Automation and AI: Masters in Business with the CEO of Honeywell

Pleasure, Barry. Thanks for hosting me.

Original career plan was to work then and get a job that would get you a plan. Yeah, that was a career plan. And you know, first I did two small stints of a job, and then I joined Honeywell in early '89. It was a new company in India, so set up. So I ended up joining a startup because it was set up as a joint venture between two large companies. There's a large Indian company called Tata Group. They jointly automobiles everything, everything. So they invested in this venture. It's a big Honeywell, a lot of tech. And then they create this joint venture in which you show up and it's basically creating something from scratch. We had no revenue when I started. Our revenue was 0.001. So you learn how to build a company, how you scale. You wear multiple hats. Like in a startup you don't have a very defined role. So I think that early experience of high flexibility and you know, growing to a very high pace in a short period of time that laid some very strong foundations.

It turned into what it was planned for because Honeywell did not have its automation business footprint in India at that time. So you're talking 40 years back. So they partnered with a local company to scale the business. They already had those products and capabilities in US, and they were trying to get into Asia. And they formed partnerships in few countries, India being one of them. And the strategy was to penetrate the local market, develop the local capability. And we were able to do that quite well. So it's not that we have to change our product strategy, but we have to learn as we go through. We had intense local competition. How do you beat that? How do we create our own, you know, our own revenue stream there. So it was a very successful story.

I mean I think it's an advantage in a way to work in a practical business because you have to deal with actual problems, which the business deal with. And having worked in different businesses gave me an opportunity to deal with a different customer situation, different end markets, operational issue, commercial issue, product development issues, supply chain. So I would say, I mean, there is no replacement for formal education. I'm not suggesting that having higher degrees is a disadvantage, but I would say that there is equal amount of advantage to get practical experience. And I was benefiting from variety of experiences I got in my long career in Honeywell.

Yeah. So process solution business is, you know, it provides automation system in the energy sector. So energy sector think about it. Refining, petrochemical plants, other oil and gas facilities, pipeline terminals even I would say facility is likely to be paper, metals and mining. So these facilities are very complex in terms of their operating procedures. And if they're not automated, it's nearly impossible to run them. So this business provides a sophisticated automation system to these large companies. So think about Exxon and Shell and BP, as kind of a typical customer or Aramco in Middle East and Adnoc. So this serving these customer, this business was very global or is very global. Even today the business still is very successful. And I became CEO in 2014 of this business. And oil downturn happened within six months of I becoming the leader of the business. So you learn through tough experiences. Oil price was from forever 100 and $40, $50 to like a big nosedive. And we did a lot of work in that downturn. Learned a lot. But primarily or question this. This business is all about sophisticated automation and complex facilities. And then I moved to the building automation business where we still do automation. But now, in this case, buildings of different type hospitals, airports, schools, university campuses, data centers and their the business model was very different. Now you serve multiple building through a variety of channel partners across the board. And so our strength comes through product innovation. Our strength comes through channel management. Very different business model compared to what I did in my process automation days. And then performance material and technology. The very interesting business, they build technology, they build energy infrastructure. So if you want to build a, if you are a refiner, you buy crude, which we all hear a lot about today due to, you know, ongoing or on conflict. You don't sell crude, you sell product, you sell gasoline, diesel, you sell jet fuel. So they have options to make multiple products. And as the input changes on a market needs changes, they need to decide what are the options they have to build different offering from their perspective? This business provides technology to energy companies to build energy infrastructure. Because it's a molecule transformation. Converting one molecule to another molecule, that's a heavy technology involved behind it. So performance material and technology provides technology to the customer to build it, you know, energy infrastructure. So very high technology or research oriented business. You have a lot of chemical engineers who are going to invent the next best technology. And you provide the technology to some very large companies. And that was fascinating to lead that business, to see that cycle of nomination and work in that business.

I mean, I think at that time, the biggest challenge that time actually was the chip shortages. And how do we really redesign our products? Because chips are simply not available to really how to learn. How do we lead designer products in a much shorter period of time. So think about if we design a product in one year, we have to do that in two months because there's no other option. If we don't do that, we can't have an alternative source of the supply and we can't ship our product. So I used all of our experiences on dealing with such different scenario in my earlier jobs, and we were able to successfully, you know, deal with that. That was also a job. I also got exposure to the businesses of Honeywell, which I hadn't done before. Aerospace being the biggest one. So that got that into my responsibility. So there was a lot of learning there on how that industry works, which is totally different from everything else I had done.

Different animals in the sense of the end markets? These are right. There are some commonality of the business models. And you know, there are there are there is a common denominator, but there are differences, which really led me to think about whether we are good to be one company or multiple companies. When I started as a CEO, and part of it was the differences between them, but part of it was the opportunities which is ahead of us, that how these businesses independently could shape or scale much differently. Versus when we are together which which may led us to do a lot of work to think about optionality and pros and cons of each option. And which led us to make a decision that we are better off to split into three companies.

Yeah, I think it evolved a lot. I would say, you know, we there was a big change moment in early 2000 when Honeywell and Allied Signal merged together to call. Yeah. So, a little bit of, fun fact a light signal acquired Honeywell and changed its name to Honeywell. Which doesn't happen the quieter keeper's name, because they figured a Honeywell brand was so powerful, it was more impactful. So they changed their own name. So that was a big moment. Your question on cultural assimilation of two large companies. It was kind of merger of equals. And it did go through its own motion of ups and downs. And that's when Dave Cody came in as chairman and CEO of Honeywell. And Dave did a great job to rebuild the Honeywell culture, which was much more one company mindset. We are not two companies. We are one company. We're going to work towards one stock, one Honeywell mindset put a lot of operational culture in the organization. So that was one phase of, you know, under her and under his leadership. Then my my predecessor, Darius Adam Chick, he became CEO in 2017. He further enhanced our operational excellence skill. He invested a lot of effort to build more digital backbone of the company, simplifying Honeywell in terms of internal systems, we have. Darius was very passionate about digital on how to mine data and create more capability for our customers. So he created a culture of more operational excellence, more operational rigor, while there was much more focused on one Honeywell mindset cultural integration, not multiple companies. And as my tenure comes in over the last now two plus years, we are not pivoting from the more growth oriented company. And the reason that's important is that over a period of time, our margin rates have grown up and we were up 10% margin company in 2005, 2006. Last year it was 23%. So our earnings growth is going to come. More from the top line growth versus margin expansion. Not that we want to do margin expansion, but we can't get from another 15%. There's no headroom. So growth culture is important, which means we have to be more externally focused. Now. We need to understand our markets, need to understand our customers. What's changing, need to understand our competition. So our company, even though name preserves itself as a heritage and but it has been constantly evolving itself. And that's the one of the reason this company has survived 120 years, because it has courage to reinvent itself versus being inward looking and always saying that, okay, we are what we are and we are not going to change.

So I would say the we have to go back to where the automation industry started from to better appreciate how will AI impact automation offerings or automation products. Go back to mid 70s when this industry got created sometime in mid 75 timeframe. When computing was invented, chips were invented. There came the need to say the world has a lot of these expensive assets. Those assets are not running very efficiently. So can we move from the older technologies, which were kind of World War one, World War two, war era to more modern digital technologies? And the way automation system was created was that use senses set up properties and how a particular equipment or a machine or a processor is running. And then you have a software program running in a computer, which is going to make sure that it gets back to the desired condition and what it wants it to be. So it's a logic based predefined system. And the assumption was most of the time this will work in a normal situation when exceptions occur. Human will take a call. So automation systems were always designed with the human in the loop. And human was supposed to take care of change in input condition, change in output conditions, maintain the equipment, take care of maintenance requirements down the line. Now you fast forward 50 years before I and data science came in. The people who are running these equipment or automation system are different facilities in different environment. Think of a pharma manufacturing facility or a data center they acquired a knowledge on the exceptions which were occurring in those operating conditions. But when they retire or the move on, their knowledge went along with them. So when the next set of people came in, they kind of have the same learning cycle. Maybe some of it was captured in some documents, some manuals, but not a lot. So what AI is solving for is our systems have no intelligence layer on top of the core automation layer, so that when the next human being comes in, they are not starting from scratch. They have an advantage of all the learning over the last 25 years, all built in. So they get to say when this condition occurred, nine out of ten times this was done. It always worked. So you as a human being can say, okay, I think I will choose this logic mix. So humans still need to make a decision. So I think it's a changing the human and making them more capable. At the heart of it. And the reason it becomes even more compelling now is the shortage of skills which are happening in the industrial sector for performing these kind of tasks. So I would say it's a perfect convergence of the situation that more capability is coming into our system because of availability of data science. And at the same time, situation requires this capability to be there because less people are available to do this work. And that's going to create more capability in automation system. So automation system remains. Intelligence layer is on top of it. So it makes is automation system better in terms of what it can do by preserving its capability.

That was when I started as a CEO. My incoming thesis was that we have to simplify. This company is performing extremely well. Great return to shareholder, great service to our customer. But what will we do for the next 25 to 30 years? Are we set up for that? And my thesis was that we need to simplify this into a few things where we have a scale. But I started the job in middle of '23 as a CEO of the company. Two things happen in the year of '23, which is good to kind of reflect back just three years back. That was the first year when the aerospace cycle really became very strong. That was a near one where everybody said, oh, this industry is growing a lot. Let's pay more attention to it. And this was also the first year and something called AI was talked. Right. So if we were sitting here three years back we wouldn't be talking AI. So it's that recent phenomena. So the question we had to really ask ourselves that if we have to simplify as a company and these two external drivers are occurring simultaneously, a huge demand in our largest business, which is aerospace automation, which is core to Honeywell, is going to probably redefine itself. And so I should we do it as one company or should we do it as a in a different construct? And that question get into a problem solving by early '24 to say, let's look at all the scenarios. What's possibilities. What others are doing. And as we did the work over 2024, we got more and more conviction. It's better to separate automation and aerospace into two separate companies. But we ended up making three decisions because specialty chemical is extremely good business, which neither fitted in any one of these two. And we said it's compelling to also spin that off as a separate company. So rather than you know two. So it ended up becoming three. So they became a standalone business in October of last year, doing extremely well since we spun it off for six months. Very proud of the management team and the board which is running this company. The aerospace will become a standalone company in about 6 to 8 weeks from now. Six weeks actually, as we speak today. 29th June is a date and date is form week. We are quite committed to that. And it's going to be a leader in its segment in aerospace. And there will be a power play automation company, which will be probably one of the largest, if not the largest automation company in the world.

It's a pure play chemical specialist straight up chemical chemicals business. They make refrigerant which goes into your car. It goes into your home. They have some other technologies which are related to chemicals. That business is doing extremely well as a standalone company. The automation which you mentioned, building automation or automation of industrial facilities, that's part of the remaining Honeywell. Which is Honeywell Automation. Now we've already called Honeywell Automation. We're using as just as an equal descriptor on what the business will be. We will re-imagine our name as we go by in a couple of weeks from now and will reveal the name, what it should be. But for sake of simplicity, the chemicals business and aerospace business and an automation business and performance materials and technology is so part of it became into advanced material, advanced materials solstice and then part of it is retained within Honeywell. So it's split into kind of two because this is really everybody thinks of these very broadly. But there are some really narrow specific use cases for different correct groups. So I was trying to figure out what would align with what. So think about automation. Business serves three large end markets all types of buildings, all types of energy facilities and all types of industrial facilities. That's what we have kept in the automation. And we also are conscious that we should not make automation business serving so many segments that it's becomes confusing again. Right. So we want to narrow down to a few very large and impactful segments. This market size is about $200 billion. We will be just shy of 20 billion of revenue. So we have a lot of runway to think about creatively what more we can do. How do we grow more to be in our shortage of runway? Circularly automation is a naturally high growth, you know, segment because it's something which is so basic to existence of an industrial facility or on an asset. And then when you add the AI story coming on top of it, it's going to have increasingly more a growth momentum. So all all things being said, yeah, it's very well positioned for a compelling future.

So we do make aircraft engine for the business jet, some more midsize, smaller and smaller engine. The business jet engines we make, we don't make the big engines, but we are a systems company. We make different components from the nose to tail of the plane. So our components are right in the cockpit. Our components, we make rudders, we make navigation system, we make brakes for the plane, we make environmental controls in the plane. So we are a systems company, we make engines, we make Apus. So our, our approach is system design for a new platform. So every platform comes in and it could be a commercial plane could be a business. It could be a defense platform. We will pitch in different components and systems of Honeywell. Customers will select many of them, some of them then that will become part of that. You know that fleet for decades and decades. So it's a multi product business not constrained to one particular product line. And the business model is more powerful because it's a systems approach and not a component approach. So you're right in the heart of the systems. You understand how the whole mechanics work, and really add more value for our customers.

I would say the situation in our case was a bit unique because we started doing work to investigate our future optionality early 2024 and did a lot of work and actually even announced the separation of chemicals business. In October, Elliott wrote a letter to those in the public domain, and I got to see it at the same time, and everybody else saw it to say we should further split aerospace and rest of Honeywell two. That was their argument. There's a more value to be created. The good news was that we already had done the work, and we were convinced that's the right thing to do, but we have not announced anything. So we treated them as another shareholder who has a point of view and we have to articulate our strategy. So there was strong convergence on the thinking. And I think we worked with them very collaboratively on path forward. And I would say that deserves being said on activist shareholder. But my experience is that they are they are like any other shareholder who have a logical argument. If you have a counterpoint used to support this are the facts and data. Or if you support their point, then you have to execute it. And in that case, it just becomes much more of not what to do but how to do it. So our conversation with Elliott, like any other shareholder, was this is the situation. Here are the parts. This is how we are thinking about it. And we benefited from their expertise in capital markets how the shareholders will react. And definitely that helped us to shape our decision in terms of in a certain way, which was very constructive.

I think every business model has an era, so I think we have to go back to what created this era of conglomerate or larger companies. The it really started from them in the world was started becoming more globalized after 2000. China came into WTO. The world became more global. And there was much more global trade, which became the norm on how companies were growing. All US companies started growing globally, but at the same time, they were able to drive a lot of productivity by taking manufacturing into Asia. A lot of, you know, manpower, productivity, by doing work in different virtual way, with a lot of IT skills coming in. So there was a case to make bigger companies bigger because they had the unique know how to drive a lot of productivity and scale at a global scale because they were already present there. And that cycle persisted for almost 15 years till the time that value was captured and that value capture became generally known. Therefore, the question started asking to say, is creating this complex company worth it, or simplification or a sector focus is a better way to do it? So I think there was a reason that proposition really worked well and created a lot of value. Take a case of Honeywell or shareholder value creation from a time of 2000 to 2017, 18, one of the best in class and the entire S&P. So it's not that anything was wrong. We created tremendous shareholder value. But now this point of saturation comes in. And then it really brings you to the point of specialization. If the markets have scale and you can preserve scale while you are a specialist. That's best of the both worlds and that's what we are. We are doing now to create a scale aerospace company, a scale automation company. We are still very global and still have very mature processes, but at the same time we are focused on singular segment. So I guess like in everything else, you learn through cycles, and this cycle is all about having the mix of scale and specialization. And this will persist. And something else comes in now where there's a case to do something else. And I feel good about where we are in our position. And this is going to create much more shareholder value.

I think the situation for each company is very different because separation cannot create value alone by itself. You have to be convicted that the standalone asset has enough growth potential and invest and asset base, which is going to grow, which is going to create value. So I think comparing example you gave versus Honeywell is absolutely very different portfolio. Apples and oranges very, very different I mean so I would say that our drivers were more around what I talked about. Our stock price are more static. We were more we did not destroy any shareholder values. Our question was how do we create more shareholder value with external factors coming in growth or aerospace growth of AI is that inflection point for us to make a different decision? So we did it more from a point of strength for this. We have some crisis coming in. So sometimes you use your point of strength to make the right decisions. And we did it fast and we did it right. I think every other company would come from a different circumstances. But the decision on the outward look very similar. They look like they all did the same thing. But they all came from different backgrounds and you know, different set of assets. When when we started looking at it, some people believe that we, we got influenced by success of GE. I want to remind that GE success came post our decision. That was a process which was occurring. So yeah you have that's a data point to say they are also doing it. But some of the success we have observed some outstanding work by the GE leadership team that really started happening '24, '25 timeframe. We were far along the way in our own analysis, but at that time. So I think those are parallel things happening. So there's no one thing you can attribute to say that this thing influenced it. It's a combination of the reason which all come together, and that's what really brings us to where we are today.

So I would say that we as a country have an advantage of being the leader in the space of cloud and data science. And companies like Honeywell has a responsibility to take the knowhow which the tech sector is creating. Be it Microsoft, be it Google, be it Nvidia and all the, you know, very capable tech companies. How do we bring that capability into our sector? Because our customer is not going to go and they are not looking to buy a cloud capability or they are not looking to buy AI. They want to solve a problem. They want to run a business, they want to run their operation. They want to have more uptime. They want to have more, you know, profitability. So our job is to take our system to what I mentioned to you before and add this intelligence layer and what this intelligence layer is all about, taking capability from the tech companies. Take large language models from the likes of Google and Nvidia. Use the cloud power which is there from Amazon on Microsoft, but really build a purpose built offering from in the industrial sector. And as we are doing that, we are able to create the genetic models for our customers, and that's what they buy from us. The underlying plumbing, what we have, they don't want to know it. They don't want to know how this is built. Say, so you're automating this piece of my work. That's great. So I'm going to get more productivity for that. How much I should pay you for it? Right. So I would say we are in the state that this is no more a hypothesis. We are in the not in the early innings, but we are in the stage of deployment of these capabilities across different customer base. The why it is not taken up at scale is because our customers have to go through a significant change management in their organization because fundamentally, the roles of people are changing. Some rules require skills which are less important today, and some more new skills are required. And they can't do that overnight just because they created a new set of technology. They have to absorb it. They have to ingest it. But we have some fabulous examples on customer use it in scale in different sectors like university systems, quick service restaurants. People are using some of our technologies at a very large scale in refineries, etc. So I would say that if I'm sitting with you at 12 months back, I would have said very modest deployment setting. Today, I would say I'm very excited on what opportunity we see a year from now. I would argue that the penetration will go up substantially up, because there's a real economic value creation from what we already property, and we as a country are leading, because we have the core components of this technology. And now we have to, you know, take this capability across the world. And our customers are excited. They really like what we are doing.

So think about it. I mean, when you look at a small fast food dining restaurant, there's not much automation in that. But it consumes energy for sure. I mean let's take a typical McDonald's restaurant as a just an example. There's a kitchen there. There's a fryer. There's a refrigeration to just keeping a lot of products there. There's of course lights going on. These assets were never thought as a way to improve energy efficiencies by companies like us. We said we should automate a large hospital. Oh it's massive. There's a lot of opportunity there a large building. These assets were never paid attention by us because there was no technology available. But when the cloud technology came in we are able to connect these assets flawlessly. You know in a matter of hours and then you able to use a lot of AI based rule set to understand what should be the energy consumption actual versus what it is today. And give that tools to the owner to say, you know, in example we connected a quick service chain in UK, I think something like 500 plus of their restaurants into a single operating system, and they are observing 30 to 40% energy reduction. Wow. Like anything else, the good old management principle what do you inspect? Is what you get one of these for thinking running of my own. Nobody paid attention even though their desire to do something. There was no mechanism. So we created an easy mechanism to make this available to the customer. So all of a sudden they're able to generate a lot more productivity without adding too much of cost. And that's a part of the new tools which is coming in which was not possible. And that gives me a lot of excitement that this is going to be much more level of, you know, productivity, efficiency, which is less talked about. You know, whenever I dialogue about jobs is going to cut jobs. Nobody talks about economic value creation. It is doing a real value for our customer base, making people more productive. That's the story of the industrial side, which is probably requires more, more amplification.

I think, there's a scenario in your example, because of the technology, displacement of some old method was is a more new method. But the reason I believe more bullish about it is that we are solving a known problem. And the known problem is what has less people to do a lot of work around skilled labor in the industrial world, that's a real problem. So our solution is not trying to find a problem. We are fine. We are giving a solution to a known problem. Adoption rates are lower because of the change management issue, but this is the change management of the order of 18 months, 24 months, 30 months. That's not a decade, not a decade. Right. So I remain very optimistic, given my, you know, experience in these sectors. The adoption rates are going to be much more quicker because the problem is real. We are not inventing the problem. This problem exist for it. And by the way, this problem is everywhere in the world. This is not a US problem. Unskilled labor. Skilled labor. Europe has more population shrinkage than us. Go to Japan and Korea. They have the same problem. China has population shrinkage. Right. So this is a universal issue. This is not invented here. Now we get excited on the job displacement happening with robots and humanoids. That's a small portion of a manufacturing industry that probably is also displacing some tasks which humans are not willing to do, like lifting boxes. Right? Yeah. I mean, okay, it's not very interesting. But then there are other jobs with other sectors which we address where a physical AI or intelligence layer is going to create a tremendous amount of economic value.

I think the AI impact will be different in each sector. And I think if we make it to broad brush, we are losing the bigger picture. But when we are making it specific to a segment, then you are being more precise to say in context of the end markets. We serve the industrial sector. I talked about examples there. It's all about the skill shortage issue, which is very different from if we are using AI for better search engine. If I'm using AI for, you know, making a summary of our talk, which somebody can do, and that's a very different use case. And one can argue is it going to add productivity or not. Or is it going to take away jobs. That's a different scenario from simply not having people to do work. Very different scenario. And I think that makes our case more compelling. The adoption rates are driven by a near real, real need versus we are trying to create a need which is unknown. And that's not being talked a lot more. A lot more dialogue is around job displacements, but those are more in the jobs which could be automated like finance function or HR function. Maybe to a certain degree it's true, but not to the point. My personal view is that it's going to have the amount of impact which is being talked about.

So I think we have to appreciate the fact that where we are deploying AI is substantially different from what we are talking about in broader public domain. If you think of applying AI in industrial cities, let's take a case of a hospital. And I want to apply AI into automation system to make it more efficient. The data of that is not in public domain. The data is in Honeywell system or one of our competitors system. So you cannot go to internet and train anything because there's nothing to train on. So that makes data friction are the big problem in industrial sector, which in a way becomes a protection layer for us. But that does the friction becomes a protection layer, but it doesn't mean we should not do anything about it. Right. To say, oh, I'm protected. It means we should take it seriously to think of potential threats coming in because of the data, friction is removed, which is hard to do, but it humans are very intelligent, so we have worked very hard to remove the data friction and also use our domain knowledge. Because interestingly, you cannot solve a horizontal problem in industrial domain. What I mean by that is you do not have a software application like a CRM system or an HR system. The problems of each sector are very different. If you are a refinery, you are trying to produce more jet fuel and more diesel. If you are a life sciences manufacturing facility, you're trying to produce drug with minimal quality give away. But if you're a data center, you want more uptime. It's your problems are so different. So we can't create a magic AI application and sell to everybody. We have to be purposeful that where do we use our data and what problem be solved which only come from years of experience. So those two really become, in a way, a constraint for a generic company to come in because of data friction and lack of understanding of domain, which means companies like us, which possess both, have to solve this problem. And that's why we are very bullish about it, to say we are going to do it. We are going to take all the capabilities from tech companies and build a new set of capabilities to take our industry from a pure play automation to more towards autonomy and autonomy doesn't mean humans will disappear. Humans will become more empowered, humans will become more capable. And to the extent there's some skill shortages, it will address that point.

That's definitely a challenge for companies to have. More stability is what companies want. So I would say that companies like us have very mature processes to deal with it. So every time this issue occurs, we have some sort of disturbance for the bands for weeks, eight weeks, 20 weeks, who knows depending on the situation. So we have learned how to deal with it, but it doesn't come without a cost. You lose some growth in that window. You may have to incur extra costs like it happen in case of tariff, because in tariff court announced we have no choice but to pay it, right? Right now, whether we can recover it or not as a subsequent decision, are you one of the many companies that have filed litigation to get, get read or not find any litigation? How big of a hit was? It was not big for us. We were mostly down to the second part of your question. We have been doing manufacturing local for local for multiple years. So we made for us, in us, made for Europe, in Europe, made for China, in China. So we don't move lot of stuff around. However, what we cannot control is a global nature of the components we buy, right? If I have to buy everything in the supply chain and raw materials, correct. Because we can't make everything. So if if a component is made in Korea like batteries, we have to buy it from there. And if a component is made in China or somewhere else, we have to buy it from there. So that impact is certainly not under our coverage, because we don't have an endless capacity to invest in everything but our core manufacturing. We have 150 factories, you know, and they are well distributed around the world, and they are well distributed across the world. I mean, so we are so we don't have this foundational challenge of reshoring, but we certainly have to deal with changing environment in which we have to think about more local, supply based development aligned to what the expectations are at this point of time.

That's about 40% of the aerospace business. Well, it's certainly the current changes in the geopolitical environment and government spending more money is only positive. So it's going to become a even more growth driver for the business compared to what it had. So when we started this thesis two and a half years back, we did not predict this level of demand in the defense. But now that's really a reality. Whether it's in us, whether it's some of our US allies. There's a lot more growth opportunity across the board for different products and services we provide.

I think we have to look at what's look ahead. Where's this time to look back and be, you know, skeptical about it? I think we will look ahead. The problems of it's the world is in head of us. We clearly know the US lead in AI. So how do we protect the lead? We clearly have a lead in quantum, which is one of the businesses we own. That how do we really keep that scale? You do.

So we own a business called continuum in which Honeywell has a majority stake. We spun it off a separate company in 2021.

Yeah. I call that way back when. That's right. Really '21? Correct. So there are technologies in which us have an advantage. US have to rebuild its supply base for some of the critical sectors like semiconductor, like pharmaceutical, which are mission critical. And I think that's underway. But we need our patients that those things take years to happen. That's not a switch to say we want to do it and those things show up. They can take five, seven, ten years. So I think it's heading in the right direction. We as a country has all the capabilities. We have the capital, we have knowhow, but we have to refurbish some of our skills, which we lost over a couple of years in few portions of industrial sector. But let's not forget we have very, very capable companies which created the same sector all over the world. So those have not gone away. So reshoring is not as challenging as a lot of people making. It is more thoughtful in terms of which, how do you prioritize all things being equal? Reshoring is the right thing to do, but my personal view is we should pick up the top five and say, okay, here are the five. You want to go really low after I make it successful, because try to do everything is going to be just extremely difficult in order prioritization.

I think the point we discussed earlier that the automation gets heavily enabled by AI and really create the intelligence layer and that opportunity it creates is being underestimated. I think this opportunity is real because of the skill shortage, because of the knowledge gap, because AI got created over a period of time. So I truly believe that something which needs more, more conversation and more emphasis.

My early managers, I mean, I was lucky to have some very good managers who taught me different things. You know, not to be fearful about whom you are talking to. How do you think about value propositions, how to think global scale. So I think in Honeywell, you're blessed to have some very strong leaders in different parts of my career and in the first 15, 20 years, which really shaped you because of you. What shapes you as the first 15 ish years of your life? Because once those value system is built in your brain, you kind of live by that. And I was benefiting from some very powerful winters in different parts of the company.

So books I read variety both from leadership to sector specific. The reason why the book I'm reading is the Prize from Dan Yergin. If anybody's just read about oil economy, please do read it. Six months back I started reading chipboard. So some of the sector specific things, but also read about leadership of some of the people I admire. Dave Cody, who was chair CEO of Honeywell for a long time. He has a very fascinating book winning novel, and winning later. Indra Nooyi joined our board recently. She has some fascinating leadership books. I've read some of them. I read a lot of books on China. I think it's underestimated the scale of that economy. So I think we just need to. There's a book called World's View China's View of the world. Very interesting book. It's like we have a view about China. What about their view? Have we ever asked them the question why do you what do you do. So I kind of have very diverse reading habits of, you know, reading from my business specific to leadership to some of the country specifics. You are talking around a lot on that.

I mean, both are fascinating career. I would say engineering is a career, which gives you a lot of options. So do pursue that because it gives you a wide variety of choices. Management is something that people should do who have more willingness to take a risk and have courage to make decisions, because in the end, at some point in your career, you will have to do both. And if you think that's not your sphere, that's something you are not good at it. I would rather argue than you choose something you really good at what it is. Otherwise you're going to get saturated at some point. But management is an excellent carrier by itself. So both our both are excellent.

I don't know, I think I'm always excited about learning new technology all the time. You know, I still very curious to read things, how they work. I think I will say that staying curious is very important for us as a human being. We should never be satisfied on what we know. We should always ask the question, what? We do not know whether it is about a technology or a business process, or for that matter, any fact of life and more. You are curious, more successful. You are because you are open minded and you are always willing to learn. And that has been my principle all my life. Always learn something new about anything.