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Made To DO
Most transformations fail for the same reason AI is going wrong - we keep treating people as the cost of change instead of the source of it.
I've spent fifteen years working with thousands of employees of Fortune 500 companies and over 50,000 entrepreneurs across 110 countries trying to make change happen. And the one thing I know for certain: the most important conversations are not happening in the boardrooms or the tech labs. They are happening with the people actually doing the work.
This show is where those conversations go public. Every episode, I sit down with a global leader working on the most pressing challenges of our time.
I'm Florian Hoffmann, founder of The DO School. This is Made to DO.
Made To DO
Governance is the Accelerator: Navrina Singh on making AI safe and human-centered.
Use Left/Right to seek, Home/End to jump to start or end. Hold shift to jump forward or backward.
When Navrina Singh founded Credo AI six and a half years ago, "AI governance" wasn't a category. It wasn't a buzzword. It barely existed as a conversation. Today, it's one of the most urgent topics on the agenda of every board, every government, and every regulator trying to keep pace with a technology that reinvents itself every few months.
In this episode of Made to DO, Florian Hoffmann sits down with Navrina — founder and CEO of Credo AI, advisor to the White House, OECD, and UN on AI policy, and one of Time's 100 Most Influential People in AI — to talk about what it actually takes to make AI safe, compliant, and human-centered. Not in the abstract. In the messy, high-stakes reality of Fortune 500 companies deploying third-party models, regulators racing to write rules for systems that change faster than legislation can move, and leaders who know they need to act but don't know where to start.
What emerges is a conversation that refuses to pick a side between innovation and caution. Navrina's core thesis isn't about what could go wrong with AI — it's about what would go right when we get this technology right. That optimism, paired with a deeply practical understanding of how AI systems are built, deployed, and governed, makes this episode less of a debate and more of a playbook.
Key takeaways
- AI governance is an enabler, not a hindrance. Companies that build a foundation of risk and compliance adopt AI faster, not slower — because they can evaluate, procure, and deploy new systems with eyes wide open.
- Context matters more than capability. The guardrails for a marketing flyer generator and a claims-processing system should be drastically different. Governance has to happen at the use-case level, not the model level.
- Leaders need to go back to building roots. You can't govern what you don't understand. Navrina's advice to CEOs and board members: build an AI system yourself before you try to set policy for one.
- AI literacy is the bridge between public and private sector. Policymakers who have never used an LLM and builders who have never navigated a regulatory regime both need to meet in the middle. Adaptive policymaking — flexible enough to evolve with the technology — is the way forward.
- The 10–15 age window is critical for girls in tech. Navrina encourages her 12-year-old to use AI tools freely (social media is off-limits). The confidence gained from building at this age shapes whether women show up at the tables where AI decisions get made.
Video chapters
0:00 — Opening
0:45 — Meet Navrina Singh
2:10 — What Is AI Governance?
5:06 — Governance Across the AI Lifecycle
6:07 — Building a New Category
7:26 — Keeping Up with Rapidly Evolving Models
9:13 — Bridging Tech and Policy
13:04 — What Separates Good AI Leadership from Bad
15:36 — Existential Risk: Responding to Harari
17:13 — Governing AGI
18:11 — Who Should Govern AI?
21:54 — AI Literacy and Adaptive Policymaking
24:25 — First Steps for Leaders
27:23 — Raising Girls in the AI Age
30:37 — Women in AI: Funding, Investing, Celebrating
33:20 — Final Thoughts: More Human Connections
I would say the core thesis for us was not really what could go wrong with AI, but the core thesis was what would go right when we actually get this technology right. And I think that's what's really inspired our mission of making sure AI is in service of humanity.
SPEAKER_01Welcome to Made to Doom, the podcast exploring the ways people can still be the most important part of the transformation of our economies and society. Every episode I sit down with a global leader to uncover human-centered transformation insights for all of us who are navigating a positive, feasible future in the age of AI, climate, global fragmentation, and societal change. I'm your host, Florian Hoffman. Listen on to find out more. Let's get right to it. Who and how do we make sure that AI is being controlled by people? As AI agents start taking real-world actions rather than just generating outputs, who's responsible when something goes wrong or a company is being hacked by agents? Legal and governance analysts are increasingly framing this as a live debate over whether AI agents should be treated as legal actors bearing duties or something closer to legal persons. My next guest has spent her career trying to make sure AI doesn't run ahead of accountability. Narina Sing is the founder and CEO of Credo AI, which she built after nearly two decades leading AI and enterprise products at Microsoft and Qualcomm. She advises the White House, the OECD, and the UN on AI policy. And last year, Time named her one of the 100 most influential people in AI. What distinguishes Navrina is not only that she's an absolutely inspirational and kick-ass leader, she also lived on both sides of the table, inside big companies, actually shipping AI, and inside the rooms where the rules for AI get written. Back when Navrina started her governance business six years ago, this category didn't even exist. Today it's one of the most discussed topics. Here's my conversation with Nareena Singh. Enjoy.
SPEAKER_02My dear Nadri, it's amazing to have you with us. Thanks so much for um taking the time and having blown away all the young entrepreneurs down there. Um that was fantastic. As an incredibly inspiring entrepreneur, you've built a category, AI governance. What is that?
unknownYeah.
SPEAKER_00Well, thank you so much for having me, Florian. You know, uh startups are hard, and creating a new category is even harder. So AI governance is essentially a set of processes and requirements that are required to make sure that the AI that you're either buying or building is compliant, it is safe, it's reliable, and more importantly, it's human-centered. So imagine, like you're a Fortune 500 company and you're buying all the latest and greatest AI tooling that's available right now in the world, whether it is from foundation model providers or from open source ecosystem, how do you ensure that as you're bringing that within your enterprise to use it for different applications, that it actually meets the requirements that are important for your business? And similarly, if you are one of the enterprises or a company building customer-facing applications, you really want these systems to be trusted by the end users. You want these systems to be compliant to regulations, you want these systems to actually always work reliably in a secure manner. So that's what AI governance product and platform enables.
SPEAKER_02So break that down for me as a non-techie. So this means I'm buying, I don't know, Copilish or my Anthropic Business Cloud solution, and then I'm buying your software on top to check on what Cloud is doing or what Microsoft is doing, or how does it actually work?
SPEAKER_00Yeah, great question. So imagine that you've already uh bought Enthropic and you're using Cloud to build a customer-facing system, and that customer-facing chat bot, for example, answers everything reliably about your business. So you'll be using Credo AI, which enables you to do use case-level governance. What that means is at the point of use of Claude system for this customer service, we will look at your customer service agent and say, within what is important for your business. For example, for your business, it might be really important that this customer service chat bot is not toxic, that it serves all the different demographics in the right fair way, that the language is very aligned with your brand voice. So Credo AI basically will take all that business intent, convert that into code. It's called governance as code, and then use that code at runtime to make sure that your conversational agent that is built on Claude actually ends up doing the things it's supposed to do.
SPEAKER_02And if the agent doesn't, is it being shut down? Is it being corrected by Credo AI's code? Or how is the intervention?
SPEAKER_00Yeah, great question. So the intervention happens throughout the AI lifecycle. So you assume that you're in the design and development phase. At the design and development phase, obviously there are checks and balances. The minute you go out of the risk threshold or compliance threshold, our system will alert you're now risky or you're not compliant with a certain regulation or standard. Now you take that system and you put it in production. So in production, we will connect into your monitoring platforms, whichever platform you're using, and we'll be taking the signals back from that monitoring platform to check whether your system is still risk managed or compliance managed. And the minute it goes out of those boundaries, Credo AI actually creates an incident. And based on that incident, it will provide you prescription on how you need to reduce or mitigate that risk.
SPEAKER_02And so when you started out, you've been doing this for a while, right? Seven years. That wasn't really a topic. Now it's on everybody's mind. Does that mean we don't need to worry about AI and where it's going?
SPEAKER_00Well, uh, we are working hard to solve that problem. So I started the company almost six and a half years ago, created the AI governance category. I would say the core thesis for us was not really, you know, what could go wrong with AI, but the core thesis was what would go right when we actually get this technology right. And I think that's what's really inspired our mission of making sure AI is in service of humanity. The product that we've built, the capabilities that we've built, the four-deployed advisory function that we've built to really up-level enterprises around AI governance, I think we are well on the way to make sure that you can adopt third-party AI systems, but also build your own AI systems with the highest level of safety and trust. And so we are very well, uh, I would say, ahead on making that mission come to life. But still, with the AI capabilities on the rise, there's so much more work for us to do each day.
SPEAKER_02And is it actually possible if you imagine that you know all the big LLMs are putting out a new model every two, three months that is shifting significantly what it can do? How does it work? Do you have to then build a new model every time with it? Or are you able to still capture a new model as well?
SPEAKER_00Yeah.
SPEAKER_02What's the risk potential then there?
SPEAKER_00Great question. So this is where we actually work, uh, we have a very unique place in the ecosystem. We not only work with on one side the AI builders, the foundation model providers, the enterprises, but on the other side, we work with the policymakers, regulators, standard setting bodies, independent researchers. And the reason that role is really important is to your point, we can't figure out all the risks within all the emerging AI systems. So we work with all the ecosystem providers, whether it is the MIT risk repository, whether it is OWASP, whether it is MITRE, to really understand what are the known risks. So things like fairness or security known threats, right? What are the unknown unknown risks? These could be emergent threats like psychopincy, which we don't even know what could happen. Or what are the even more emergent threats, especially when you have multi-agent systems where you could, you know, result in data poisoning or agent poisoning. So we actually monitor risks from known inherent risks, proliferation risk, which is when you actually are using a foundation model or an open source model, and that risk proliferates into your application. What do you do about it? And then last is emergent threats. And many a times for emergent threats, we might not have mitigations at that time, but we can alert the enterprises to these new kinds of risks. So I would say it's every day the finish line of this marathon keeps extending because as the capabilities increase, new threats are emerging. So we have to be a great ecosystem partner to figure out what mitigations or at least awareness we can bring to these risks.
SPEAKER_02And ultimately the question is so big that it goes beyond any one single company, right? And I know that you're also you've been advising now multiple White House administrations and uh are in touch with lots of different governments. So one of the the feedback that I hear quite a lot between uh private sector leaders but also people in in office is that obviously government is uh working much slower than this incredible speed that AI is now pushing. And on the one hand, everybody sort of trying to achieve supremacy over the other models and also uh sort of uh chasing the returns. So, how do you look at that dynamic and what do you think right now are the big biggest risks in that, just societally speaking?
SPEAKER_00Yeah, great question. So, you know, one of the things I think that's getting missed in this AI conversation is it's not just about the model. It's actually what are you trying to serve? And we call it the context or the use case, right? So the kind of guardrails that you might have for a system that is helping you make marketing flyers versus a system that is helping you with claim processing, the guardrails are going to be drastically different. So, our role in the ecosystem here, and this is one of the reasons we work very effectively and close with policymakers and standard setting bodies like NIST, like ISO, is there are guardrails that are coming from this amazing body of research, policy, foundation model providers that you want to see can these actually be codified and used within the context that primarily enterprises care about because we serve Fortune 500s. So we are basically the operationalizing layer that might take a policy. So, for example, the EU AI Act, we'll take EUAI Act, we'll codify that in Creed OAI as governance as code and check for the enterprises that are using Creed OAI to get compliance to the EU AI Act. Does our software actually meet their needs? Or imagine you are trying to uh build a very good risk management posture aligned with ISO 40 2001. So we will take ISO 40 2001 and we'll work with ISO to really make sure that the codification and then the implementation actually aligns with not only their objectives, but is actually getting operationalized within an enterprise. And so, as you can imagine, for us, you know, this role of a translator becomes really critical, where we are translating what the AI builders are building as the next level capabilities and then what actually can be put in place as a standard or potentially a regulatory regime that actually makes sense for these enterprises. And that role, as you can imagine, needs to be able to speak tech and it needs to be able to speak policy. And having said all that, I think one of the key things that I keep bringing back to everyone is model capabilities are going to increase, but it always depends upon what is that context. And do you have the right guardrails for that context? Do you have the right benchmarks and evaluation created for it? And more importantly, once you've done the testing and evaluation, once you've looked at that use case, can you actually create the right governance artifacts, which can be a transparency report? It could be a risk report, it could be a compliance report, it could be a disclosure, so that you can show that within the context of what you know as a business, you've done everything to be risk managed and compliance managed.
SPEAKER_02And if you go a little bit for a moment beyond the compliance and risk management lens, and you're dealing with so many uh CEOs and board members, um, all of them are trying first now to create adoption with AI internally. And then the next step is how do I get outline and create innovation for the customer? What distinguishes a leadership team that does the integration well, including the risk mitigation and the compliance piece from a CEO leadership team that is doing AI transformation for Lit?
SPEAKER_00I think the biggest thing that I see uh falls in two categories. One is they are seeing AI governance as an enabler versus a hindrance. So if you talk to a board or uh, you know, uh the executive leadership of a company that just has this mindset that I don't want to build a foundation of risk and compliance because it's going to slow my AI innovation down, you should already count them out. Because what we are finding is when you actually have the foundation of governance, you can adopt AI faster with eyes wide open. Like, is this new system from this XYZ vendor, does it meet my needs? Should I be procuring that? So we are seeing faster adoption of third-party AI, but we are also seeing faster development of your AI systems because once you know potentially what could go wrong, and if it doesn't meet your consumers' need, you can actually make changes much more effectively. And also, lastly, we are seeing that the companies that actually uh embrace AI governance, they are able, because they're able to bring in more AI, the productivity increase among their employees is so much more, uh, I would say at a higher level than a company that did not. Because now you're enabling your employees to get access to this very powerful technology with eyes wide open again, with the governance and the oversight, much more faster. So, one thing, as I mentioned, is really using AI governance as an enabler rather than a hindrance. And then the second distinguishing factor is AI literacy, where the enterprises and the leadership that are actually investing in AI literacy, not just for their employees, not just for their board members, but also for their customers, actually are winning. Because what you are doing in that case is you're um, you know, I'm a big believer that if you can build with AI, you can actually govern AI better. And if you govern AI, you should know how to build AI, right? And I think we are seeing that the more you can provide AI literacy to all these stakeholders, your employees, to your consumers, to your board members, the better informed you are about all the new technological innovations that are coming your way.
SPEAKER_02And if you now from from that perspective of being so steeped into the risk question most of your day, you know, if you listen to, I don't know, like a historian like Iwal Harari, uh, who talks about the end is coming and we're giving out our own agency to a machine, how how do you look at these types of narratives uh around sort of us basically taking a significant step into the wrong direction as humankind?
SPEAKER_00You know, so this is um I'm a I'm a big proponent of agency, but with right oversight and governance. And that's one of the reasons I created Credo AI. And when you think about the spectrum of, you know, folks who are very worried about existential risk to the folks who are just so worried about maybe some of the more known risk, we somehow like fall right in the middle because we want to be the solution providers. We we know how powerful this AI technology is going to be, not just for enterprises, not just for SMBs, not just for startups, but for humankind. And we really want to get this technology right. So most of the time that we are spending is actually not in thinking about risk, but we are really thinking about how do we get this technology right by managing risk, by making sure it's compliant, by making sure it's human-centered. So, you know, do I worry about a Skynet scenario? Right now, no, because I think a lot is still in our control. However, this is why governance and investment in governance matters right now. Because if we do not have that investment, I think we'll have a very different conversation and we reach a fork in the road.
SPEAKER_02What about the scenario of AGI? Like uh if you have general intelligence, how would uh an oversight uh over something like that look like?
SPEAKER_00I think we are in very early innings of figuring out what um governance for AGI might look like. But this is exactly the reason why we should be, you know, thinking about all the different scenarios. That's why we should be investing more in AI governance, why we should be investing more in AI safety and trust ecosystem as well as startups, uh, because I think we just don't know so much, um, especially on the, you know, we're already on this exponential in this technology. And I think uh, you know, we at least, you know, we do get access to the frontier more often than the others. And we know that we are much nearer to some of these scenarios around AGI uh on the frontier. So the key question for us is now we should be investing equally in oversight and governance so that we can control it.
SPEAKER_02And who would be ultimately the actor that would make you feel the safest? Is it like should governments basically prescribe this? Uh are we giving this as self-government to businesses? How do we from from a societal point of view, how would we best navigate this or manage this?
SPEAKER_00You know, it's uh I this this is such a great question. I've thought a lot about it and I've looked at some historical context. Like if you think about even the creation of New York Stock Exchange, New York Stock Exchange didn't even come to being maybe two here, 200 years since like the first time trading started. Similarly, FAA didn't come to being like 30 years since the first flight took off. When you're when the technology is advancing so fast, you have to sometimes let the innovation play out, and the people who are building that technological wave need to also guide it in the right direction. And I think we are in right now in that place where the a lot of what these LLMs are able to do and what these systems or agent tech systems are able to do, unless you've built those systems and use those systems, you really don't know what good governance cardrails you can put in place, let alone what policy and what regime you can create. And so that's why you're seeing a lot of private sector coming together to create the right assessment mechanisms.
SPEAKER_02So a senior bureaucrat in some capital somewhere around the world just doesn't have the tools or understanding to actually To be able to effectively govern these systems, yes.
SPEAKER_00And that's why educating the other side becomes so critical. And we are big believers in AI literacy, because over time, um, you know, I think the best way that we can make sure that these systems actually work for humanity is the private and public sector has to come together. Uh, you know, there was a recent um uh very interesting call to action by Demis uh from Google really talking about a FINRA-style um agency that can provide oversight, especially to the frontier class models. And I think we are going to see something like that uh, you know, come to fruition, especially for frontier class, where things or systems that are so powerful and so capable that potentially are important for us to watch out from US competitiveness or from national security risks, you will need this public-private sector coming together and doing evaluation and testing and making decisions around it. But the key question is how does that sort of foray into enterprise class models which are being used in healthcare and insurance? Do you need an agency like that? I think those are exactly the conversations we are having right now in terms of regulatory regime as well as um uh thinking through the right guardrails. Again, context matters so much.
SPEAKER_02And it's a little bit going against the flavor of the day, which is so much around, you know, fragmentation, uh deglobalization, and regional focal points of many governments and and many businesses, right? So in a world that is fragmenting much more, suddenly to say, okay, we all need to come together across the public and the private sector in order to navigate this effectively. Um now thinking a bit about the leadership side of that, what kind of leadership skills or approaches are you using or have you seen working in order to actually advocate for such a unified approach effectively or successfully?
SPEAKER_00Um I that's such a again a good question. I think I I keep Going back to and might might sound like a broken record, but I think the thing that's going to bring the leaders together from both sides is going to be AI literacy. You have to get on the same level, and to be able to get on the same level, you have to be able to communicate.
SPEAKER_02Can I then ask yourself if you if you say AI literacy? So let's imagine I don't know, I'm I'm living in Little Right now. I'm thinking of uh a government minister or a CEO of your country company, what do they need to what do they need to learn? Yeah in order to be able to then understand what you want them to do in coming together.
SPEAKER_00Yeah, I I think a couple of things they need to be able to learn is how are these systems actually work, how are these systems actually being built, how are these systems uh getting trained on your data, how are these systems actually being used in specific contexts, whether it's healthcare or whether it is uh CPG and retail. Uh, you also need to understand how are these systems being governed by a particular either provider or deployer. Um, and I think a lot of those questions uh are getting asked right now. So I'm part of a lot of public-private um, you know, communities where these conversations are actively happening, where policymakers are bringing in learnings from the past regimes, whereas the AI builders are bringing learnings from what's really happening on the ground. And I think we are seeing a very interesting exchange of ideas. The challenge that happens is when, let's say, you're a policymaker and you have never even built an agentic system or you haven't even used one of the LLMs. And the other problem that happens is when you as a builder assume that a particular policy regime might be very easy to pass at federal, state, local, or global level. I think that's when the friction points really happen. And given the pace of technological development in AI, I think there are a lot more unknown unknowns. So something that we have been very actively, I would say, uh, behind and promoting for is adaptive policymaking, which is, you know, you can't put an institute in place a set of rules and regulations uh for next 10 years when this technology is changing so quickly. You need to have flexibility to be able to adapt it based on the technological change. So that's a pretty big focus area for us right now.
SPEAKER_02With all that, um that being said, um if uh if a senior leader now comes to you and says, Whoo, um this all seems like a pretty pretty pretty big mountain to climb. And I'm worried about that. Um, but I do want to um sort of get involved and do something about it. What would be sort of the first steps that you would recommend that person to take?
SPEAKER_00Um, first and foremost, it is a pretty big mountain to climb. And you have to do the work, uh, especially if you're not coming from, you know, the AI background or technological background or even like policy background, if you don't have the the key key things. I think uh when I talk about um, you know, uh let's assume that this is an enterprise leader. This enterprise leader, uh, if they are adopting AI within their company, again, you have to be able to build with AI and you have to know the basics of AI.
SPEAKER_02Which is usually not what happens, right? Because usually I buy an AI strategy from a consulting career, then I do a tender for tech implementation from one of the, you know, and then I uh sort of get somebody to do change for my people at the very end because I'm realizing that they're uh scared uh of losing their jobs, right? Yeah. So that's the that's probably the status quo of what most leaders are doing.
SPEAKER_00Yeah, and I think that needs to be disrupted because you uh to become an effective operator, an executive or a board member, I think you have to go back to your building roots. You have to become an AI builder right now. Uh, you have to become an AI user right now. It's only when you build uh and use do you understand the power of this technology, but you also understand the challenges that this technology poses. And once you become, I'm not expecting everyone to become power users, but at least when you have that fundamental understanding of how this technology, this technology is so much different than traditional software, that's when the aha moment happens. And that's when you start to understand why AI literacy for your organization is important, why investing in AI governance becomes paramount, why changing your board structure where you actually similar to an audit committee, and now you have an AI governance committee, becomes really critical. Why you need to also have, uh, especially if you are in some of the critical areas, a very important government relations uh function and a private-public partnership in place so that you can actually exchange ideas, all those things become very clear only when you actually start becoming going back to your building roots. So I'm a big proponent of, you know, I speak to a lot of public company and private company board members. It's like go and build an AI system. And once you understand the power and the challenges, all everything else will unfold.
SPEAKER_02Before we come to an end, I want to make a uh a big shift. We're both parents, uh part parents of uh preteen girls. Um and we talked a bit before about you know the um the role of women in building AI, also that there's pretty much a skewed picture towards men, right, when it comes to who gets the investment, but also who's calling the shots. Um how do you think about the skills and the capabilities that our girls need to learn? And how do we um support them and enable them and give them the the courage and the confidence to figure stuff out that is developing so quickly? Like at home, do you have like a nowhere policy, no tech policy? Um, and over the coming years, how do you think you'll approach that?
SPEAKER_00So um I would say that I have an all-tech policy, and especially for AI, it's the mom as a guardrail policy. So um I actually actively encourage uh my 12-year-old to use as many AI tools as possible to assist her with her creative endeavors or even with her school in you know work. Uh, where we do put the limit is on social media. So she is not allowed on social media at all uh right now. And the thing that I have found is this age from you know seven to fifteen is so critical for girls that you have to really enable them to jump into tech at this age. Um, because otherwise uh I have noticed that there is, there becomes like this inherent fear of tech. And I think that prevents them from actually showing up on the right tables. So this is such a critical age, the 10 to 15 year old, that encouraging them to use AI tools, encouraging them to really, you know, play around and build with AI, whether it's a music video, they don't all have to code, but build new things with AI, whatever their passions are, is really critical. Um, the second thing is I think it goes back to uh the more you build, the more confidence you gain, and the more you're able to actually speak your ideas much more loudly. So I'm also a pretty big believer in enabling their verbal and communicate, you know, verbal and written communication skills. Investing in that is really critical because you can build the greatest thing, but if you can't share your ideas uh and influence people, I think that's a pretty big miss as well. And then the third thing, I a big believer in let her make her own mistakes. Uh, because that's from those mistakes comes the confidence that, you know what, it's another step in my learning journey. And the earlier we start um letting them make mistakes and making big mistakes, I think the better I will I believe that they're going to show up as entrepreneurs. Um, I think a lot of uh what I would like to see is more women participating in the AI economy, whether it is as entrepreneurs, whether it is as investors, whether it is as AI enterprise leaders or AI policy leaders. And right now we actually have a dire need of more women represented on these tables.
SPEAKER_02So, what do we need to do to um accomplish that? Um, our Geralds will need a bit more tire.
SPEAKER_00Uh yes, and they'll need role models. So, first and foremost, invest in more female-owned AI businesses. I think we hold women to very different standards than we all uh, you know, uh hold men to. And there's actually a Harvard study that proves it, which is women entrepreneurs are uh have to show more outcomes. And men entrepreneurs most of the times are really based uh judged on their vision. And I think when when you're held to different standards, you're going to actually end up squashing a lot of, I would say, female entrepreneurship. So I would love to see more women uh, you know, own businesses getting funded and especially in AI. Second is I would love to see more women in investment. Uh, you know, I uh I'm really proud of women actually putting capital to the ideas that they believe in, but we need more women investing in other women. I think that's going to be really key. And also investing in other just good ideas. And then I think the last key thing, uh, which we don't do enough is uh recognizing and celebrating women who are actually doing great big things. Dr. Feife Lee, you know, she is not only an amazing academic, but now has created world, you know, labs, which is an amazing startup. We need to be celebrating women like her. We need to be celebrating women like Mira Murati, thinking uh labs, you know, having created some of the largest frontier models, most powerful frontier models. Michelle at Cloudflare. Oh my god, Michelle Zettlin at Cloudflare, like one of my role models, because if you think about all not only the glass ceilings are broken, but they've also really stepped into this AI game in big ways. We need to be celebrating and acknowledging their contributions in much more meaningful way.
SPEAKER_02When our girls graduate from high school, do we still gonna send them to university or something? Are we gonna recommend it in something else?
SPEAKER_00That's a good question. Uh you know, as a parent, I would say I would let her do whatever she wants to do uh and support her uh decision. I'm hoping that everything that we've embedded in her right now, add this way consequentially, she just ends up making the right decisions for herself, whether that is higher education, which might not be able to keep up, or starting her own startup or doing something else. I think we can just provide her the ingredients, let her make her life her own.
SPEAKER_02Larina, it has just been such a treat spending a bit of time with you. And last question that I'd love to leave the listeners with is on an individual level, what's what's the thing we should do more of and what's the thing we should do less of?
SPEAKER_00I think uh we should be doing a little, you know, I I have found myself that I've been so obsessed with this problem of how do you uh really get AI right, that in that obsession um I have not invested enough time in human connections. So I the thing that I want to do more of is invest more in human connections, uh, because at the end of the day, you know, I think great ideas only happen when you have great people surrounding yourself. And the thing that I would like to do less of is uh, which is very hard as a founder, is to turn your brain off sometimes and think just uh taking care of yourself. Um, I don't do enough of that at all because I'm obsessed with this problem of how do we get AI right. Uh, but I wish uh I wish to some days like, you know, take care of myself and stop obsessing so much about this problem.
SPEAKER_01The great Narena thing, everybody. Thank you so much.
SPEAKER_00Thank you for having me.
SPEAKER_01Thanks for tuning in to this episode of Made To Do. If you enjoyed the show, please hit follow or subscribe wherever you listen to podcasts and leave us a review. It really helps new listeners find us. You can find show notes, links, and more at thedo.world or join the conversation on my LinkedIn handle. Until next time, I'm Florin Hoffman and I hope you're inspired to get out there and do.