Transcripts of Season 3

Welcome to Deep Pockets by Petra Söderling,  a conversation about governments, technologies, and innovation.  You're now listening to season three of winter 2024.  I call this season the book club.  In March, 2023, I published my own book. Governments and innovation, the economic developers guide to our future, which is available in Amazon in paperback, hardcover, and as a Kindle ebook. 

It's now time to look at some other great books out there that discuss the same theme, how publicly funded technologies turn into privately run innovation. And what happens after that?  Our theme song is by New Orleans jazz icon Leroy Jones.  Deep Pockets works in cooperation with Studio Aguse, a boutique recording studio in south of France for audiobooks, podcasts, and music. 

As I started reading the book for this episode, the very first chapter, third paragraph, threw me back to a cold winter day that I will never forget.  Let's see if you recognize this following quote.  From this day forward, it's going to be only America first. America first. Every decision on trade, on taxes, on immigration, on foreign affairs will be made to benefit American workers and American families. 

We must protect our borders from the ravages of other countries making our products, stealing our companies and destroying our jobs.  Protection will lead to great prosperity and strength.  That, of course, was Donald Trump's inauguration speech.  I remember watching it on TV, watching the faces of the leaders of the previous administration as they sat there, expressionless, staring into a void.

I remember the face of the Democratic candidate who had lost, the person who might have been the first woman president for the United States of America.  The reason, of course, why Donald Trump was up there being inaugurated instead of Hillary Clinton had much to do with globalization and how the American middle class felt it was being left behind these uncontrollable global forces. 

The book for this episode is Open, The Progressive Case for Free Trade, Immigration, and Global Capital by Kimberly Clausing.  It's a book that defends the global economic integration and suggests ways for Americans to first of all ride along and not become crushed by international realities, but also to create national policies within the U.

S. to equip workers for the modern economy, better tax policies, and better partnerships between taxpayers and businesses.  Kimberly Clausing holds the Eric M. Zalt Chair in Tax Law and Policy at the UCLA School of Law.  During the first part of the Biden administration, Clausing was the Deputy Assistant Secretary for Tax Analysis in the U.

S. Department of the Treasury,  serving as the lead economist in the Office of Tax Policy.  Prior to coming to UCLA, Clausing was the Thurmond A. Miller and Walter Mintz Professor of Economics at Reed College.  Professor Clausing is a non resident senior fellow at the Peterson Institute for International Economics.

A member of the Council on Foreign Relations and a research associate at the National Bureau of Economic Research.  She has worked on economic policy research with the International Monetary Fund, the Hamilton Project, the Brookings Institution, the Tax Policy Centre, and the Center for American Progress. 

She has testified before the House Ways and Means Committee, the Senate Committee on Finance, the Senate Committee on the Budget, and the Joint Economic Committee.  That was a long intro, but I really wanted to make it clear that this is a person who is one of the highest authorities on the subject.  Dr.

Clausing, I'd like to welcome you to Deep Pockets and also ask if I may call you by your first name as we do with other guests here on this podcast.  Thank you so much. It's very nice to be here. And yes, of course you may call me Kimberly. Thank you.  Wonderful. Okay So to get us started, I wanted to ask you about something not directly related to the book itself the title of your book is the progressive case for free trade etc  and also The first sentence on your Wikipedia page is Kimberly Clausing is a liberal American economist.

So my question to you is, are you a scientist or are you a politician?  Thanks for that question. So I consider myself an economist first and foremost, and I look at things with the  dispassionate view of a scholar and researcher that wants to find the truth. And I studied economics because it seemed really like a good tool That enabled our common sense to go further than it otherwise would in solving social problems that we come across.

And I really think economics is quite good at that. So I also come to economics with a passion for solving social problems, um, and doing so in a way it's easier to do, of course, if we can do it in a cost effective way, right? So I think economics helps us see that, um, but it also really serves.  So I don't think my views are easy to label as liberal or conservative, actually.

And I don't control Wikipedia  or edit my own page very much. Um, so, you know, I, I, but I do think some people on both sides of the spectrum would have discomfort with aspects of the views and in a way, they might be centrist relative to, to either the far left or the far right, certainly. Um, but I did choose the title of my book, and I think what I was trying to convey there were two things, you know.

First, that the values that, um, we might consider progressive values, such as concern for the lower and middle classes, such as making progress and solving social problems, those kinds of values Also align with the values of economic openness, right? And that may not be immediately apparent to every reader, every citizen.

So that's part of why I wrote the book. Um, and I think a lot of people are, you know, in present day America and in, in around the world, um, concerned often about these groups being left behind, you know, the, the lower middle class groups. And, and some of these concerns are quite real and they're quite documented by the data, which I kind of go over.

In the book. Um, but one of the interesting things about the ways in which some groups have been left behind is that that sense of being left behind isn't really something that we can lay at the foot of globalization alone. There's a lot of other forces. Um, that are disruptive and that affect everyday people.

Um, and examples include technological change, the growing market power of companies, the declining power of labor movements. Um, those are just a few. But there, but there are a lot of other forces as well that create disruption. And what I'm trying to kind of say in that, in that book, um, is that if we choose,  To scapegoat, um, foreigners and immigrants as the, the true source of all of our problems.

That ultimately it's going to do more harm than good for the types of things that progressives value. Um, and as one example, if you, if you put up a bunch of tariffs. Right? Um, that's how we used to fund the state. If you go back a century or two, um, and those tariffs are regressive consumption taxes that fall disproportionately on the poor, right?

So if you replace tariffs with an income tax, you actually can do more to make sure that everybody benefits from any source of disruption rather than just kind of having the people who are buying imported goods sort of shield. That burden. Um, and there are a lot of other examples I go through in the, in the text too, but there's an enormous amount of evidence, for instance, that immigration on net helps almost everybody in society, right?

Um, it creates new jobs when people come to. the United States, or they come to Europe. They come not just as workers, but they come as founders of businesses, um, and as entrepreneurs and they create the new industries of the future, right? And if you've got that kind of energy in your economy, you're going to be creating more jobs, more GDP, more innovation than you would if you shut your doors to the best ideas of the world. 

Look at the Nobel Prizes,  just like how many Nobel Prizes were, yeah.  Yeah, the Nobel Prizes, it's, uh, the United States has an outsized, um, uh, you know, uh, is lucky enough to have an outsized role in receiving Nobel Prizes, but if you look at who receives them, they're almost always, I mean, the majority of them in scientific fields are foreign born, um, Americans. 

You've come here and been able to, uh, meet each other and, uh, native born Americans in the finest universities of the world. Right. And there are lots of examples in Europe and elsewhere, too, of this kind of synergy of people coming from outside, uh, mixing with the people who are already there and coming up with better ideas than they would if they all stayed in place. 

Yeah. That's fascinating. Um, so when, you know, I looked at your resume, of course, before this call, and you've been writing articles since the early 1990s, but the book that came out in 2019, as far as I know, it's your first book. So why did you decide to write this exact book? Why did you write it in 2019? 

Yes, so, um, it's kind of a interesting story. So when you write a book with an academic press, um, as, as I'm sure you know, they can be slow. Um, and so this book, actually, I started writing it in the early days of 2017. It was my response to that. Election. And if you look at that election year, it was characterized, um, by just a lot of bashing of things for and, and of immigrants, right?

There was a  sort of return to almost a tribalism where, which you hear in that inauguration speech, it should be us, not them, you know, and the us is defined pretty narrow, narrowly. And I think that kind of nationalism and xenophobia is, is very dangerous. Like for the future, um, peace of the world, but also for the future prosperity of the world.

And I, and I talk a little bit about those dangers in the book, but my, my sole emphasis really in the book is that not only is it dangerous, it's counterproductive to the very aims that you're purporting to serve, right? If you, if your response to American workers that feel left behind is tariffs and immigration restrictions, you're going to be subjecting them to.

New shocks is as foreign countries will retaliate to our tariffs, and that will reduce jobs and export sectors. Um, and new shocks is as your industries will have to pay more for their imported intermediate goods, and then we'll have to still face competition from abroad. Um, and new costs at the grocery store.

Um, some simple studies have suggested that the Trump tariffs have added, you know, 1, 500 or so to the typical costs faced by an American household in a typical year, right? So all those things actually kind of don't serve the very interest that they're supposed. To serve and cutting down on immigration is the same thing.

I don't think having fewer entrepreneurs and fewer discoveries in the United States is ultimately going to serve the long run economic prosperity. So I suggest in the later part of the book, um, solutions that I think are productive, cause I do think there are some real problems associated with economic inequality.

And with having a group of people that feel left behind, I think those are huge and serious problems, but I think there are productive and straightforward ways to address those problems that don't kind of shoot yourself in the foot. Um, and, and three that I suggest in the book are making more investments in people, right?

So that includes everything from pre K education to community college, to infrastructure that people need to get around in their daily lives. Um, having a more fair tax code where we ask a little more from those at the top who've been really successful due to technological change and all these market power and all these other forces.

And then we use some of that money that we collect from those at the top to beef up the safety net for those at the bottom. And that includes. Expanding the earned income tax credit that generates negative tax rates for those at the bottom or expanding the child tax credit to help pull kids out of poverty.

Um, and then a third policy that I recommend as well is sort of focusing on, um, you know, ways that the business community themselves can be more transparent in their actions. So it's less about I think business. And big business even has a lot to offer, um, Americans, but I think they do need to pair that pay their fair share of taxes right here in the United States.

And I, and I think they need to also, you know, be more transparent about a number of other. Um, uh, you know, uh, actions that they take that have larger societal effects. So I'm not suggesting that we throw out the profit motive. I come very much on board with capitalism. Um, but I, but I do think it's the responsibility of the state to, to set the rules of, of capitalism and then those rules include, uh, requiring a certain amount of transparency and responsibility from business as well. 

Yeah, I'm, uh, uh, chuckling a little bit when you said, you know, you like capitalism. Uh, I also say in my own book that heck, I love capitalism and like my book is not against capitalism because I grew up in Finland right next to the Soviet Union. So I saw what it was like going to the Soviet Union. So capitalism.

rules, even though, uh, you know, my kids and their friends, they're anti capitalists and you know, they want to, uh, embrace more, um, equitable and social models, but I don't see an alternative for capitalism at this,  at this point.  Okay. So yeah, it was 2019 when the book came out and a lot has changed since.

Um, so how do you see the things now? We've had COVID, uh, Ukraine war is ongoing or Putin attacked Ukraine. Um, Israel, Gaza is an ongoing unrest. So would you still write the same book if you had to write it today?  Yeah, I, I certainly would. I mean, I would maybe broaden it in a few respects. I think one of the things that we've seen in the years since the book came out is even more ascendant nationalism, even more conflict.

Um, we've seen Authoritarian governments kind of throughout the world double down on authoritarianism. Um, and also use the nationalism that they see in the West as sort of a, their own scapegoat to say like, you know, it's a, we have to crack down on, uh, this or that or, uh,  inkling of democracy because the West is out to get us, right?

Um, listen to Trump's inauguration speech, right? Um, and so I think some of that. Backfiring nationalism has kind of caused resurgent authoritarianism elsewhere and, and really kind of made these messages of, um, openness, I think more important than ever. Right. I think it's important to, to think about, well, how do we build strong, uh, democracies and strong economies?

And I think part of how we do it is by focusing  on our own Domestic strengths, which means investing in people right? Um, and making sure our prosperity is shared. And you can do that in the capitalist economy by having appropriate state policies, right? The take a little from the top and and help build a safety net, helping those at the bottom, right?

So I think these are ways to have a strong, solid  Shared prosperity, but then leaves you less vulnerable to these arguments that say, Hey, no, the only way to be prosperous is to close the border and to scapegoat others and to restrict what we cooperate with other countries on. And one thing we've seen with COVID and we see with war and we see with climate change is that when you're in situations facing public health and national security and climate disaster, no one country can solve these problems on their own.

Right. It's not like Finland can tackle climate change or Russian aggression or public health all by itself. Right. We all need each other. And some of these problems are global collective action problems. And we don't just need, uh, the West. We need the whole world to tackle things like climate change.

Like if we can't get. China, uh, to work with us. And there are also very interested by the way, in working on climate change. We can't build a more cooperative, um, you know, uh,  framework for working with countries like China on climate change. We're not going to solve the problem. Like the world divided into blocks is going to be a less prosperous and less.

Peaceful and a less safe place to live. Um, and so I think moving towards openness is, is more important than ever.  Yeah.  Okay. With openness, uh, comes, uh, innovation, innovation, technology is everywhere. Um. Prevalence, um, you, uh, I noticed you've received funding from, uh, the National Science Foundation as well.

So you're an expert on innovation. Uh, so how do you receive the role of technology in economy? And I understand this is a really broad question, but what's, what goes through your mind when you think about the things like social media, cybersecurity, national security, and so on?  Yeah. I mean, I think this latest, um, wave of Technological innovation around artificial intelligence is yet another example of the enormously transformative role that technological change has played in our economy, you know, my entire life.

I was, I was born in 1970. And at the time, technology looks very different from from how it looks now. And every decade that goes by, like there's an enormous amount of change with computerization, with digitization,  with, um, you know, all of these other trends. And so, um, in a way, it's sort of analogous to trade in the techno, technology and technological change creates winners and losers.

But it's ultimately something that we'd be worse off if we stopped somehow, if we tried to throw away our computers and machines, or if we tried to uh, You know, um, prevent, um, biomedical discovery, right? Like those things, while they create winners or losers and are very disruptive, they, you know, they're ultimately very good things.

So I think the case here is, is quite similar. You want to keep the good, but you want to use. The power of the state to make sure that those who are left behind or those who are harmed or displaced by these big forces have rungs up on the ladder. And I think, um, community college is a good example, right?

If you, if you lose your job doing some old school manufacturing that got displaced by the next wave of robots, like you need to be able to find resources that let you use the, the robots to your advantage, right? That let you retool and reseed. skill and you need to be able to do that and have the economic resources to support yourself while you're doing that.

You know, so I think those kinds of disruptions, um, there's every bit of evidence or just as, just as disruptive as anything that comes from trade, if not far more so, um, and we need to be prepared with the sort of fundamentals that I talked about earlier, investing in people and infrastructure and, uh, having a fair tax system.

And having the state set appropriate rules of the road so that we can make sure that society benefits. Um, so I, I view these as, as very similar in some respects. Yeah, absolutely. Yes, absolutely. Uh, very insightful. Okay. My final question, uh, based on everything we discussed here today, What kind of advice would you give to the young people of today? 

Yeah, so, um, I have a couple young people in my life, myself, my, my children, but also many students, um, and, uh, all these people are in their 20s, um, and many of them, I think, do get a sense of of, um,  futility and fatalism around some of these big forces that are surrounding us. Uh, they look at climate change, they look at technological change, they look at globalization.

Sometimes they just want to throw up their hands and be like, you know, I wish we could all have a more simple life. Um,  but I think it's extremely important that, that people Engage and that they work toward the future that they want, right? Like, so my view of a future I've already described as one with capitalism and openness and where we work with other countries to solve global collection, active.

Global collective action problems, but, you know, you can't just wave a wand and make all of that happen. So I think sometimes what's most productive is for people to pick one thing that they care about, um, and sort of engage and work on that. If, if, if it's that you care about, you know, clean water access for a community, work on that.

Or, or maybe you don't have the skills to work on that and you just have a, a job that's, um, Doesn't directly address the problem you're worried about and find some ways to, you know, volunteer or contribute or think about these other problems that do stress you. But pick something,  you know, that's tangible that you can focus your energy on.

Don't let the sort of overwhelming, you know, magnitude of all the problems in the world lead you to inaction because, you know, the world improves By a million actions of a million people all determined to make it slightly better in their little corner. So I think everyone should join up, engage, and work. 

I love it, that's good advice for even us older people.  Kimberly Clausing, uh, the book is called Open, The Progressive Case for Free Trade, Immigration, and Global Capital. It's available on Amazon in paperback, hardcover, and Kindle ebook. Thank you so much for coming to Deep Pockets. Thanks  so much for having me, it's been a real pleasure.

You've 

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Speaker 1  0:19  

Welcome to deep pockets. The podcast for exploring how basic science, Once created in a lab and funded by public means is fueling the economy with completely new private industries. deep pockets is created by Petra Soderling.

Petra Soderling  0:34  

It's common knowledge that internet a Connect connected network of computers communicating via TCP IP is a result of the US Department of Defense funded research in the 1960s, resulting in what was called ARPANET. It is also widely known that English scientists Tim berners, Lee, invented the World Wide Web in 1989. While employed at CERN, the European Organization for Nuclear Research. What followed after how the web became what it is today, has received less attention. In 2001, Tim berners Lee James handler and oral Acela published a paper in the Scientific American called the Semantic Web. Scientific American is not a lightweight publication, founded in 1845, it has published articles from people such as Albert Einstein, and it is the oldest continually continuously published monthly magazine in the United States. This paper, the Semantic Web, argued that a new form of web content that is meaningful for computers will unleash a revolution of new possibilities. Today's guest is Dr. aura Lassila, one of the three co authors of this paper. Welcome to deep pockets, aura.

Ora Lassila  1:56  

Thank you very much.

Petra Soderling  1:59  

Before going into Semantic Web, I'm gonna have to ask you, how did the three of you get together?

Ora Lassila  2:06  

Well, yeah, that's a that's a good question. So I guess sort of the combination, in a way sort of, of things that led to the Semantic Web happened in 1996, when I was working at MIT, but But before that, when I was working at Carnegie Mellon, in the early 90s, I met Jim handler, you know, you know, DARPA funded program with DARPA is the Department of Defense's Advanced Research Projects Agency. And we were working on on ontologies. I'll come back to what an ontology is. But basically, it suffices to say, it's a rich data model at this point, and so so I knew Jim, from before, and then, in 96, I went to work at MIT. And I worked at Tim berners, Lee's group, the World Wide Web Consortium, which was headquartered at MIT. And one day, Tim comes to my office and says, so what are what do you think is wrong with the web? And of course, I you know, this is a little bit of an intimidating question, considering who is asking. And I said, well, Tim, the web was built for humans. And human interpretation is needed for kind of making the content meaningful in some sense. And that makes it hard to automate anything. I want automation, and I want autonomous agents. And so Tim gets really excited and goes, like, that's it that is wrong with the web. How do we solve this? And I'm like, I don't know. But yeah, I've been working on knowledge representation and ontologies for many years. So maybe we could see if we could apply knowledge representation in the context of the web. And he gets very, very excited. And he says, Good, please look into this. And to me, this is the this is the point where where sort of semantic web gets started in a way and after a couple of years of work on it, in that area, and we so we found a lot of people from other institutions, who were also looking into something like this and, and we got all those people together and and decided that we were going to have to do something together and then maybe that leads into a standard or something like that, then Now. So one of the groups we found that was that the Jim Hendrick group at University of Maryland, had been looking into into this. And then subsequently, Jim became a What do you call that a program manager in a DARPA program that was called the, the DARPA agent markup language. And that's, that's obviously a misnomer. But it was Jim's sort of a strategic invention of nomenclature. Because DARPA and the Department of Defense, they were very interested in agents. And if he had said Semantic Web, we probably would not have created as much buzz as this as this other term. did. And but the DARPA agent Markup Language Program, or daml, really was DARPA's take on the Semantic Web. And and then, Jim calls me one day and says, Hey, you know, we really should write something about this. So that, you know, more people would find out about the Semantic Web and, and, and I'm like, yeah, let's write something. And then he had spoken with some folks at Scientific American. And, and, and, and found out basically, that the Scientific American was interested in publishing an article in the sand. And we started writing this, and then at some point, maybe halfway through the writing process, we thought, well, maybe we'll invite Tim as well, because he has name recognition, and you know, maybe, maybe, maybe, maybe it would be a good idea. And so so then the three of us

got together a few times. Tim's office at MIT, and we do a lot on the whiteboard and, and, and stuff like that. And there's a lot of writing over, I don't know, maybe a period of eight months, nine months, something like that. Um, the funny thing about this, the outcome then is that when the article was published, not only did Scientific American make it the cover story, which we had not anticipated. But Scientific American has a policy that author's names are alphabetized, regardless of who is the lead author, and thus, does this is berners Lee handler, and last Scylla, which, I'm sure your data, Jim quite a bit. I mean, I was just happy to get published. But of course, now, forever, this article is referred to as berners Lee at all. So Nevertheless, the article created quite a bit of buzz, when it when it came out. both good and bad, in some ways, I mean, a lot of people got excited. And then a lot of people accused us of being science fiction authors and things like that. So that was roughly 20 years ago.

Petra Soderling  8:18  

Yeah, you mentioned a lot of big science and tech words, I think we need to back up a little bit on on the like, what is Semantic Web the basics. So what did you mean by Semantic Web being his new form of web content?

Ora Lassila  8:33  

Sure, sure. Yeah. So if you think about quote unquote, traditional web content, which is funny that we can now say traditional, but But nevertheless, it really is content that is meant for humans. pages that you read pictures that you look at. And more so when you go back 20 years or more than 20 years, that's really what the web was about it was text and pictures and and in order for for that information to be meaningful in any sense. You basically needed a human to interpret that content somehow. Now of course, someone will now say, Well, now we have natural language processing and all that stuff. Well, of course, what we're gonna we're gonna talk maybe talk about that in a little bit, but but the but the direction where we wanted to go was to create content that was structured in such a way that it could be interpreted by machines because it had clearly defined meaning. And this question of meaning or semantics is a very tricky one in, in in AI and computer science in general. And ever since the 1960s, there's been an effort within the within AI research called knowledge representation, which basically aims at structuring information in such a way that machines can infer things from it and draw conclusions from it. And, and this information is typically structured by way of these things we call ontologies. Okay, so now of course, people who went to the University and studied philosophy, they go like, what the heck is that guy talking about, but in computer science, we have sort of hijacked the term ontology, we don't necessarily exactly mean metaphysics by it. But even though we're not necessarily that far from it, but but the essence is that an ontology is a rich description of a data model. It's like a description of a domain of discourse, if you will. an ontology identifies the concepts that you talk about the relationships between these concepts, and it may identify logical conditions that have to hold in that particular domain. And then once you have information structured, using an ontology, you can then use reasoning, to draw conclusions. So basically, uncover things that are implicit in your data, but not explicit. So let me give you an example of an a sort of a most trivial possible example of what reasoning is. So let's say in my, in my data, I have this thing. Let's call him Felix, and we say Felix is a cat. And then we also say that cats are mammals. So now, implicit, in this information, by way of some reasoning rules, is that Felix is a mammal. Because Felix is a cat and cats are mammals, and we can infer that Felix is a mammal. Now this is, of course, an incredibly trivial example. But this is basically what what what reasoning is about, you can kind of either uncover implicit information, or you can find out things that are not possible. So for example, we can say things like, we have cats, and we have dogs, and we can say, well, the set of cats is disjoint. With the set of dogs, it means that no one thing can be both a cat and a dog. This would be one of those logical constraints in an ontology. And so if you now find out that, that some something is a cat, you can also conclude that it's not a dog, by way of this, or if you find something that is a cat and a dog, you can conclude that you have a, you have something of something wrong with your data.

So so in some in the Semantic Web, we basically took this kind of technology. And mind you this technology has been studied, at least since the 1960s. We took this technology. And we basically said, Well, can we apply this in the context of the web? And maybe even more importantly, we said, can we build support for this kind of technology, using existing web technologies? Why web technologies were web technologies are ubiquitous, they're well understood, they're well supported. And if we think about this, they are our initial motivation, which was to build these what we call autonomous agents, which had been studied in computer science for quite some time. These would be software entities that basically would act on your behalf and maybe could move from one place to another and do things for you. The good news here is that web technologies, by way of being so ubiquitous, enable these kinds of things to be built easier because HTTP goes through firewalls and provides basically this ubiquitous connectivity. And this is what people not only the agent community, but also in the knowledge representation community have been asking for a very long time. So can we can we have universal connectivity and can We have can we have things that we can share ontologies we can share and data we could share and the Semantic Web would have provided all those things. So this is basically what we had in mind that there would be this data that you could access this data, you could share it over the over the web. And then whatever software you had could could then draw conclusions from that and do things for you or on your behalf. And, and now we come to sort of an important distinction about this, this this question of autonomy, and an autonomous agent. So software, and Information Systems. Throughout the history that we've had computers have been like tools, think of a hammer. Hammer is an extremely handy tool, but he won't build you a house, you still kind of have to swing your arm. But a hammer makes it a lot easier to pound nails into the wall, rather than using your plain fist. So what we had in mind was that we would be moving away from the sort of tool paradigm towards something that ultimately might culminate in something like the, the Information Systems equivalent of a building contractor. So building contractor is someone you tell, okay, I want a house, can you build it for me, and then the contractor goes and builds it for you, unlike a hammer, which requires you to actually do the work in order to get the house built. So so so this is sort of one of those. I I'm hesitant to use the word paradigm shift, but I'm doing it anyway. Something that kind of moves things, to a completely new way of doing things new realm. That's that's kind of what we had in mind. When when we were writing this article, and of course, because we were describing things that happened in in this way, people then accused us of being a science fiction authors and describing all kinds of all kinds of things that were not really possible and but I think that a lot of the things we said in that article became true very quickly. And now after 20 years, really a vast majority of the things that we said, has become true, the thing that hasn't happened is the is the sort of massive global kind of adoption of this technology, which would have made made the this kind of data widely, widely available. Now, that said, we have a lot of data a structured using using the mechanisms of the Semantic Web. We built we built standards.

for for for representing this kind of data. And there's been a there's been a lot of uptake in in the standards. And in fact, chances are you are using semantic web technologies every day. You just don't know it. It's a little bit like AI. People say, Oh, this AI new thing. Well, we've had we've used AI for a very long time. I think this is sort of generally kind of a funny phenomenon in our industry. We talk about new things. And then we realize that things actually aren't new. In many ways we well, let let's take AI and Semantic Web as an example. So AI, has been around since the 1950s. And we've studied AI since the 1950s ontologies. We've studied in computer science, at least since the 1960s. But we've known since the year 1900. Then ontologies are a good vehicle for formal representation of information. Thanks to the philosopher Edmund Husserl and ontologies are often structured as graphs. Now, graph theory has been part of mathematics since the late 18th century. And then this, this this sort of new AI, machine learning and all that, well that's in a way that's sort of fancy application of statistics. And we've known how to do statistics for a long time. And in fact, nobody calls them statisticians anymore. They call themselves data scientists or something like that. But it's still the same technology in many ways. Okay, So now a lot of lot of people are screaming now like what is that guy talking about but I'm not wrong

Petra Soderling  20:06  

about yeah i think you know caught my eye when you said it's this agent acting on your behalf so that's your vision came true that web is is now semantic and it is what you envisioned you wanted the web to be designed for computers and not for humans and many would argue that it's it's too far now the web is designed for computers, especially computers working for advertisers well it's too computer driven

Ora Lassila  20:34  

well there is that of course and yeah, so this is of course true and in some ways I'm sure many people feel that that in a way they've sort of lost control or or there's some some kind of partial loss of control over what's what's happening and and then of course, with this come will be sort of doomsday scenarios about AI and I don't know why these doomsday scenarios tend to come from people who are not AI researchers but are are doing something else and and I know there's a many physicists have weighed in on this Stephen Hawking weighed in on this. And, and Roger Penrose, who it I'm not sure if he was Hawking's thesis advisor he and I already in the 90s weighed against the AI and I think Elon Musk has has has has had some kind of doomsday scenarios about AI and I think that this honestly, my feeling is that only people who have not participated in large scale software projects would make predictions like this because when you think about the software it's so you know damn right a miracle that anything works and and and so the the the Skynet is not happening. This Skynet from from Terminator, the machines are not taking over, this is not happening. So you know. Now, of course, part of the concern about AI is the sort of ethical use of AI and all that then, and I understand those constants to their their real concerns, but those concerns are not new. those concerns have always existed when it comes to technology. What is the ethical use of technology, and we're no matter what the technology is somebody who can can dream up some unethical, quote, unquote, way of you using that technology. In fact, when I was I was interviewed about 20 years ago about the Semantic Web by by the magazine, New Scientist. And the interviewer asked me about the sort of the possible negative uses of the semantic web technology. And I my answer at the time was in it we used to say master today is that almost any technology can be used for good or it can be used for bad. And, and so I'm not entirely sure it's the technology's fault, how something is used. And in many ways, when you look at the development of technologies, there are things it's mostly bad things that drive technology development. The war, for example, drives technology like nothing else, and and the other thing that drives technology is vise. So in some ways, maybe there is no good without the bad, I don't know. But I'm not going to get into the philosophy.

Petra Soderling  24:05  

So, you know, some people might say that the web works extremely well. And it's anticipated if I want to buy new shoes, it's gonna advertise shoes for me immediately before I can even think about it. But then on the other hand, sometimes it doesn't work. You have this example, the medical example in this Semantic Web paper where a mother is alien and the children need to find a doctor's I actually had a real case, but 20 years later, a real world world case where, because I recently moved to a new city, I needed to find a new general physician for an annual checkup and I ran web searches for a general physician who is near me accepts my insurance policy and accepts new patients. So that's three different attributes. And I got a lot of hits on doctors near me driving distance, but none of the results will at least none of the first 10 results were about The insurance or about if they're accepting new patients. So my question to you that do you think that these so called code less platforms or no code platforms would solve this problem? And I mean that in my case that the various doctors offices would all use these simple web tools, and sort of enable the insurance policy and the accepting new patients as searchable attributes?

Ora Lassila  25:27  

Well, I mean, it's a nice idea. Personally, generally speaking, I don't believe in there's no code idea at all, there is no such thing as no code. There's always code and the person who, who tells the computer what to do is called a programmer. And whether you were trained as a programmer or not, is a it's a it's a separate question. But this for me, let's, let's put it this way, the scenario described is is a good one. There are things you can do by way of configuring rather than by way of programming. And, and those those are those are, of course, perfectly possible, I'm not entirely sure if that's what we mean by by low code or no code. But But, but obviously, we can imagine a tool that would, that would allow medical providers to accurately describe what it is that they do when under the conditions they can do it, and all that then so we can imagine a scenario like that. But then, of course, the funny thing is that then there are obstacles to this, like, market economy. And, and and sort of commercial entrepreneurship is actually an obstacle for this because everybody, you know that now we have now we have competing attempts to do do these kinds of things. And oftentimes the things that we would really like about information systems, such as interoperability, and the ability for these systems to exchange information with one another, are often things that don't easily happen in a market driven scenario. And I don't mean to sound cynical, but I think that that's one of those things that well, we realized that and I learned this after working in a venture capital firm for a while, is that unless somebody is making money off a technology, that technology is not going to happen. And now we have a lot of these technologies, that would be extremely useful. But we cannot really describe a good way for somebody to make money off them. And I think that Semantic Web is largely predicated on the idea of interoperability, Semantic Web, in fact, is a technology that promotes interoperability and interchange of information between between different parties. But it's hard to put a price tag on, on on on interoperability, like how much would you pay for interoperable interoperability? Because somebody is going to come and ask, Well, you know, does that allow me to sell more stuff? Well, in some cases, it does. But it's, it's, it's, it's hard to kind of find the direct link. These are sort of indirect benefits of the technology, and people are more interested in the direct benefits, because they can kind of put a price tag on Okay, I'm willing to pay this much for that, because, you know, my bottom line is gonna get improved by this much and all that. So I think that this, this, this, this, this is one of those things that well, I don't have a good answer for that. interoperability would be nice. How do we achieve it? People have to want it. And, and oftentimes, the sort of the distance from the actual interoperability technology to what people perceive as is the is the behavior of an information system is too, is too low. Okay, so let's come back to this no code, low code, part that you suggested. So let's say. So let's say that a medical provider can actually configure their system and can provide information about what it is that they're doing. And obviously, this is a great idea. But how do we make it happen? Who is the kind of arbiter of this infrared or the broker of this information, I mean, needed. In a way sort of the way things ended up happening on the web is that the original idea of the fully decentralized web in a way got bastardized a little bit then now we have sort of large technology companies that that can, you know, in some ways control the dissemination of information on the web. We have that We have search engine companies and we have cement, the social media, social media platforms. And in many ways these these companies what they do in a way sort of works against the idea of fully distributed, fully decentralized web. Because, you know, in a in the original scenario, anybody could could have been a publisher.

Anybody could have could have provided information about whatever. But we have now these kind of gatekeepers gatekeepers for that. I mean, technically, it's still possible, obviously, but but in practice, and in financial terms, you may not be that easy.

Petra Soderling  30:51  

Let's talk about standards. It sounds to me like you're arguing for standards and against standards. At the same time, you of course, yourself, were writing specifications that w three C, or maybe still are, I don't know, there are adjacent standards that affect the outcome of how we use the web, or how the web uses us. In this paper, 20 years ago, you were talking about, for example, Sun Microsystems, Genie for locating services, there was Microsoft plug and play for locating devices. And many of us at that time, we were working with standards, we saw standards to some kind of utopia for the future. But now today, I live in my house, it's a pretty smart house, but I can only control some things with my Alexa and some things with my iPhone, and I cannot control my Alexa with my iPhone or, or vice versa. So did the standards fail? Or did they not fail?

Ora Lassila  31:45  

Well, no, I mean, this this, this is an example of standards not being deployed. I mean, they did this is this is we have sort of we're in some ways, we're moved into this kind of post standardization. scenario. Standards are a great idea. And we've had standards for a very long time in many, many different areas of life. The semantic web standards that we ended up creating, and the most fundamental of these is called RDF, the fundamental representation standard. What we did there is that rather than doing what all standards pretty much had done before that which is saying, here's what you say, and here's what these things that you say, here's what they mean. So we decided we're not going to do that we're in fact, going to say, this is how you can say things to communicate what things mean. Which, which basically means that that we didn't, we didn't standardize what to say, we standardized how to say it. And our thinking at the time was that then this would allow sort of a kind of delayed commitment to to semantics or meaning of things, you could actually build things and then the definition of semantics would would, wouldn't have to exist. Before you start, start building something.

Now what has happened, and now you have to come back to my earlier comment about sort of the gatekeepers and large, large, large, large companies. So in some ways, we have moved into a situation where standards and official standards play less of a role in the world of technology, and it's more sort of a question is what is it that you managed to deploy? You get your technology deployed now that is the standard. Right? And in fact that some of the things we mentioned in that article 20 years ago, were not really standards in the sense that they would have been some kind of wide industry agreement. that yes, these are the technologies we want to use, but it was more they were more like one company's kind of attempt to build a framework or platform where certain certain things could happen. And if you have enough, sort of, if you have wide enough user base, let's say, then you can build, it's like a like a, what's the word that people use? It's like an ecosystem, right? So, so so. So you have technology that a lot of people use And you build it in such a way that other people can contribute to that. So they can build their own products that work with your product. And in a way, you've now managed to deploy your standard. It's not what we traditionally call a standard, but that's that that's how it works. Now, it's unfortunate that the world is sort of in a way the world has sort of split into a few different kinds of kinds of things. I mean, we've, for a long time, we've had the Mac versus windows. We've had iPhones versus Android. So they did these are these are sort of the way the, the world has kind of cleaved in half.

Petra Soderling  35:42  

So but you said, we're doing less standards, does that mean that we're being less innovative, and we're doing less new, new things? We're just applying and creating solutions of things already invented?

Ora Lassila  35:56  

Well, I mean, I, that is an excellent question. Because I think that the the sort of the question of innovation is a tricky one, when it comes to standardization. I mean, in many, many people see standardization actually a hindrance to innovation. Because standards traditionally have, well, there's been like, two ways to build a standard. Either either you say, this is what the world looks like, and it will not look any different than this, no matter what you do going forward. So you have to kind of conform to this, this this particular particular standard. Or you say, we have managed to anticipate all possible scenarios about the future. And this is the great standard that we have crafted for you, which is a highly unrealistic scenario, right? So So in a way, sort of standards have always be seen as restrictive, you have to restrict something so that everybody kind of conforms to something so that we get the kind of the economies of scale and the critical mass for for for things. What happened. And if we look at some what happened in the 90s, and then the early 2000s, is I think we saw a fundamental shift in how standards are done. So used to be the standard standard, we're seeing a something like hey, let's standardize these so we can then all play. And and I think that in the last couple of decades, we've seen standards move more towards a situation where like, we're let's see if we could write the standard in such a way that we can kind of screw our competition. And so when a long time ago, when I was working at Nokia research, the head of head of Nokia research professor Johan he calls, he would always describe that the proper way to do standardization and it was like a cake. And standardization is what you do when you want to make the cake as big as possible. And then competition decides how large of a slice of that cake you're going to get. And he or he always saw standardization as a sort of a means of actually building a market building an industry that then later on competition would be used to decide who gets which piece of that, that kick, so to speak.

Petra Soderling  38:34  

Yeah, we're talking about software, but I can't help thinking of semiconductors while you're talking. So if I'm using semiconductor as a standard, which it's not, but it's kind of a, it's a defined platform that people can use for building things. But the actual industry for semiconductors has reminded me small in a way, you don't have a lot of competition in semiconductors, they're pretty similar, only a few companies in the world make them. So in that sense, the semiconductor would fuel innovation, but not in the semiconductor industry, but in other industries. But

Ora Lassila  39:10  

that now that is a that is a really interesting thought, in the sense that, that in order for innovation to happen somewhere else, there has needed to be consolidation, sort of intellectually speaking, consolidation in, in another industry, to to, to, to build a platform that has the has the scale. So that innovation innovation is possible somewhere else. That's a that's an interesting thought. I mean, we we really thought during the early years of the Semantic Web, but what we would do is that we would really build these mechanisms that would let them other people use these technologies. For our Other things for for for for innovating, innovating, innovating new things. And I think that well, what, what ended up happening is that maybe the Semantic Web didn't exactly happen, just like we described in that in that article. I mean, many of those things did happen. But maybe the full blown scenario did not happen. But the Semantic Web technologies laid the groundwork for modern knowledge graphs, which everybody now wants to do. And nobody actually remembers that. That's, that's where they come from. We're still using the semantic web standards, though.

Speaker 1  0:12  

Welcome to deep pockets. The podcast for exploring how basic science, Once created in a lab and funded by public means is fueling the economy with completely new private industries. deep pockets as created by Petra Soderling.

Petra Soderling  0:27  

This episode of deep pockets goes right into the heart of what I wanted to convey when I started planning for this series. Winners and Losers Do not tell the whole story. David Wood futures to catalyst author and it singularity Korean phrases it as follows

Speaker 1  0:46  

the story of the evolution of smartphones is fascinating in its own right for its rich set of characters, and for its colorful set of triumphs and disasters. But the story has wider implications. Many important lessons can be drawn from careful review of the successes and yes the failures of the smartphone industry. When it comes to the development of modern technology, things are rarely as simple as they first appear. Some companies bring great products to the market true. These companies are widely lauded. But the surface story of winners and losers can conceal many twists and turns of fortune. behind an apparent sudden spurt of widespread popularity, there frequently lies a long gestation period. The eventual blaze of success was preceded by the faltering efforts of many pioneers who carved new paths into uncertain terrain. The steps and missteps of these near forgotten pioneers laid the foundation for what was to follow.

Petra Soderling  1:45  

In this episode, we discussed with David Wood, who held core positions in the executive teams behind Europe's pioneering tech companies, Scion and Symbian. What were those early days like? Welcome to the show, David.

David Wood  1:58  

Thank you, Petra, it's my pleasure to be here.

Petra Soderling  2:02  

In your book smartphones and beyond, you describe the the rise and fall over Symbian OS, once powering the vast majority of the world's smartphones. The story of Symbian starts off as the story of Saigon in London in the 1980s. You worked at Saigon from 1988 until 1998, when it gave birth to Symbian, what were those 10 years like?

David Wood  2:28  

Well, the Scion was a great experience, a great place to work. But it was also a place of intense hard effort. With the periods of intense hard work often continuing far longer than anyone had predicted or wanted. We were very ambitious in what we were trying to do, we were creating the next generation of handheld computers, as we saw them. That hard work that we ended up doing arose from our own ambition and our commitment to creating these breakthrough new product categories and product platforms. And along the way, to these eventual successes, there was many disappointments, delays, doubt, and even despair, before the disruptions finally resulted in significant products and markets success that we'd hoped for. And one point about these delays is that they used that much more cash than had been anticipated. It was only because science still had previous generation products doing very well in the market. And these sales continued to hold up well, higher than we had any right previously to expect. That was the only way that could keep on funding what we were doing. And if the sales have dropped off if the trajectory of sign would have probably been very different, and Symbian probably would never have existed. And I offer that example, as part of a general pattern that applies far wider than just a single company sign. With disruptive technology that takes time to develop into a useful, reliable form. There's often a long disappointment before the actual disruptive breakthrough. Now, they might ask, Well, what was it about science that did allow us in the end to create several generations of world beating product families. And it's difficult to prove any cause and effect. And I'm dubious about many success stories that people write about their past history. Often, they were quite lucky and they forget about the luck and they just said I did x and I was successful, therefore x must be responsible for my success. Whereas I think it's often much more mixed than that. I have given a lot of thought as to what was special about Symbian when we were successful, and it grew out of the original culture at Scion, two, which gave birth to Symbian. So perhaps if you'd like I can offer a few comments on that pioneering culture because I think it's relevant to many other companies. And that culture came from Imperial College in London, where the founder of Simon, David Potter had been a lecturer in physics and some of his core staff had joined him. And David Porter, by the way, went on to be the first chairman of Symbian to there was a respect for learning respect for knowledge or respect for intelligence. But it was very much a practical intelligence. It wasn't knowledge for knowledge sake or knowledge for cleverness and sake, it was always oriented around real world applications. And it was tied up with a strong desire that computing technology should be useful for not just computer geeks, but for courts, ordinary people. And in many discussions over the years at Scion about what we should include in various products, people would say, No, we can't do it like this, because my manana or my aunt, or my mother wouldn't be able to understand it, we have to make it simpler, we have to make it more obvious, we have to make it more intuitive. So that passion for usability, along with a passion for reliability and robustness, that no data that people entered into these devices should ever be lost. And that grew out of some hard practical experiences in the early days, when some of the prototype products had been used by the partners, wives or others, are they some of our management team, and they fell in love with the devices, they enter the data into it, and then suddenly, the data was all gone. And it was a terrible, agonizing thing that they reported back, what's happened, what have you done to us? And we analyze and we figured out that, you know, the batteries had failed, or just the wrong moment, and the algorithm in that court with that particular failure. And so we learned about the importance for data reliability, we learned the importance of what David Porter, the founder of Scion called frugality, which wasn't just being frugal with money, it was being frugal with energy, that we should not rely on powerful batteries or powerful hardware to solve problems, we should solve problems with efficient software. And we had a saying, more gives, but gates takes away referring to Gordon Moore's Law, about improving power of hardware every 18 months, there's twice the power for the same cost. But we also saw that Bill Gates's software tended to get slower, faster than the hardware got faster, if that makes sense.

In the midst of all of that, we took the long term view, we weren't just designing one product, we were designing a platform that would last for a long term. And we weren't just looking after the present generation. You know, when we were building a new generation, sometimes there were arguments, some people said, put more money into maintaining the present generation, that's where the cash is coming from now. But on the whole, the company said, Yes, but there's a limited runway there, we have to switch over to the new product. So there was a long term view. And that was tied up with something else. I think it's very important. We were very deliberate about strategy. We didn't just do what came into our minds at any old moment, we took the time to set aside the issues of the day to set aside the firefighting as it were to separate ourselves off and think hard about strategy, out of all the things we could do, which were the right things to do. And sometimes that meant giving up the things that previously had been very important to sign, for example, had made his early money, a lot of it by selling games and software for some of the earliest home computers. But science on the market was changing in a way that emphasize more first person shooter and fast action games rather than the cerebral games like chess, and Scrabble in backgammon that day, science culture felt more comfortable with. So this conscious decision not to invest much longer in these gaming programs, but to switch into different fields. And that's how the Scion organizer, and the handheld was made. And later on, when we were trying to upgrade some of our products, we had 1000s of ideas how to improve each of the apps, but we were quite careful to pick the few that we thought would make the biggest difference. So switching from a product called a series three to one called the three A, which was the first really successful product that we made, it sold a million devices, we decided to focus on just a few applications, including an agenda app. And all the other ideas we heard about doing lots of things to the other apps were carefully pared back, we had another saying which was we have to be prepared to kill your puppies or strangle your puppies in order to ship a successful product and maybe could kill slugs, things you don't like but you have to be able to let go of things that you wanted and loved in order to be successful. So it's a discipline there too. For these are some of the characteristics of sign which I think self sign well and which were taken for a while into Symbian, which was given rise to maybe I'll just end with one other kind of general principle. Which is that I think any long term company, a company that wants to survive in the long term needs to do three things very well. You need to be able to focus on the present product, and I need its meets, you need to be good at keeping your commitments, operational excellence, it's often called managing cash flow managing projects, you also need to be good at spotting the increments how you can make your current product significantly better, making it faster, like we did when we moved from the three to the three a and later when we moved up even larger to the series five, which had a 32 bit operating system instead of a 16 bit operating system. And that became the basis for sabinus. So there's an incremental step ups the foreseeable future, as I call it. But you also have to be ready to spot the elephant on the footpath the things that you weren't expecting the thing that changes the game. And Simon did have the ability to see that as well. We saw that an elephant was coming for the space in which we lived in that elephant was the mobile phone industry. And that required a lot of strategic reflection as well. What do we do? Do we ignore it? Do we just double down and keep on doing what we already did and loved? Or do we adapt ourselves in anticipation of that emerging smartphone market? And that's what we decided to do. It was a difficult and hard decision. But I think one that was profoundly correct. And it was in part because we took the time to do this strategy. Well.

Petra Soderling  11:30  

You mentioned taint games in terms of software, sort of early apps, but video games and game consoles that came out in the 80s and 90s. Were also groundbreaking in the hardware form factors. Did you add sly on consider gaming as a hardware template as well.

David Wood  11:49  

While we had different form factors in mind, the first was a kind of a rectangular block, which had a simple ABCD keyboard, and which was the most compact would fit in and people's launch shocked pockets. It was the original organizer, and the organizer, too. So that was the template. And yes, there were a few games on there, too. But it was when we moved on to the devices that were more like a clam shells, I'm going I'm doing the actions. But of course, they won't be conveyed in the audio, more like the palm tops now. And they were quite radical. In some ways with these palm tops, we were brave enough to change the conventions about the QWERTY keyboard nowadays, everybody is quite happy to have alternative keyboards. But it took quite a while for us to convince ourselves we could be slightly quirky, but not completely Qwerty. And on there, we had some very successful games, including a very good implementation of chess, originally, and a very small number of bytes, highly efficient. And so there were still some games. And then we had games that took advantage of the graphics on there. So it wasn't particularly Fast and Furious, though there were a few games like that. It was games that engage the users. And still people tell me they play emulations of these games today on some of the devices. So that that was a long term view. And like shoulders, that add ons were important and add ons that we hadn't anticipated in detail, which meant it was important to be open not to dictate in advance what would be the successful apps and what wouldn't be the successful apps. Instead, the community as a whole, the developers would be important. And that's why we had a series of developer conferences going back to 1992, which there were always games developers, as well as people who were trying to develop business apps.

Petra Soderling  13:40  

So in a way you were envisioning the app economy that exists today, and laying the technical and strategical foundation for what was to come later.

David Wood  13:50  

That's right. And I still have my notes, my PowerPoints for that 1990 to the present a conference when I gave three different PowerPoints. When we all learned to PowerPoint, we took two weeks off, so the people are going to be involved. And we discovered this amazing software, which is still why I like using PowerPoint to this day. At this first conference, we had, I think a 20 developers, so it was a small stepping stone. But the one later, one year later 1993 it was up to about 40 or 50 developers. And people realize you know, there was a marketplace here for games. I don't think any of these original developers became particularly rich, but assured what needed to be done. It also led me onto a trajectory of supporting the developer. So this is when I had my first public a email address at that stage, dw to kickstart computing.co.uk. As a result of being on this online, a bulletin board system in America had compuserve. In Europe, Kix cx was quite popular. And although in my day time as it were, I was developing so were increasingly in my evenings, I was answering questions from developers, how could they do this incredible thing? connect to the advocate the alarm server, how could they connect to the set of world cities and countries. And so I would provide interfaces to the developers. And it showed me many of the requirements for looking after developers, which I developed further in my time, looking after many of the developer relations inside Symbian.

At the same time, when Simon was building the 32 bit epoch platform as an upcoming smartphone operating system, Apple was there, Microsoft was there with their own PDA visions. You have an interesting anecdotes on Apple's Newton device. Here's a quote from your book smartphones and beyond.

Speaker 1  15:51  

The first Newton went on sale in 1993. Five years later, with original company founder Steve Jobs back at its helm, Apple discontinued the entire device family sales had been extremely disappointing. There was so many unwanted Newton's that infoworld blogger Robert cringy could claim in July 1995, that Apple had been forced to dump 30,000 brand new Newton message pads in Los Angeles landfill, where they were crushed under the treads of a bulldozer.

Petra Soderling  16:20  

This is pretty remarkable. And just tell us how turbulent these early times were.

David Wood  16:27  

There were many people who invested long and hard to try and create what we might now recognize as the iPad or the iPhone. But it was too early. And it wasn't obvious that it was too early. So the team at Newton had many brilliant engineers, and it didn't quite work. Quite a few of them went on to work later at another company called general magic. And they did some remarkable things too. But also they failed as well, it was just too hard to get everything right. And General maju curve admitted subsequently that they failed to spot the importance of web browsing until too late. So there were a lot of efforts to try and break in. And again, it conveys it confirms the pattern that with truly disruptive products have in this kind of exponential curve in capability, which lags behind for a long time, our expectations. So we overestimate what may be possible in the short term. But then we tend to underestimate what finally happens in the longer term. And that was our case as well. We knew that the market would eventually be launched. We didn't quite imagine exactly how large it would be.

Petra Soderling  17:36  

So that Apple story was from 1993. Then in 1996, three years later, Microsoft launched Pegasus their own 32 bit operating system. But you had met Bill Gates even earlier than this. Tell us what happened.

David Wood  17:52  

Bill Gates said came to see Simon in I think 1989 and the reason for that was that day, we were inside, doing our laptops for the one called laptops and we call them mobile computers. And we were doing too with our own ybox software 16 bit 16 bit predecessor of some universe, but the view was we should also as a safety do one on dos. And there was two doses at that time. That was Dr DOS, digital DOS and MS DOS. And for a while we thought Dr. Dos was technically better. The Bill Gates heard about this and he went on a tour around Europe. And this came up later in the FDA know the American inquiry into monopolistic practices abuse of power that he told many peak companies. He made many promises about MS DOS which were turned out to be unfounded. He came to see sign to convince sign up to use Dr. Dos and MS DOS and one of my programming colleagues, Robert Ramsey, said to me Hey, I've just seen Bill Gates. And I thought Bill Gates, I've heard that name before. And I said to him who's Bill Gates. And that got written down in the courts file for cya. And there was quoted back to me many times subsequently, I have to say, We sat there later we all studied Microsoft and Bill Gates very carefully, we realized he was a competitor but in some ways also an inspiration. Because what we wanted to do with being provider of the preferred operating system for mobile computers, wireless computers, was like being the Microsoft of that space. So we did learn a lot from Microsoft along the years. And my views on Bill Gates have changed a lot as he has mellowed.

Petra Soderling  19:51  

What a story. You've described the history of science as a device creator, but then towards the end of 1990s you decided to move Towards software licensing model. One pivotal date in this storyline is Wednesday, March the 13th 1996, when the Nokia N 9000. communicator was launched. This was like a PDA, but it had a cellular GSM connection. It looked like a real Gordon Gekko cell phone, but it had fax, email, and calendar, all of the modern business tools, tell us how that product was received at Siam.

David Wood  20:29  

So in March 1996, we were already under some Distress because our project to create the 32 bit operating system have taken much longer than anyone had expected. And it still was by no means clear. There was a lot of pressure, hey, we got to speed up. And we looked at what the competitors were doing on the whole and nothing really worried us until this Nakia 9000, communicator was announced, probably I'd save it. And the photographs appeared. And my colleague, Bill bachelor, longtime science, programming soprema, he took a picture, put it on the kitchen, and I say the kitchen because most of the development was done on one floor. And he wrote on it, that competition has arrived with a big exclamation mark. So we could see, you know, the world was changing. And about the same time as Sunday Times journalist came to see David Porter, the leader of sign, and he asked, provocatively well, Simon, are you going to die like apple? Or are you going to die like IBM. And the view was that Apple had the best technology but was being outclassed. This was on their desktop market was being outclassed by inferior software by Microsoft simply because it was available on a much wider range. And the journalist was saying, look, you may have the best software the cleverest guys, but you're going to be outclassed because there are other software coming along by other providers. So then could sign take the leaf out of IBM, which a had the relationship with Microsoft, and then IBM first PC, there was IBM hardware and Microsoft software. But IBM had also been perceived as dying because they Microsoft then gave away the same software to multiple other companies, many of which then outclassed IBM. And it's all ironic because of course, both Apple and IBM didn't die. They had their near death experiences, IBM PC as well, one of the world's leaders for a long time, and Apple, of course, thrived. But this question was very much on Simon's mind, how could we cope with this increasingly competitive world? So could we share the costs of a building the software, could we get more people using it apart from sign and that's eventually after a long strategic review, involving people like Steven Randall, who we engaged as a contractor, he had been previously the founder of another long forgotten, handheld, a computing company, and then use the different operating system, g works g OS from geo works. So he had done this operating system, and it hadn't really succeeded. But he came to give advice to Scion and look at the pros and cons of licensing the software. And he pointed out there would be other markets, including something he called Smart hyphen, foreign, this was late in 1995, the first mention of that in my archives. And he said, Well, the market is hard to estimate, but it's probably going to be large. And we should probably tailor our software in a way that it would be capable of running on these other devices as well. And to do that, we're going to have to assume a wider ecosystem that wouldn't just be sighing using us. We already had some success with a different manufacturer called acorn, which is also the parent company of ours, that of course, it's done much better in the long term. But acorn took some of our science software and repurposed it for educational purposes, doing things like leaving out the password protection and the word processor because misty for school children would probably password protect things they shouldn't password protect, and so on. So that was a simple customization. And so we were going to allow more customization. So we started talking more widely, we hired a few salespeople, who had actually been our hardware engineers or marketing specialists in the past, they moved into this new role. We talked to foster Phillips, which is one of the leading manufacturers of mobile phones at the time, and they had some very innovative and exciting ideas. So we signed up a project with Philips. And then we started talking to a strange Finnish company called Nakia. And we could see there were different, you know, at the same time that they were doing these Nakia 9000 communicators with GE works. They were considering their long term strategic evolution too, and it was clear that they were actually frustrated by some of the limitations of the GR software that was in their first 9000 It was a 16 bit software, they really wanted to get to 32 bits. Otherwise it was too hard to program advanced applications. So they were looking around, and then we talked to Ericsson as well. And by December 1996, we did another careful off site strategy review. And we rather bravely decided we were going to D prioritize the product segment of our science flagship device, they discuss their separate, unconnected is a PDA, we're going to prioritize instead support for mobile communication devices, particularly small mobile communication devices. So very brave strategic decision going against our current customers strongest desires. But again, I think it was the right long term decision, but we knew that would require even more funding. And that's why the idea of Symbian came around as spinning off Scion software a separate division and gaining funding from the lead customers, which were Nokia and Ericsson. slightly later Motorola, who put in funds that valued the new Symbian company at 100 million pounds.

Petra Soderling  26:14  

I remember in the early 2000s, the red herring magazine, ran a cover page article titled men behaving badly. They're referred to m e n, that's Motorola, Ericsson and Nokia joining forces behind Symbian and leaving other OEMs out of the game. Do you remember this type of press coverage?

David Wood  26:36  

Well, I do remember that red herring wrote some nice articles about us, they had the understanding that there this was a long term disruption that was going on and that Symbian was in a leading role. We were quite careful at the beginning not to let too many cooks into the kitchen at the same time, or as Anders vestre, leader of Ericsson famously said, we can invite more people into the board once you start moving, but let's not have too many different data navigators at the very beginning. So we were careful to exclude some of the other companies that we thought didn't have the same long term vision or the deep commitment to this market, but not to restrict them as licensees, we would talk to anybody who wanted to talk to us, we would license the software, if we saw sufficient signs of commitment to at least one product one project with us. But we would keep their initial governing team small with a view to expanding it. So Motorola came on board quite soon afterwards. And then Panasonic, which at that time, was the world's number four. And then Siemens which is also one of the leading foreign manufacturers. And Samsung came on later to so we did expand, but not in a way that would be too disruptive to the leading management team.

Petra Soderling  27:54  

Yeah, all of these names that are basically gone from the smartphone industry now, Philips, Siemens, Panasonic and others. Quite one name has not gone though as arm. You talked a little about the role of semiconductor in the way you approached Nokia arm was close to your headquarters if I remember correctly.

David Wood  28:14  

So we were geographically close. That's right arm was in Cambridge, just off the MLS in about an hour's drive from a London. But there was a background as to why the relationship between arm and Symbian became strong, which was going back to the design of our 32 bit operating system and the device the series five that would run on it. And Previously, we were unsure what kind of chips to put in their map. What made science successful in part was the design of a hardware platform in parallel with the design of a software platform a bit like what Apple does. It designs the hardware and the software in tandem. So we had brilliant world class hardware designers like market greerton and Mark van Vich. and world class engineer manager Mike Ken McAlpine, all of whom have gone on to great careers elsewhere since these days. And in that decision as to the hardware platform, there's a hard decision as to what kind of risk ri SC architecture to put in there. So Callie Myers, who became the fastest CEO of Symbian and Mark Gretton spend months investigating lots of different competing risk architectures mainly from Japan. And arm was a late entry into this set. And we eventually decided you know, on was the best. This was a wonderful example of long term strategic vision. I can't claim any credit for that personally, I had no idea what was going on there. But this cemented the relationship. And without a lot of kindred spirit with the arm executives the Symbian and arm executives would occasionally meet to exchange ideas to the to executives grouping together on went on to be a much more successful company than Symbian ever did. But we did Aspire To the same kind of success and wasn't just arm there were other aspects in the stack as well. We depended a lot on Texas Instruments. And they had some farsighted people who created a wireless division inside Texas Instruments to use the ARM architecture to design their own omap chips. And Nakia separately had already decided that they were going to use arm so that was a separate decision. And when Nakia talked to arm and said, Well, what should we do about operating system that people in arm kindly said you should at least consider the guys from Symbian. They've got some very attractive devices under play. So there was a mix of companies involved. And we had a sufficiently good report which served as well.

Petra Soderling  30:49  

Earlier you mentioned how PDS and devices need to be usable by our children and grandmothers and others. One of the things that you describe is about Symbian is their pioneering technology is not just technology, it's about the first use cases, beta testing with users laying the first commercial opportunity. Tell us how this was first laid out in Symbian.

David Wood  31:13  

So of course, we were interested in technology, we were technology geeks, but it had to be technology that would appeal to ordinary people. That was our guiding principle. You shouldn't need to understand software in order to use these devices. But we realized what we would create would be complicated, we realized that there would be applications, we realized there would be a variety. And what we created we realized early on that one of the reasons for manufacturers like to talk to us was that we gave them some leeway in terms of implementing their own product conception, different I have to say from Microsoft at the time, which was much more firm and its views they had decided how the software should look, they had clear views on how the hardware should function. And that was limited. And although Microsoft were very effective in what they did, many of the foreign manufacturers wanted more leeway involved. So we decided we would aim at supporting a variety of licensees with openness in our system. So we architected it with openness in mind, it was not a one design fits all model at all. Although this would be the same operating system. So applications written for one could be easily adapted for a different device family, as we call them. We created these device family reference designs, df rd, an unlovely acronym that they went around for a number of years. And it was also in a tied up with ensuring that undue costs, their software would run in a lower and lower cost devices and therefore reached larger and larger markets, but would still be perceived as easy to use. So in that way, we envisioned that in due course, everybody might be using Symbian smart smartphone. Bill Gates famously had said back in 1976, or thereabouts, that the vision for Microsoft was a microcomputer on every desk and in every office running Microsoft software. So around about the year 2000, we had the view that the vision was a smartphone in every pocket running Symbian OS. And we didn't literally mean every pocket of all calima was our CEO sometimes did go on stage, with a different smartphone in every pocket of lots of pockets he heard and he would drag them out and show here's look at this incredible device, look at that incredible device. But we meant that everybody in the world would have such a device. And that was a different mindset. And it's sort of come true that almost everybody in the world nowadays uses a smartphone. Sadly, it was not a Symbian OS that was in there. It's other operating systems that came along and displaced those eventually. But these other companies saw the scale of the market on account of what Symbian and Nakia and our other licensees achieved, and that drew other very powerful companies in there. And they shared the same vision that it had to be usable by the mass market. And it had to be something with a long term retention.

Petra Soderling  34:27  

I remember the year when someone announced that this is the year the number of mobile phones exceeds the number of toothbrushes in the world. But would you say that the fact that you let OEMs decide the form factor became the demise of Symbian? Should you have been tougher or stricter?

David Wood  34:44  

Well, that's a hard question. The OEMs did things that they actually added real value to the product, which we could not have done by ourselves came about more because we lost All power than the early days in the beginning, their licenses had a great respect for the software we developed. They loved what was done in the science series five, they could see it was world class and world beating. But what we needed to do next required even more investment and we weren't able, in retrospect to fully appreciate the scale of what was ahead. And in my views, we probably under invested even though there was lots of money, and even though we grew quite quickly, probably we should have found ways to grow even faster. I wrote a document in 2001, which anticipated by 2007, there would be 100 million Symbian smartphones created. And I fed off ideas from some of my colleagues such as Steven Randall in envisioning a roadmap for the next six years. In 2001, there was less than a million, so it was quite a step up. And that was turned out to be correct, we did reach 100 million shipped by 2007. But in that document, I said, Well, the question of what operating system will power these new devices? It's a billion dollar question. I said, a billion dollars. That's why it's worth investing a lot. Turns out I was wrong by an order of magnitude. The question of the successful operating system in these devices is more like a trillion dollar question, because after all, Apple solved that. And on the backs of their success with iOS, they became the world's first trillion dollar public company. And Google did a great deal also, by having its Android, and they too became a trillion dollar valuation, if we'd really seen that we could have argued for more investment. Well, what happened is that we weren't able to deliver all that we had promised. And we fell a bit behind. And there was kind of despair by some of our licenses at our capabilities. And at a strategic off site in 2001, held in a place in the suburbs of London called Nightingale lane. So this was project Nightingale, that we decided we would give away some of the power of the upper layers to the manufacturers, and we would concentrate on the lower layers. And this was sort of a mistake, because the manufacturers although they were capable in software, they weren't as good and software as we were. And although they did some fine things in the end, the disconnect between the upper layers such as makios, series 60, also known as s 60, or the corresponding things and other branches of the same universe, cause more and more problems over the years. So it was a mistake in retrospect, how we could have avoided it is hard to see we must needed would have needed some way to generate even more funding and sine at this time were apprehensive. They had already invested a lot of their own value in this company. And if it had failed, they would have suffered greatly, so they were reluctant to put two more into us.

Petra Soderling  38:00  

Talking about money and being in 2001, Nokia launched the 9210 communicator. And then in 2002, Symbian shipped a 3g enabled version of this NBN operating system. This podcast is called deep pockets since we're talking about how government investments view our private industries. And of course, smartphones need a telecommunications network in order to function. And many of those networks were built at the time with money from different governments in different countries. To what extent would you say public regulation and financial support in the early 2000s helped the smartphone industry?

David Wood  38:40  

Well, there would not be a smartphone industry if there had not been large government support for the GSM standardization effort and the networks that came along. My echo the analysis of the economist, Mariana mazzucato, who has written in her book that the real father of devices such as the iPhone is their public government, defense spending, and so on, that created many of the standards on which these devices benefited. So of course, we software engineers and hardware engineers like to feel that the credit should belong to us. But it would not be possible without the wider infrastructure. And so, of course, the GSM networks were important. But there are many other standards and many other bits of technologies such as the standardization of the World Wide Web, which of course had been funded initially by the sound project, which had housed Tim berners Lee as he put his initial view, ideas on HTML into the place, and there was lots of others as well. regulation of the devices was important to before a device was allowed to connect to the wireless networks, the wireless operators insisted on various tests. That was important to Avoid the network being damaged. Arguably, I think the some of this went a bit too far. Some of the network operators, for example, didn't really like third party applications, they were worried that if a user installed a third party application onto a device apart from a limited set that they knew and loved, that it would add to support risks, people would ring up the support staff of the network corporate and say their phone's not working. And it would be due to a third party app. They were worried about security risks about the spread of malware, which was already causing a problem on PCs. They didn't want that on their phones. And I have to say the operators were also worried by the risk of revenue bypass, they made lots of money from text messages. And they were worried by apps that we we nowadays call wats up that would allow people to send messages or Voice over IP calls that would take some of the revenues. So these network operators resisted having too many forms with too many apps. And they pressurize the foreign manufacturers to emphasize simpler devices and lower costs. And that turned out to be a mistake. Apple stood up to that Apple had the confidence that they would just tell the network operator what the form would look like. And then there was successful in a way that ultimately none of symbionts, licensees could do. That was an interesting story. One more story here is that another aspect of government regulation, which caused us problems was to do with cryptography, which was the software that enables a lot of the security systems and cryptography was classified as an armament ammunition, and therefore was subject to export control, which meant that we had limitations in what we could do with our source code. And we wanted to ship our source code, for example to some licensees in China, to develop software, their own devices, we wanted to get a Huawei and other companies on board. And I don't remember all the details now. But over the years, we had a lot of hiccups in that, until we finally made our s cryptography software the same as was already open source. And then then argument went away. Oh, and I should mention one other thing in which government policy did help us. The government in Britain for a long time encouraged actively encouraged the migration of developers from all over Europe and all over the world, indeed, to come to London. And if I look at the leading managers in Symbian, in kind of middle phase, so many of them came from Europe or further afield. And sadly, nowadays, it's not quite so welcoming, because of a political culture in Britain is quite intolerant of what they see as undesirable immigrants, even though these immigrants could actually earn huge amounts of money for the British taxpayer. So again, the government policy kind of a significant impact on whether these devices are successful or unsuccessful.

Speaker 1  0:17  

Welcome to deep pockets. The podcast for exploring how basic science, Once created in a lab and funded by public means is fueling the economy with completely new private industries. deep pockets is created by Petra Soderling.

Petra Soderling  0:35  

Quantum mechanics is a field of physics that has been studied since the early 19th century. The term quantum mechanics was coined by three German physicists in 1924. Fast forward from quantum mechanics to quantum computing. Quantum Computing began in 1980 when American physicist Paul Benioff proposed a quantum mechanical model of the Turing machine, which in turn is the first theoretical model of a computer by English mathematician Alan Turing from 1936. I wanted to start with this intro to highlight that quantum computing has been around for some time. And it's a great example of a technology that has international publicly funded routes that is now generating profits to private businesses around the world, or is it? To discuss this with me today is Andre M. Koenig. From entanglement capital, a private firm that makes investments into quantum information science ventures, Andre started quantum computing at MIT, and holds an MBA in economics from the University of Chicago Booth School of Business, as well as a master's in business from ICM School of Management. He speaks English, German, and French. So is a perfect representation of the transatlantic landscape we're discussing today. Welcome to deep pockets, Andre.

Andre Konig  2:04  

Thank you so much, Petra, I think every introduction also needs to mention that Einstein thought quantum mechanics to be spooky. And hopefully you and I can bring some order into that quantum mess. It's my pleasure to join deep pockets here from South Beach, Florida, where I'm overlooking the Atlantic towards Europe.

Petra Soderling  2:26  

Awesome. That sounds very nice. So the reason why and why did you hear is because of your experience as an investor, but you run many other quantum related businesses. So before going into the investment side, can you tell us about the other enterprises you run,

Andre Konig  2:43  

I run three companies, all of them. 100% focused on quantum information science, as we call the field or quantum tech, entanglement capital, a tiny little investment fund with the purpose of operating a startup accelerator for growth stage, quantum tech companies and two other companies that kind of feed into my investment activities. Interference advisors, which I call the Gardner of quantum hoping not gardeners not going to get angry at me for that. But for the last three years, we've collected about 30,000 data points on who the vendors, startups, investors, use cases, product roadmaps and so forth, are within quantum tech Clovelly. And we offer that data as well as analysis and custom inside reports. So recently for the Department of Defense here in the US, to our customers, one quantum, which is a global community organization and quantum technologies organized by chapters we have 21 or 22 chapters. Now, from the mountaintops of Nepal, all throughout Africa, Latin America and North America, Europe. We also have a couple of topical chapters a woman in quantum which holds a bi yearly conference to wohnen Quantum summit, which is the second largest conference and quantum technologies by attendees, but also by engagement where reach 150,000 people online over this event. And the goal here is really twofold. Number one to fight hype. There are a lot of fake experts and one of the experts in quantum technologies. And as Einstein said it is spooky. So you really need to be committed and invested into quantum to understand it and bring value. But we welcome everybody who is on you don't need a PhD from Stanford or Oxford, or some other fancy university to be successful in quantum and that's what we're hoping to do with one quantum through events conferences that I mentioned by a mentoring program that we run with about 400 participants right now, career services and career fairs, as well as ultimately projects. And back to the mountaintops of Nepal, a group of five students there just ran the first quantum computing project ever in Nepal and add one quantum. So we're very excited about that.

Petra Soderling  5:23  

Wow, that's pretty breathtaking. Do you sleep,

Andre Konig  5:27  

I don't sleep, you can hear that in my voice. I've been dragging on a cold for the last three weeks. But it's it's a fascinating field, it's a privilege to work in this field. It's still a very small field, I think we'll be talking about the ecosystem later a little bit as at least from a commercial point of view. So it's not easy to make a career out of it. And, you know, investments on the investment side, obviously, are still very small and modest. So but very exciting. Very fast evolving, and ever changing, especially in the second half of this year with with some big deals and activities. So not much sleep. But we'll we'll do that when we're sitting on the beach show one day, hopefully, rather than at the desk here.

Petra Soderling  6:20  

Yeah, so let's talk about the beach. So in my intro, I kind of alluded to the fact that are people actually making money. So you do make private investments into quantum ventures? What do you look for when you make these investments? What are the criteria for investing?

Andre Konig  6:37  

So I said that my other two businesses kind of feed into my investment activity. And that is for very specific reason, one quantum, our global community organization with 1000s of members, you can think of that as a source of deal flow and deal flow is one of the critical success factors for any investors. How do you get the first look at a company How do you have credibility but also reach and exposure to different founders, maybe even before she or he knows that there'll be founding our company because they are still studying or working in a different field. So with one quantum, we've really been able to build up a fantastic organization that gives us amazing reach, but also a really deep insights into global ecosystems, which is amazing for deal flow, interference adviser through the due diligence, the data, the insights, and so forth, allows us to really create benchmarks and facts, an industry that has very few benchmarks and facts just through the nature of its youth. If you're trying to assess a company in a specific sector or application, then you need something to kind of compare it to, and really validated spice baseline. And that's what interference advisors allows us to do. Beyond that, we're indeed 100% focused on quantum technologies, which is very unique. I know, two or three other investors globally that have that focus. Other investors, you know, more generic deep tech investors or large venture capitalist fund. So so we do look at that. We do look at specific stages of startups, we do want to help a little bit I mentioned, an accelerator program that, that we operate, where we hope to help companies that already have a prototype and MVP, maybe a couple of research partnerships or, or even the first customer or to hone in on their target market, their their positioning their differentiation, and then build up their teams, their sales and marketing processes and collateral to successfully what investors call a demo day. At the end of a three or four months program, be put in front of the lion cage, a big room full of investors hungry for the next deal, and be successful and raising a 1520 $25 million follow on round. So that is our model. And we're very data driven. We have a number of different assessment frameworks to assess the quantum technology, technological readiness level, the business maturity of a startup, and many other things. So we tried to be as objective as possible in this spooky quantum world.

Petra Soderling  9:41  

So if you mainly invest in very early stage startups fresh out of the lab, isn't that very risky for you? is there other other ways that for you to kind of reduce the risk? I'm thinking of, would you focus on hardware or software or different types of ways of reaching the quantum state

Andre Konig  10:01  

Unfortunately, I studied economics at the University of Chicago Ross, you were so kind to mention in your intro, and literally the first thing we learned there is that the risk of the drives the rewards, so that was probably something I've been dragging with me for 20 years now, I don't mind the risk as long as I can, you know, control it as much as possible through having this early deal flow. And this early sourcing, through having all this data, these frameworks and due diligence concepts that we've established for quantum that are quite unique. And really through having a deep network and understanding of the larger ecosystem and its context. Of course, there are certain applications that might make more sense than others, hardware for the small fund that Iran is really largely out of reach. Maybe I could buy a couple cubits but but not much more. So we are looking mostly at software applications, but also at the other fields within quantum information science, like quantum sensing, quantum communications, and Quantum encryptions. While those markets might be smaller than the infamous trillion dollar quantum computing opportunity, they are also more mature with much more specific products, really more certain timelines and roadmaps and an initial commercial roll out. That is, you know, really a year or two years old already. And that's years ahead of what we see in quantum computing. So through the nature of being a very small investor, we are probably a little less risky by definition, since we are forced to look at a deal that we can afford. And these big moonshots is not part of it. If I had 100 million or more to invest, I certainly would love to take that risk as well, on one of those big machines.

Petra Soderling  12:05  

So I'm no expert, but I've been following this space for about 1218 months now. And it seems to be that most startup companies are currently making money by creating equipment that they're selling to public players, like universities or countries even. And then you have the super big ones like the Googles devices, realities that are building that are well funded and are building. So do you see the kind of landscape being in the future for the startups to being swallowed by these big companies? Or do you see them having their own business long into the future?

Andre Konig  12:45  

Present, I think any investor that invests into quantum computing specifically and expect liquidity event other than through an exit, is fooling her or himself. Commercial revenue is very far out, I can tell you that out of 500 or so startups that we track globally, in the space, very, very few have any commercial revenue. And if I say very, very few, you know, 10%, might already be too much. And if you peel back the layers of that onion, you discover that most of what's reported as revenue, there are grants from federal organizations, universities, and others. And then really, in our best case, scenario, research partnerships with certain after large customers, and we've all read about what some of my German friends at Volkswagen BASF are doing. The New York Wall Street banks like Goldman Sachs and JP MC, and some other large fortune 500 type of companies. They are spending money on this ecosystem and with IBM with regard to what some of the startups but that really is very, very small.

On the other hand, and you know, this is a fortress week to be having this conversation. Inq one of the most famous and well funded startups that closed us back deal six or eight weeks ago, is ringing the bell in New York just a few days to have its first IPO and bringing in large amounts of cash, that I frankly wonder where will they spend it Yeah, hopefully on me and some of my services, but even I can can't write an invoice for 100 million, the quantum universe is still very small. So there's a lot of cash coming in, through mergers through divestitures, we've seen Honeywell spinning off its hardware business and merging with Cambridge quantum computing, I think we'll have one or two surprises by one of the large famous names in the space before the starts, before the snow starts to fall. And not just here in Miami, but but in a more temperate climate zone. So a lot of startups that I talked to currently have offers on the table from one of the large vendors or financial partner to be acquired. I think a lot of these startups are still very skeptical about these offers, and feel they are under valued and tending to refuse them which, which is exciting. So a lot of money in the new quantum universe, I don't think it's it's really figured out yet where to go. But almost no commercial revenue at this point.

Petra Soderling  16:15  

That's so fascinating.

Andre Konig  16:17  

Unless you're IBM, and run the IBM Cloud, then then you're making money, but everybody else, I think it's, it's not at the table.

Petra Soderling  16:26  

So we probably have some listeners wondering at this point, what is what does it do? Why do we need quantum computing? What like, what are the use cases that I'm thinking that Okay, we have this quantum space, lots of money, lots of companies, but who is the end user? Is it gonna be finance, biotech telecom?

Andre Konig  16:46  

What Why do we need it? I think that is the best way to start thinking about it. And if I give you kind of the, you know, late evening, happy hour type of answer is because humanity is being faced with a lot of very big challenges from climate change to personalized health. Yeah, universal basic income is being discussed and more and more countries, but also security issues, and increasingly read about cyber attacks on some of our big companies, and even government infrastructure. And these problems when you dig into them, mathematically and computationally, all problems that lend itself to being solved by a quantum computer, simply through the way that a quantum computer performs a calculation. So it has nothing to do with the speed or the power, the performance of a quantum computer, which is a million times more than than a supercomputer. But similar to a fighter jet compared to a bicycle. It is not about the speed it is, you know the physical principle principle behind it, and what you would use it for. And it's these NP hard versus p p, hard problems, optimization problems, factorization problems, MonteCarlo problems that you find in new material research, new drugs, new medication, optimizing financial portfolios, traveling salesman, to kind of logistical problems via FedEx and their trucks on the road, or routing of packages in a 5g network that theoretically, we believe we can really solve. With this new technology. There's a lot of skepticism out there because we haven't done it yet. And many times, we don't even have the algorithms, the massive quantum mathematical way of describing these problems to a quantum computer. But that is the hope, then there are applications in quantum machine learning. A neural net is nothing else but an optimization problem. And there are security implications that I hinted at. Since current encryption standards, some make the argument can easily be broken by a quantum computer. It's a factorization problem from a mathematical point of view. And a big fear that China or that's rumored to have invested 11 or 12 billion US dollars into a national quantum program already, might potentially crack all of our infrastructure and data, or some other bad actor or hostile hag hacker, could potentially do so a lot of potential and a lot of maybes and hypotheses today. We really haven't done anything useful with them yet. It's all experimentation. It's all theoretical gameplay. Everybody firmly believes that we can solve these big problems that we're facing with quantum computing. And if that's the truth, then we are ushering the next industrial era with hopefully a future that resembles a benevolent science fiction movie and that that'd be fantastic. That's That's why everybody that I know works in this field, but a long road ahead.

Petra Soderling  20:23  

So help me understand a little bit still with this technology. If, in classical computing, when companies want to create products or new technologies they do, they create standards, industry specifications, they want to strive towards interoperability of their equipment, so they work together, so everyone, everyone can grow the cake bigger and enjoy. But in quantum, you're saying that's not the case that if one person has a quantum lock and a quantum key for the security, and then another one has the same system, but it's impossible to break the other quantum system, even though you're both of you are using quantum encryption.

Andre Konig  21:06  

And a lot of people are working on regulation on ethical and moral standards on standardizations, on frameworks, even on jurisdiction for quantum computing applications. So all of that work is being done, which is kind of crazy. If you think were on the maturity curve. Quantum is really just came out of the big labs, and companies just started to play around with it very, very recently. So I do want to make sure we're sending a positive message out here, this technology is maturing extremely fast, a lot of very smart people in North America, in Europe, but also in all these other corners of the world where we on one quantum operate, really trying their best to build local ecosystems and collaborations across, you know, public private partnerships, with startups with with investors and so forth. From a pure application point of view, and others just my personal opinion, I think, you know, Ilan musk version to one day is going to come along with this all powerful machine. And hopefully, we'll do something good with it. It is not something that will ever run on your iPhone, even so some people are working on that. But yeah, you would, you would need to understand quantum and quantum math, which, which takes a little bit of work. So I think the application and the cake that you refer to, might be very big and not very shared one, one person that's going to get very fat from eating all that cake, her or himself.

Petra Soderling  22:59  

One of the arguments, I think about 20 years ago, when all of the Western world had the internet, but many African countries did not. But then companies started building cellular networks and an offering mobile phones in Africa. And there was a conversation that many African countries that are actually making a frog leap in using apps and mobile services. And going ahead with mobile banking, for example, much faster than Western countries who already had internet and laptops. Do you think that maybe something like this could happen with countries like Himalaya and some of the African countries that you work with it? Because they invest in quantum right now? They would have an advance.

Andre Konig  23:42  

It's very difficult, unfortunately, and they pile us. amazing job. My president Manish tapa of our Naipaul shop their job has done, it is still very difficult to get support or response from local key influencer, stakeholders, government or companies. In Africa, we have eight chapters from South Africa to some Bob way Ghana, Kenya, up to North Africa, we see a little bit more appetite. We're starting to work with the South African government, for example, to help them formulate their national quantum strategy. We've run large conferences there we've run hackathons and educational courses for folks. realities, though, is that these quantum computers, as much progress as we've made in this space, which is really ridiculous if you think about it, just over the last few years, are still extremely, extremely difficult to comprehend, to build and and to bring to market and the basic science that you need the sheer understanding of physics but also math And then the engineering talent and resources required to put something like this, in place, kind of limits it to North America. And both the US and Canada, a very big players here, the large European economies, Germany, with probably the second largest government program in the world after China, countries like the Netherlands, the UK, Finland, France, also Spain, that are doing tremendous, tremendous work. Israel had a very strong kind of entry on the on the global quantum scene, just a year, a year and a half ago with a very strong and impressive national program and a local ecosystem. Japan, Australia, and then of course, China and Russia. Beyond that, it's going to get very, very difficult to find the talent required, the basic scientists, the engineers, the developers to mathematicians, that you would need to pull something like this off.

Petra Soderling  26:05  

So before the recording today, you shared some of your data material with me. And there were two maps that caught my attention. One map is the public investments. You alluded to this government programs around the world. You have many countries, we're putting a lot of public money into quantum research and development. So those US, Canada, China, Russia, the countries that you mentioned. But then the other slide that you have is the private technology startups in this space. And it's pretty much only the US. So other countries are investing in the research but they're the startups are not coming up. So why is that? Are the countries are not interested in making money or what's going on here.

Andre Konig  26:47  

I think nobody has really yet figured out how to make money in quantum. And that is a good thing, because we need to give our scientists and engineers time to continue to mature to technology. Beyond that, I think you see some of those typical geopolitical differences up play the North American market where some of the big OEM vendors, IBM, Google, Microsoft, Amazon, Intel, now to have gotten into quantum, they, of course, have a bottom line driven mandate. And, you know, IBM is so fantastic, very impressive research organization. And they have given and will continue to give their quantum program, a lot of leeway and freedom. At some point in time, the manager of that program, Diego and j day will need to report to some some good numbers to their CEO, or they'll be in trouble, right? In Europe, we simply haven't reached that stage yet. and Europe, independent of the application that's not much different in digital AI or blockchain, we just don't have that same startup culture, but also not the same investment culture. private investors, you mentioned us map of deals in quantum. And most of the startups indeed are in North America. If you dig deeper into that chart, you will see that there's about 35 40% of the money that does come out of Europe, so and goes to the US, which is a shame the UK here is a big exception. But unfortunately, our British friends left the EU. So those programs are struggling a little bit and itself. It's a question of startup culture and investment culture. But we also don't have these big Google, Facebook, Amazon type of companies anywhere else, except maybe China. And companies, they're like Alibaba, heavily engaged in quantum as well.

Petra Soderling  28:51  

You mentioned lack of talent and resources everywhere. So maybe the solution is for this European and other countries to train more quantum professionals.

Andre Konig  29:02  

That is something that every government and every big company is talking about, we in fact, just delivered a study on the quantum workforce. The problem here is that you need to train a wide variety of, you know, roles, these PhD types of profiles that do the basic research and assigns engineers at a very high level, but then also developers, folks that help with the kind of assembly measurement control and auxiliary systems. So it is an effort that takes years and years and years in AI and blockchain and detrital it might be enough to you know, take a few classes, online certificates, and you know, then learn kind of on the job in quantum if you really get You're really talking about building a viable system. It takes years and years and years of training. So we will be seeing a gap which in the startup world dramatically called the valley of death. And it will be very interesting to see who gets through that. It's a problem that can't be fixed with money or or anything else. We just need to ride people.

Petra Soderling  30:25  

Okay, as we approach the end of our time here, do you have any final words to our audiences on quantum

Andre Konig  30:34  

I think everybody, even if they don't feel concerned by anything that we just discussed, should at least invest half an hour or so of their time into googling quantum computing and watching some some videos will will close with Einstein, this spooky indeed, theoretically also makes things like time travel or teleportation possible. So it is a fascinating field. And everybody you know, will probably enjoy learning about things like superposition, entanglement interference, because they truly are fascinating. To all my professional friends out there in the quantum world. We got to keep at it, we have to be positive, we have to avoid hype. hype is what's killing us hyper customers, hype with partners hype with investors. We need to give it the time to be successful too much your figure out the problems that we still have and then really bring it to market.

Petra Soderling  31:37  

Andre M. Koenig from entanglement capital. Thank you very much for your time today.

Andre Konig  31:43  

My pleasure.

Speaker 1  32:20  

You've listened to deep pockets by Petra Soderling. Be sure to subscribe for more episodes.

 Welcome to Deep Pockets, the podcast for exploring how basic science once created in a lab and funded by public means, is fueling the economy with completely new private industries. Deep Pockets is created by Petra, so. On September 14th, 1858, Mr. Fordice Biles an employee of Remington Arms Company L l c received his US patent number 21,478 for his design, which required lowering of the loading labor to allow the cylinder pin to be pulled forward to free the cylinder.

This ingenious patent was quickly implemented in Remington revolvers, which the companies supplied to both the Union Army and Navy. Throughout the Civil War, more than 80 Union Cavalry regiments were armed with hundreds of thousands of Remington revolvers. Around the same time, a former Milwaukee newspaper editor, Mr.

Christopher Latham Schultz experiments with what he calls a writing machine. His writing machine is built from piano, hammers, telegraph, keys, and a pedal from a sewing machine. The writing machine is patented 10 years after the Remington rifle. By this time, the war is over and Mr. Filo Remington sits on a pile of cash.

Mr. S Schultz presents the writing machine to Mr. Remington. He gets excited and the Remington typewriter is born. As the telegraph keys, piano, hammers, and sewing machine lent themselves to the typewriter. The manual typewriter lends itself to the electric typewriter, which lends itself to the word processor, again, to the personal computer, and finally to the smartphone.

So who should we thank for the phone in our Pocket Union Army, Mr. Shoals or Mr. Remming? To discuss this with me today is Laura Thomas, a specialist in National Security and Emerging Technologies. She's a former CIA officer and chief of Base who led Sensitive CIA A Operations both at both at C I A headquarters and abroad.

And currently is a Senior Director of National Security Solutions at a quantum sensing and computing company. She also serves as an advisor to tech startups. Welcome to Deep Pockets, Laura. Thank you, Petra. It's really nice to be here. So, in your opinion, who should we thank for our phones? The Army who created private wealth, the wealthy individual or the inventor?

Well, I think as your narrative illustrates, it's, it's probably a combination of all of them. Uh, though I do think we should probably give the most credit to individual inventors. You know, it's, it's this very special person who's willing to tolerate constant failure without a loss of enthusiasm and a type of person who has the perseverance to endure and eventually leads that spark that creates a new product or industry and, and really alters our way of living.

So, um, let's talk about the c i A a little bit. CIA launched its own venture capital firm in Tel in 1999. I found a wired article from 2003 that said that Silicon Valley was skeptic at first, but now everybody is a believer. Over those four years, a total of 3,400 companies had pitched to Intel. It has long been, and I think still is the country's only federally funded venture capital firm.

Why would an organization like the C I A do this. Wow. That's a good question. Uh, yeah know, I think an organization's decisions, they're ultimately a result of its culture and its people. And there were some real visionaries at CIA I at the time who realized that inq tell is exactly what the CIA needed.

And I know that Sue Gordon, you know, she was a, a driving factor behind NQ Tel. As you might be aware, she eventually became the principal Deputy Director of National Intelligence, and she is quite the guiding light for many women in the intelligence community. Uh, she also happens to be on the board of my, my current company, the, this would be the honorable Susan M.

Gordon, who indeed served as a principal deputy director of a national Intelligence until August, 2019. Prior to assuming that role, she was the deputy Director of the National Geospatial Intelligence Agency, as well as the director of the CIA's Information Operations Center and Senior Cyber Advisor to the director of the cia.

Gordon worked for the CIA for over 25 years. Um, but I believe that she, and, you know, a number of others recognized really early on the tectonic shift that technology was having and would continue to have on economic and national security and the forces of, of innovation that were really being unleashed.

In the late nineties. And, and then if you kind of look back at the same time, there was this divergence, um, between government and industry. It was a sense that the Cold War was over. There's a real sense of, of promise and optimism in the US that, you know, how these new technologies would, would shape our future and we're looking to the future rather than ha walking around with that fear.

Fear of the future and the threats that, you know, previously lurked within from adversaries like the Soviet Union. And at the same time there was a recognition that the slow movement of government acquisition process meant that the agency and the US government were really losing out on a lot of innovative technologies.

And of course, you know, c i A being a. Entity. It couldn't really afford to hire top venture capital talent. So they knew they'd have to create a new way of doing things. And, uh, thus Intel was born. And one thing to note is Intel. It serves more than just the cia, but it's really the entire intelligence community.

And also it, it does some work with affiliated partners, uh, in the UK and Australia, and they're really looking at technologies that, um, have the potential to commercialize and also have national security implications. So think ai ml, quantum autonomous systems. Data analytics and cybersecurity. These are all very key areas for Inq Tel.

And you know, on the whole, it's, I think it's an incredibly valuable organization. Uh, there's still really major challenges to getting the right technology into the hands of the people who need it. And, uh, you know, this can be done, it's done from time to time, but there's still a bureaucratic process there that, that, um, sort of slows us down.

And, and sometimes by the time the technology gets into the hands of the user, it's, it's outdated. Uh, I think it would probably take hours to untangle that in, in this podcast. So we don't have that much time. Um, but you know, the main takeaway for me is that our defense acquisition policies, they were built for a different time.

And that time was when we purchased large platforms. That have been proven to work exactly as tested. And I think until we make cultural shifts within organizations, we're, we're sort of always going to be rolling a ball uphill and trying to address technology adoption into the government. And ultimately I think we're going to have to, to change the way we, um, acquire.

Technology through the federal sort of, it's called the the far, the Federal Acquisition Regulation, and that's a lot easier said than done. The Federal Acquisition Regulation FAR is the principle set of rules regarding government procurement. It covers many of the contracts issued by the US military and NASA as well as US civilian, federal.

It contains contract clauses and provisions such as certification requirements and instructions directed at firms that might be interested in competing for a specific contract. Most recently, the general public saw this regulation in the news when Blue Origin complained against NASA for choosing Space X as the first commercial human lender to the moon.

Blue Origin felt NASA did not appropriately evaluate the two bids. But, uh, getting back to your, your question on Nq Tel itself, I mean, the value proposition is, is really high for some of these startup companies because government is so hard to break into. And Q Tel basically breaks down those barriers for them.

You know, they have, uh, the contacts and the cache and they just know how to get things done. And, and that's very, Yeah, you mentioned that CIA is not the only government office that deals with national security and engages private companies in inventing new products and services to maintain the technological superiority on the battlefield.

The Department of Defense, D O D relies on scientific and technical. Knowledge developed in large measure through research development tests, and evaluation, what they call the R D T and E, funded by the department and performed by industry, universities, federal labs and et cetera. And 97% of this budget, which was 107.5 billion, uh, last year comes.

Defense Appropriations Act, title four. This money is divided between Army, Navy, air Force, and Space Force. And if we look at the type of research, this 100 billion funds, most of this is directed towards the application of existing scientific and technical knowledge. To meet, uh, the current or near term operational needs, and only 2.4% is given to basic research.

Given that we would not have the internet if it wasn't for DODs basic research funds, do you think this 2.4% is enough? Well, I, I think the issue isn't so much the amount, um, but it's how that amount is spent and, and the value. Lost in, in the overarching and, and very highly bureaucratic way that the US government does budgeting and allocations.

And you know, if you think about this and then you add in human nature, we can see that we're combining outdated process with sometimes the lesser angels of, of human nature. And what do I mean by that? Well, you know, I think organizations, they get the behaviors they reward and the US government as a bureaucracy, it really rewards caution.

And a lack of deviation from process when it comes to money and, and government spending. And, you know, I, I would add that that's, that's for good region reason. I mean, no taxpayer wants to think that their hard-earned money is being thrown into the ocean. And, you know, no politician would be reelected if, if that's the case, um, or at least known to be the case.

And even a, a very senior government bureaucrat doesn't want a crisis on their watch that's involving, you know, cronyism or wasteful spending. Um, but sometimes, you know, we, we kind of end. Causing what we're trying to prevent. And I think that's especially true when we don't adapt our frameworks to changing times.

And 30 years ago, it, it made sense to do long and careful, uh, budgetary planning cycles just based on the way technology, uh, worked. But, uh, today, you know, the speed at which technology forces change, we, we just have not adapted to it. And I think we're creating Band-aid fixes and we're not really addressing the root cause, which is fixing government processes.

And again, this, I think this ties back into culture and. And human nature and we're very reticent to change. And the issue has become so large that no single person or even government entity really has the ability to change it potentially outside of, of Congress if they were to change the legislation.

Uh, you know, it's sort of like watching an ant colony. Uh, a single ant can't move a stick, but hundreds can, and we just don't have that level of cooperation and cohesiveness yet. When you really think about it, I think that the, the only thing that forces bureaucracies to change is, is a major crisis. And I, I think that it's very possible we'll have to face, uh, a crisis big enough to force that change.

Right now we have this frozen middle of procurement within the government. And, um, there are individual contracting officers who are stuck in process for process sake. And I think they're divorced from the bigger y uh, you know, they, they, they're just doing what they've been taught because bad things happen, you know, if they deviate from what they're taught, like questions of wasteful spending.

Um, and, and I. You know, in many ways what's happening is we're punishing the many for the mal past malfeasance of the few. And rather than giving procurement officials really broad autonomy to use good judgment, um, we're just, we're just, we're sort of stuck in that past thinking and, you know, we have to bring the end user.

So who's actually using the technology in the go? Closer to the industry provider instead of always working through this very layered procurement process. And, uh, I think, you know, technology compresses timeframes and that's why it's so important that we break down those barriers. Um, I, I, I will go back to the procurement piece too.

I mean, procurement officers, you know, they're not going to get fired or face any type of really retribution if, if they go with a mainstay, big defense contractor for anything to include, you know, items of basic research, even if that project fails. And if they push the envelope though, and they select an non-entity, Uh, to do that research.

Then they've broken away from the pack and they put themselves at risk, and people aren't incentivized to take riskier bets, even if those bets aren't particularly risky. They're just riskier than the mainstays. Um, and you know, perhaps if that bet paid off well then they'd be rewarded, but. Because failed ones with big contractors don't incur much question or accountability.

You know what, what's the, what's the incentive to take a riskier bet? There's a really great quote that, uh, when I talk about this, I'm reminded of, uh, Desmond Tutu. He, he said that there comes a point where we have to stop pulling people out of the river and we have to go upstream and find out why they're falling in.

And for me, until we change the underlying cause, we're just treating symptoms and, you know, throwing more money at a problem doesn't necessarily fix it. So, you know, back to your question that I think there are really structural changes in the budgeting and acquisition process that, that we need to fix first rather than just.

Increasing the size and the amount of money that we, we spend. Yeah. Another quote that comes to mind when I was listening to you is no one ever got fired for, um, buying an IBM machine. So it's a similar Exactly. Pattern. Yeah. Yeah. Um, you also mentioned something that I liked, um, a lot. The bigger why. Some of these, uh, um, procurement people you said are divorced from the bigger why.

So here's a bigger why. If we go back to the early two thousands, nine 11, that was obviously a security event that made many changes in how Americans and the entire world use the internet, how we bank, travel, communicate, et cetera. Would you say that the tighter national security requirements to private companies was a boost or a hindrance in tech innovation?

Yeah, that, that's a good question. Uh, it's, it's hard to say, you know, I, we can look at some of the technologies that have started directly or indirectly as a result, you know, for example, encryption, um, and, and all the technologies around encryption today. You know, I don't think we'd have them if it weren't for nine 11 and subsequently that cat and mouse game.

Increasing communication, snooping and, and security. And you know, back to my comment about sometimes it takes a crisis to change a bureaucracy. You know, in this case, a crisis led to an entirely new segment for the old defense contractors and the rise of new ones. Yeah, so going back to the Remington revolver again, that revolver actually transitioned into a new form factor by late 1862, and this was due to continual improvement, improvement suggestions from the US Ordinance department.

In other words, the Army was giving user feedback to a tech company, thus making the tech company's products more successful. How much of this do you know is happening today? Yeah, certainly. You know, this happens today. Uh, I, I do think a major challenge for companies who work with the government. Um, especially if they're working with the government as a stepping stone to larger commercial markets, which, which many are.

You know, they have to be really careful not to over-engineer product, the government standards. And I think this among, you know, a few other reasons is why some venture capital firms, they shy away from investing in early stage companies with a government sales component working with the government. It can sometimes, Quite high compliance standards and overhead costs that that can cripple a company's ability, uh, to move very quickly to address the commercial markets.

And, um, you know, when you have to engineer to a, a highly articulated government standard, that doesn't always align to commercial consumer. Demands, um, you know, they, they can't just transfer over the product they produce for the government to the commercial sector necessarily. Um, if you can find a technology that does that very well, then you know, there's certainly benefit there.

And generally, if, if you look, many companies, they find it quite difficult to serve both a, a government and a commercial. Uh, market. And if you look at the successful companies that are doing it, you'll see that they've, they've actually split themselves into subsidiaries, you know, Microsoft versus Microsoft Federal, for example.

Mm-hmm. Interesting. So this episode is on war and we haven't spoken about an actual war yet. Yeah. Um, the war that must be on top of everyone's minds is, of course, Afghanistan. Brown University's costs of war project has estimated the war in Afghanistan has cost US taxpayers 2.3 trillion. Trillion with a T.

To put this in context, all of the 2019 budget spendings were 4.4 trillion, so almost a half year's worth of tax revenue has gone into this war. Where did that money go, Laura? Well, I, I'm not gonna give you a, a definitive answer cuz that's a, that's a lot of money to account for. Um, but you know, I will say Afghanistan has certainly been on my mind as well, having served there.

Um, I think a general answer would be so much of that money went to infrastructure costs. And if you've been to Afghanistan and, and visited Bagram Airbase for example, you know, you would've seen just the magnitude of of INS infrastructure. That it took to, to house, feed and protect US troops. And then you add in airlift mobility costs for just vehicles, helicopters.

And you know, obviously it's not just for us, it was for NATO as well. And you, you think about the big defense contractors that were hired to put up perimeter security and barricades, and then all the money that went into training and supply. The Afghan army. Um, and then the, the money that went into Afghan companies to provide basic resources and, and some of which, uh, a good portion of which actually fueled, you know, even more corruption in Afghanistan.

So that, that's where a lot of it went generally. Um, I think on the technology front, you know, the US involvement in Afghanistan and Iraq, it led to lots of money being spent. Being able to, to better sort of see the battlefield from a technological perspective and some of this technology. I, I think, no doubt, benefits everyday people in the US and the world when we think of autonomous technologies like drone delivery or the internet of things, camera technologies.

You know, it certainly could be just as easily used for surveillance, but it's, it's also used to wire your house so you can know, you know, exactly when your Amazon package was delivered. And you can look in your, your camera and or your ring device and see, you know, that it was put on your front door.

Back door as you requested. Um, yeah, I, I, I can't help but think of just the huge civil society programs we poured. Money into an Afghanistan to, to try to create a, a stable government. And a, a good example would be just the large programs to eradicate the Poppy product. Right. You know, the sheer volumes of money that went into that.

And it's really hard to say how much we have to show for it. And you know, that also makes me think about that, the human factor as well. Um, I mean, there are girls who are educated. Because of, of that, and, and that really matters. Um, and, and certainly the value of one life saved, um, is, is a good thing. And it, it makes it really hard when you're taking a, a cold, calculating view of, Financial cost benefit.

And then you compare that to just the, the number of lives lost, lost both Afghan, US and, and many other nationalities as well. And you know, overall I think we've, we suffered from a sun cost syndrome combined with hubris in Afghanistan. And I don't know where, where does that leave me with my views? Um, they're always changing.

I, I think. They're being formed according to what I learned. And there's a, a quote that I really like. It's when the facts change, I change my mind. And right now I think war has certainly shaped technology because it shapes us as people and technology, it's ultimately a reflection of human nature and human desires.

Laura Thomas, thank you so much for taking talking us to us today. All right. You're welcome. It was my pleasure, Petra.

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