff, researcher, consultant" in professional contexts
• Knowledge workers will still exist, but their work will operate at a higher level of abstraction, as AI handles more routine cognitive tasks
• Multimodal interfaces, particularly voice-based ones that enable natural, interruptible conversations, represent a transformative interface paradigm
• Current AI tools (Copilot as "version 1.0") will evolve to "Copilot plus agent" (version 2.0) and eventually to something more sophisticated (version 3.0)
• The ability to converse with AI about content (like having a conversation with a podcast transcript) creates new possibilities for information consumption and interaction

Timestamp: [31:11-40:35]

Aditya brings up Microsoft's recent quantum computing announcements, curious about whether this technology is ready for the startup ecosystem or if it's still in very early stages.

Satya frames Microsoft's quantum efforts as a testament to the company's ability to persist with long-term investments:

"For us, this speaks back to a little bit of our ability to go persist for a long time. I'm like the third CEO who's been funding quantum over the last... we've been at it for 20 years, 20 plus years."

The quantum computing journey at Microsoft began with a clear goal: to build a "real utility scale quantum computer." This required stable qubits, which in turn needed a physics breakthrough. Microsoft's team chose to pursue Majorana particles, theorized in the 1930s but never proven to exist.

"We want to build a real utility scale quantum computer, and in order to build utility scale quantum computers, you kind of need stable qubits, and in order to build stable qubits, you need to have like a physics breakthrough."

After two decades of persistent research, Microsoft has not only proven the existence of these Majorana particles but also developed the ability to fabricate them atom by atom, demonstrating that "these Majorana zero states can in fact hide quantum information in a robust way." This breakthrough means fewer error correction needs, enabling scaling into real chips.

"20 years later, we now have proven the existence of it, and not only that, we've been able to fabricate those atom at a time to be able to show that these Majorana zero states can in fact hide quantum information in a robust way."

This progress has made it easier for Satya to fund quantum research in 2023 than it was in 2014, highlighting how persistence through multiple starts and challenges has paid off.

Satya challenges the common perception that quantum computing will replace classical computing. Instead, he envisions a complementary future:

"I think quantum as somehow replacing classical... I actually think classical plus quantum is a very powerful [combination]."

He explains that quantum computers excel at exploring data spaces but aren't ideal for data-heavy workloads. For tasks like atom-by-atom construction, a quantum computer would be much faster due to its superior ability to simulate nature, but for other tasks, classical computing remains more efficient.

Satya envisions quantum computers generating labeled data to train AI models that then run on traditional high-performance computing systems. This coexistence of quantum and classical computing creates opportunities, particularly in science, chemistry, and biology fields.

"Even with 10 logical qubits, 100 logical qubits, you could use things out of it in order to be able to build better chemistry models, better biology models."

He notes encouraging market signals, such as biopharmaceutical companies going public with quantum-related initiatives, indicating growing investment in this "leading edge deep science."

Timestamp: [40:41-44:49]

• Microsoft has been investing in quantum computing for over 20 years, demonstrating the company's willingness to persist with long-term, ambitious research goals
• The company's quantum strategy has focused on building "real utility scale quantum computers" by pursuing stable qubits through Majorana particles
• After two decades, Microsoft has proven the existence of Majorana particles and can fabricate them atom by atom, demonstrating their ability to hide quantum information in a robust way
• This breakthrough reduces error correction needs, making quantum computing more scalable and practical
• Quantum computing should be viewed as complementary to classical computing rather than a replacement - "classical plus quantum is a very powerful combination"
• Quantum computers excel at exploring data spaces and simulating nature but aren't ideal for data-heavy workloads
• A hybrid approach where quantum computers generate labeled data to train AI models that run on classical systems shows promise
• Even with limited qubits (10-100 logical qubits), there are already practical applications in chemistry and biology
• The quantum computing market is showing positive signals, with biopharmaceutical companies going public with quantum initiatives
• Long-term persistence in deep tech research can eventually yield breakthroughs that transform seemingly theoretical concepts into practical realities

Timestamp: [40:41-44:49]

People:

• Bill Gates - Microsoft co-founder mentioned in contexts of Microsoft's mission, natural language obsession, and early quantum computing investment
• Paul Allen - Microsoft co-founder mentioned alongside Bill when discussing the "software factory" concept
• Steve Ballmer - Former Microsoft CEO credited with supporting cloud investments and teaching Satya valuable CEO leadership lessons
• Craig Mundy - Mentioned as working with Bill Gates on early quantum computing strategy at Microsoft
• Carol Dweck - Author whose work on "growth mindset" influenced Microsoft's cultural transformation under Satya
• Sam Altman - OpenAI CEO, mentioned in the context of Microsoft's partnership with OpenAI
• Ilya Sutskever - OpenAI co-founder, mentioned regarding scaling laws in AI
• Greg Brockman - OpenAI co-founder, mentioned regarding scaling laws in AI
• Dario - Author of the scaling laws paper that influenced Satya's investment decision in OpenAI
• Reed Hoffman - Credited for the "refounding" language that Satya uses to describe his approach as CEO

Companies/Organizations:

• Sun Microsystems - Where Satya worked before joining Microsoft, described as a "stunning" full-stack systems company
• Facebook - Mentioned regarding Microsoft's $240 million investment in 2007
• OpenAI - AI research lab and product company that has a strategic partnership with Microsoft
• Mistral - AI company mentioned as a Microsoft investment alongside OpenAI
• DeepSeek - Open source AI company praised for making AI more efficient on existing infrastructure
• SAP - Enterprise software company mentioned as an early partnership where Microsoft built SQL to work underneath SAP

Concepts:

• Software Factory - The founding concept of Microsoft as described by Bill Gates and Paul Allen
• Growth Mindset - Carol Dweck's concept that became central to Microsoft's cultural transformation under Satya
• Refounding - The concept of revisiting and revitalizing a company's mission and culture while adapting to new realities
• Scaling Laws - AI research insight suggesting that model performance improves predictably with more compute and data
• Mayorana Particles - Quantum physics concept theorized in the 1930s that Microsoft has been researching for quantum computing
• Complete Thought - Steve Ballmer's concept about thoroughly thinking through all aspects of a product or strategy

Timestamp: [01:03-44:49]

In a brief philosophical detour, Aditya asks Satya about the Multiverse Theory, which suggests that quantum events indicate the existence of many parallel universes.

Satya responds with a thoughtful but cautious take, revealing his preference for a different interpretation of quantum mechanics:

"The Copenhagen consensus is a little more me than the Multiverse, but it's kind of like, man, let the physicists duke it out. Let me stay out of it. It's like I'm just going to build a software factory."

When pressed about the philosopher in him, Satya offers a concise statement that aligns with the observer-dependent nature of quantum mechanics in the Copenhagen interpretation:

"I exist, so there must be something I've observed, and let me leave at that that is measured. I think that's a reasonable place to be."

This exchange reveals Satya's familiarity with different quantum interpretations while showing his practical orientation toward building technology rather than debating theoretical physics. His reference to the Copenhagen consensus reflects the interpretation that quantum systems don't have definite properties prior to measurement - a view that emphasizes the role of observation in determining reality.

Timestamp: [44:55-45:31]

Aditya mentions that Satya is an incredible father who is close to his family, then expresses his concern as a parent of three young children about how different the world will look when they enter the workforce in about 10 years. With the possibility of AGI or similar transformative technologies, he asks what advice Satya would give about raising children for that future.

Satya approaches this question by first framing it around the concept of agency:

"What does agency look like going forward, I think, is the real question. I think we're all grappling with - does this increase human agency? Does it somehow take away because we're subject to someone else's decisions?"

He shares two profound AI demonstrations that shaped his thinking about human agency. The first was seeing early versions of GitHub Copilot and realizing "this thing works," noting how difficult it was to convince software developers of its usefulness.

The second, more impactful demonstration occurred in India in early 2022, where someone had created a WhatsApp bot by combining GPT-3.5 with India's digital public goods infrastructure:

"I saw someone showed me a demo they had put together by basically daisy-chaining I think at that time maybe GPT-3.5 with sort of India's unbelievable stack of digital public goods... A rural Indian farmer was able to just go ask the bot to say 'Hey, find out about this subsidy program I heard about on television.' And it comes back and says 'Go to this website, fill out these forms, and you should do it.' And then he says 'I don't know what a form is, and I don't know what a website is. Do it for me.'"

This experience revealed to Satya how technology could dramatically increase agency for someone who previously lacked access to digital resources. It helped him reframe the question about the next generation:

"That is another way to think about our children... What would they do if they really have with them now the ability to code anything they want, research anything they want, build anything they want?"

This shift in capabilities may change what we value in education:

"Would we put premium a lot more on curiosity than expertise? I think that's kind of perhaps one of the more interesting things."

Satya shares a personal anecdote about failing to get into the Indian Institutes of Technology (IIT) while Aditya and Ruchi succeeded, using this to illustrate how traditional measures of success might become less relevant:

"It doesn't matter, one exam, right? Go and think about - how would you be able to give confidence to every kid out there that they can go explore anything?"

He concludes with his core advice for raising children in an AI-transformed world:

"The core curiosity and critical thinking are still going to be perhaps at a premium, and somewhere coupled in it is probably confidence."

Aditya responds enthusiastically to this answer, adding that he wants to impart to his children that "these computers and this intelligence works for you - what can you make with it?" - emphasizing the empowering nature of the relationship between humans and AI.

Timestamp: [45:36-50:12]

Noting that Satya is clearly competitive and that Microsoft has a competitive and winning culture, Aditya asks how Satya channels that competitive spirit outside of work, specifically mentioning his cricket team.

Satya acknowledges his passion for cricket with a light-hearted reference to that day's match, which went favorably:

"I was happy to see today's morning match, but I'm obsessed, as I think every other South Asian is, with cricket."

Moving beyond sports, Satya reflects on how his perspective on competition has evolved:

"The interesting thing that I've been thinking a lot about... at least at this stage of my life... is I want to make sure that I'm thinking about the game that is being played versus playing the game, and being a lot more deliberate about it."

He observes that founders naturally do this as they define their companies and decide "what's the game that I want to play." But Satya emphasizes that competition is about "challenging yourself first to be in the right competitive set" - a particularly important consideration in the tech industry.

Satya then shares a personal reflection on how fortunate he's been with the timing of his career:

"Here's my story: I came to the United States in '88, the Berlin Wall fell in '89, I entered the tech industry in 1990, and man, what a ride. It's an unbelievable time. And so the question is - and that's just luck, right?"

This luck of timing led him to an insight about the nature of meaningful competition:

"What is the setup and the competitive set? That is the most important thing, is what I've realized. Which is, you want to be in the sector, in a space, with competitors who are awesome. In fact, that is better than being someplace where you may be a winner but you're not really."

Satya concludes by reiterating his focus on "thinking about the game, the game that is being played, or playing the game versus the game you want to play," adding with genuine appreciation:

"What a time to be alive... to be in the arena of like the greatest game, I think, of our time. It's a gift."

Timestamp: [50:12-52:37]

• On quantum interpretations, Satya leans toward the Copenhagen consensus rather than the Multiverse Theory, but maintains a pragmatic focus on building technology rather than debating theoretical physics
• The central question for the next generation is whether AI increases or decreases human agency
• AI can dramatically expand agency for those who previously lacked access to digital resources, as demonstrated by a rural Indian farmer using a WhatsApp bot to access government services
• For raising children in an AI-enabled world, parents should focus on cultivating curiosity, critical thinking, and confidence rather than traditional expertise
• Traditional educational metrics and credentials may become less relevant as AI democratizes access to knowledge and capabilities
• Children should be taught that AI and computing work for them - emphasizing their agency as creators rather than passive consumers of technology
• Competition is most meaningful when you deliberately choose to be in the right "competitive set" with worthy competitors
• Being in a challenging space with "awesome" competitors is better than being a winner in an unimportant arena
• Success in technology careers often involves an element of luck in timing - Satya considers himself fortunate to have entered the tech industry at a transformative moment
• Thoughtful competitors focus on "the game being played" and make deliberate choices about which game they want to play, rather than simply playing whatever game is in front of them

Timestamp: [44:55-52:37]

Podcast Information:

• South Park Commons' "Minus One" podcast focuses on the "minus one to zero phase" where entrepreneurs build conviction about what they want to work on for the next 5-10 years
• Hosted by Aditya Agarwal and Ruchi Sanghvi, Partners at South Park Commons

Where to Find the Podcast:

• Available on all major podcast platforms
• Social media: @SouthParkCommons

Sponsorship:

• Thanks to Atomic Growth for their support in bringing this episode to life

Timestamp: [52:37-53:02]