welcome to the good growth podcast brought to you by bgf the most active Equity investor in the UK and Ireland this week Jimmy is joined by Tim Ray head of early stage at bgf as an entrepreneur turned investor Tim has been active in the startup ecosystem for over 20 years in this episode he explains how bgf assesses growth potential in early stage businesses the UK's regulatory environment post brexit and the disruptive technologies that he's excited about Tim what's your role at bgf so I am head of early stage investing at bgf and uh I think that's by virtue of uh kicking off early stage investing as we do it now about five or six years ago um and bgf isn't necessarily known for kind of early stage investment what sort of sectors are you specifically looking at yeah so yeah interestingly I think the the core and I always describe it as the core of what bgf does is that grow grow stage investing so you targeting businesses that have you know generated some momentum they've got good commercial traction and it's easy to assess them really in the sense of uh you their commercial engagement what the numbers look like and how those numbers are trending um so from that perspective you can I think you know quite easily tackle a very broad range of businesses because when you boil it down you can kind of assess it based on how it's performing uh and you know what the next year 24 months are going to look like As you move earlier uh it becomes more difficult because you don't have the numbers to go by you don't have customers to talk to sometimes and so actually it's really important and I think a key plank of what we've done has been to focus on specific sectors where we can actually develop some depth of understanding uh about the Dynamics of the sectors uh you how they're playing into uh you know a problem space uh how technology is impinging both the adoption of new technology but it's a generation of of New Opportunities um so we spent quite a lot of time uh I think deliberating around you know which sectors the focus on uh really kicked off in 2018 and spent quite a lot of time looking at different sectors UK economy what's important you know we looked at creative uh we looked at Life Sciences uh and of course because of uh the kind of growing interest in Opportunities around climate and climate related Technologies we looked at those sectors and really uh initially focused on Life Sciences but life science is very broadly defined because it's such a big part of the UK economy and it's a an area where actually as an engine the UK massively punches above its weight in terms of innovation and technology and breakthroughs uh but yet it's a sector that's not as well supported financially as it could be and that really rang true for us in terms of that wider BTF message of you know filling a gap in the market to an extent you know where are there gaps in the market how can we be additive to the overall ecosystem uh and you know for me a key driver was you that opportunity to combine impact with um making a return on Capital and the title of this podcast series is called good growth how do you assess good growth potential in an early stage company yeah I think it goes back probably really to that impact point and so you know it's interesting you just yesterday for example we had one of our regular meetings so every week or every two weeks we'll have a a batch of companies that you know either we've sourced somebody's introduced to us or it's just found its way to us to you know through the website even uh and you kind of go through and you assess those companies and you're thinking you know which ones of these because we can't do them all yeah uh we have limited capacity uh we got 10 to look at we might do one of them yeah which one of these are we going to do and one of the dimensions and we don't have a formulaic way of doing this but yeah you look at you know what do we think of the people all the usual stuff you know what's the potential of this you know what validation does this have so you know on balance something that is a little bit more validated than other things might stand out but you you get to the end of it you think actually this one sort of stands out for a number of reasons but if you look at the problem that they're solving could we really get excited about solving that problem versus some of these other problems that need to be solved which maybe in some cases are just more fundamental and what sort of early stage can mean a uh a sort of wide variety give us an average of sort of when you're writing the check and what kind of check size you're wri yeah you know it's it's interesting if you look at a typical growth deal and you we quite often say you're looking for companies with greater than 5 million of of Revenue and you know EIT do positive uh if you think you know if bgf is going to do early stage it's it's quite rational logical to think actually early stage will be just before that and we have looked at that zone so companies that are maybe generating some Revenue but just aren't quite at the bgf criteria and do you set up early stage to be a feeder into that the challenge with that category is that there are a bunch of companies in that bundle that actually don't really grow yeah uh and they don't evolve to be in bgf Sweet Spot uh and they bu of companies in there that uh have you know in some cases unrealistic expectations in terms of you know valuation and their likely growth rate and impact when you know they're benchmarking themselves against a you a venture style company that might have a billion dollar you know valuation potential uh some of these are never going to top 50 70 that's not to say they're not you know interesting but they're just a different sort of company and so as a an organization I I think we've we've backed off looking at companies in that zone and you know for us on the early stage side we're looking at companies that have that really significant significant potential companies where we can't legitimately say actually you know this is interesting but come back and talk to us in six months or a year we do quite a lot of that engagement and say you know actually this is this will be an ideal growth deal and so we will do it as a growth deal and maybe there's something we can do now to help give some advice make some introductions but you know come back you know in a year's time based on your forecast um this early stage group of companies that we're tackling are companies that actually will never track into bgf's normal criteria uh so if we don't engage now we're never going to uh engage and get there and what do you make of the sort of the the Valley of Death that a lot of these deep tech companies go through this used to be a sort of serious problem it was something the UK was seeing as you we had a lot of the science side of things very good at that but how has the sort of scaling bit improved over the last few years I I think it's still a problem and I think it's a problem because it's a multi-dimensional issue and I think you simplistically is tempting to home in on a single dimension of the issue and say you know the the challenge is that companies can't access you know big checks you know there's not an investor who will write a 100 million pound investment uh into to this you know struggling company that needs to jump over that that hurle uh and while that is sometimes true my view would be that if you take a step back I I think you'd be safe in saying that companies that deserve large checks will get large checks and whether it comes from a UK investor or a US investor it just doesn't matter if they deserve it they will get it and the challenge has always been to deserve it and if you unpack that I think uh a big challenge in the UK has been talent and it's both the availability of the of the talent but it's also the willingness of some of those earlier stage companies with really interesting innovative ideas to actually evolve and adopt and bring in that new Talent uh and we've got lots of you know interesting examples in our portfolio of where that's worked and companies have embraced that in cases where it hasn't yeah and it's always painful evolution is painful but the fact of the matter is some people are very good at certain stages of the process once in a while people are good at taking something all the way through but more often than not you're going to really need to augment teams and grow teams and evolve teams throughout the journey and I think the the challenges with navigating the valley of death is really a challenge of bringing on the right talent and integrating that Talent into the team in the right way and on the talent what is important when it comes to scaling Talent what do we need yeah I think you the maybe the simplest answer to that is is people who have already done it um rather than people who are figuring it out and sometimes you can find a happy medium in there with uh you people who are figuring it out because people have to learn at some stage but Guided by people who have done it before and that's where you one of our areas of focus has always been trying to understand how to augment a team with non-executive Talent yeah uh so we spent a lot of time trying to find the right chair for a business for example uh and again it's not always easy to get right but if you get it right I think you know you can have a pretty big impact there's really though no substitute for some of the critical functions of a business and if you look at life sciences for example some of the critical functions around you know regulatory process uh you know us Market entry uh if you haven't done those things before uh you know you can figure out a lot of stuff you can go talk to people and you know kind of put together a plan unless you've done it before unless you know people and already have a bit of a network to kind of pull in the people who can help you do a thing uh you know you're going to be prone to error if you're an early stage company or a company that's dependent on financing uh you'll have a a Runway that you planned you know you've got your road map you might have a little bit of flexibility in there but you've got to get it right first time yeah if you don't get it right first time you're going back around the loop and saying you know you a regulatory delay can be 6 months 9 months sometimes a year uh you know youve and you've got to keep the organization going for a year uh we've got a a company that did a very good job in those circumstances where they actually had a very complicated product that they were developing uh they delivered this complicated product within a month of their target date so you know pretty pretty amazing on the scale of things and then the regulator because of you know regulatory chaos in the UK the regulator just couldn't engage when they originally planned to it's a 12- Monon delay that company needs funding for another 12 months uh and that's that's always quite challenging if you've got somebody who has been through that Loop before a they might have an existing relationship with a regul regulator they might have good relationships with regulatory Consultants yeah um they might have planned things a little bit more thoroughly uh and you might have avoided that sort of scenario um has the UK had particularly regulatory chaos over the last few years as a result of brexit and having several Prime Ministers is is that a sort of real life impact that Happ Beyond any shadow of a doubt yeah you know the UK from an EU perspective was the Hub of EU regulatory activities uh I think there were some good people there I think it ran you know as smoothly as these things ever run uh but I think a common complaint that we've definitely heard uh consistently from almost every company is that things have been completely chaotic to the extent that at times they complain about not even being able to get a response yeah that's uh that's not great um give us an example of some of the companies that are in the portfolio at the moment and what they're doing across the kind of different sectors we' talked talks about life sciences climate and sustainability give us an example of what they're doing and the problems they're solving yeah so I think first of all starting in life sciences we tend to Define that quite broadly for some people when you say life sciences it just automatically means your new drug development discovery that you know we call that biotech um but we sort of partition that market and say you know there's a biotech piece that doesn't work so well for us because of the nature of our fund um and in the UK we've got some really great investors that just do that type of investing but if you uh take a step back there are a uh a class of company that is playing into new drug development but more from a platform perspective so instead of having you know one asset uh that they're taking through the clinical process you might have a platform company that has some technology that supports the development of multiple assets or identifies multiple targets and those have not been as well supported maybe as they should be so we focused on those we have a nice little cluster of those companies where again they can generate you know multiple drugs over a period of time um so if company called foremost for example uh that is uh is doing very well uh company in Aberdine called alasen uh again doing some very interesting stuff with you know different types of drugs and the plans for those companies will always revolve around developing things to a certain extent and then partnering with uh other companies Pharma companies to then take those things through clinical um we also have a a Diagnostics cluster and Diagnostics I think has been a difficult category uh just probably globally but particularly in the UK from an investor support perspective and it's quite frustrating because you can actually see areas where there's a really clear Market need uh you can then meet companies where there's very interesting technology that could potentially meet that market need yeah and sometimes when you break it down and analyze it you think okay problem solution but economically it just doesn't stack up yeah uh because even if you tested everybody in the world you're only charging 50 pounds so it's never going to make enough money um so I think there's a a challenge with a lot of diagnostic companies in the maybe lower end of the spectrum however there are some Advanced Diagnostic companies that were spending time with and some of those are more in the early detection early identification of problems uh and if obviously if you look at cancer the earlier you spot cancer the more treatment options you'll have with that cancer the problem when you get into early Diagnostics is you get challenges with uh not finding too many people uh you know there's a point Beyond which identifying that somebody has cancer is really useful yeah there's a point ahead of which it might not be that useful because you might end up streaming people down towards treatments that they didn't actually need so yeah some of those tricky areas around di agnostics are are super interesting problems to try to figure out and navigate but I think that's where maybe a lot of our massive gains will come from in the next 10 to 20 years uh and then we have a Medtech cluster uh and again and just why you think the advances of that 10 to 20 years why do you think the advances will come then and give us an example of what it might might look like so I think uh early identification of problems first of all has got to deliver you know the biggest potential gains uh and so you know finding that um uh finding that somebody has early signs of uh neurod degeneration for example uh will open up treatment option and if you think about Alzheimer's and neurod degeneration uh a lot of news in the last uh couple of years about the first couple of treatments that are finally approved if you look at those treatments they're slightly tricky because uh you know they they extend some hope but they're really about slowing the progression of a disease by maybe 25% yeah if you're slowing the progression of disease you want to start that slowing process as soon as you can yeah in the course of the disease because you're preserving more function uh and so how do you actually identify the people who can get that disease who who can actually take that drug to treat that disease but if you actually look at the side effect profiles of the drugs they're non-trivial yeah so you need to make sure that the people who are getting the drug are really the people who need the drug and you need to monitor them to make sure that actually the drug is being efficacious and having an impact yeah because you don't want people on the drug if it's not having an impact and so it's all of those things around early identification it's around identifying who can benefit from the drug and making sure they are continuing to benefit from the drug if you look at cancer uh you there are uh increasingly uh a large quantity of drugs that a clinician might have to choose from uh when it comes to treating a patient you know in the not too distant past it was you know it's you know one or two things you you might choose and you might throw everything at it to see what happens now you might have half a dozen drugs that you can choose from and the question becomes quite quickly well which drug yes should I give this patient uh and you can increasingly identify which drug a patient will respond to and you can identify and monitor that response but interestingly over the course of the treatment uh the cancer in a patient's body uh could well develop a resistance to that drug yeah cancer is a very creative disease and so you actually need to evolve the treatments identify when that resistance is building up and maybe move on to the next drug so all of those things require Advanced Diagnostic capabilities yeah what are the early signs of Alzheimer's so uh the that's a very interesting deep question with probably a very long answer uh you know the of the podcast the the really the really interesting thing is if you think about it you just from a human perception perspective uh it's probably the same with with most diseases if you think about you know feeling you wake up one day and you feel ill uh you always think back and think actually I didn't feel that well three days ago but I didn't notice it three days ago I notice it now because I'm now ill and I think you neurod degeneration dementia Alzheimer's is is probably an extreme form of that you by the time probably not you but those around you have noticed an issue uh it's been there for a long time and you'll very quickly track back and think actually five years ago I I now I now I remember this person was quite forgetful or was struggling to think of somebody's name and I didn't think anything of it at the time but now that they yeah you know can't find their way to their car uh you know it all makes sense um the challenging thing about relying on that is by the time you get to that stage physiologically you biologically so many things have happened you're so far gone that it's almost hard to treat uh and if you think about it from a drug development perspective it's really hard to run a clinical trial uh for your drug if you can't find people who are early in the course of the disease so a lot of the challenge around dementia has been trying to understand how you identify early signs of it yeah so you can actually pick patients to run studies on the biggest problem with dementia Alzheimer's and that whole area is that there are almost certainly multiple forms of the disease yeah you know we quite simplistically try to say you know we've got one thing what's the cause of that thing yeah uh we've got multiple things and we probably actually really don't know the causes of any of those things yeah we have some things that maybe at best correlate this is my view uh so if you look at the current drugs they're focused on treating a very specific thing amalo plaques that build up in the brain okay um if you actually take tests uh for an individual you know five years before it's clear that they have dementia uh and look for traces of amalo in the cerebral spinal fluid or doing a pet scan of the brain uh it tends to be poorly predictive of the development of the disease and if from a pathological perspective you know post humously uh if you analyze the brains of individuals who are thought to have dementia and some who were thought not to have dementia you can find the pathology in people who were thought not to have dementia that matches the pathology of people who were thought to have dementia it's a really really tricky thing to figure out um we've recently backed a company in Oxford called Oxford brain Diagnostics and uh we were drawn to what they're doing because first of all it it came out of 20 years of analysis of brain tissue samples and trying to understand the key characteristics of brain tissue and what signals imply neurod degeneration yeah uh and they developed a technique using quite straightforward standard MRI uh analysis um MRI um techniques uh and they uh can now quantify neurod degeneration by looking at the quality of brain tissue uh and that is uh I think emerging as something that is independent of disease hypothesis so it doesn't matter if you believe it's all down to ameloid or if you believe it's all down down to inflammation uh kind of doesn't matter what it is if you look at the actual physiological nature of the tissue and you can quantify neurod degeneration and even better if you can quantify that in the context of what's called cognitive Reserve so some people like it or not just have more to work with uh and so can tolerate a degree of neurod degeneration whereas some others can't uh if you can assess that objectively uh you have a lot more to work with that sort of approach gets us excited because it's more fundamental than you know throwing everything behind you know a specific hypothesis in a disease area that is really interesting I haven't planned to go quite so much into sort of spray neurology uh but that's you are very good at explaining these things so it's it's very interesting um give us some examples of some of the other companies you're excited about in the portfolio that are doing interesting things uh we have on a similar you know kind of uh thread we have a company also in Oxford called chisto Diagnostics and they're working in cardiovascular disease uh very uh interesting founding team of cardiologists from Oxford and uh they have uh studied the problem of um of diagnosing heart attack risk so uh I'm sure lots of other people have maybe had a similar experience to me where you go and you talk to your doctor and they eyeball you on the way in and they think okay you you're this age you know you're you got these characteristics uh they might ask you a couple of questions about your family and because my yeah my father and my grandfather didn't eat well uh and didn't exercise uh and probably live very stressful lives you know they had heart attacks before they were 60 yeah which automatically makes me doomed yeah even though I think well I don't know I feel pretty good right now um but you know that that statistically uh driven approach to telling one you know this we think you know kind of might drive what happens to you in the next you know kind of five years uh I think a lot of people find difficult to accept because if they say look you know five out of 10 people who are in your category are going to have a heart attack think well yeah I might be one of those lucky five uh I think if you can actually though look again physiologically at an individual and say you know forget about all the statistics we've looked at you we've looked inside you and we looked at your arteries and this is you know this is really where you are at and what you need to do about it yeah so much more impactful uh so this particular company is uh is leveraging the fact that for Europe and the US there's been a a move uh towards first line of Investigation being a heart CT scan when somebody shows up at the hospital with chest pain they look at the scan and you know first of all there's a process of is somebody having a heart attack or not it's surprisingly difficult to figure out if somebody's having a heart attack or not it can take quite a while uh if they're not having a heart attack the next question is you know what is the underlying driver of of this chest pane uh they look at the the CT scan and they'll say you know really there's either a major blockage or not a major blockage uh so there's a big fixation with is there a major blockage or not uh when in fact if you analyze the data the larger proportion of heart attacks aren't linked to a large blockage they're just linked to a you know some plaque deposit that ruptures and that rupturing event is driven by inflammation yeah in the artery wall and so uh this company Christo in Oxford benefited as do some of these I think companies that do well benefited from having access to large quantities of data uh and so they were able to uh leverage their access to patients to analyze um uh at molecular level what was going on in the arteries of people who had these sorts of problems they were then able to develop Imaging analysis techniques to correlate machine learning based analysis of images with what they had identified from the actual analysis molecular analysis of tissue so that they can now spot the areas of inflammation in an artery that are likely to trigger a rupture of plaque and it doesn't matter how big it is uh and so in their studies they're taking uh patients who have been basically released to say you know you should be fine you don't have a major blockage 45% of those people who are sent home as being kind of roughly okay uh are redesignated as a result of this analysis as needing some uh to be on a different care pathway wow okay that's interesting so that's you very very interesting impactful um we've got a couple of uh projects that are more in the early cancer identification space uh and I think uh kind of operating in stealth mode yeah uh but there are some very interesting things coming down the pipe on that that I think again when you get Innovation at the early part of the process it has KnockOn effects in terms of what you then do with that information and and how you stage treatments and what kind of treatments you you know kind of put in place um but it's kind of interesting uh to uh to think some of those things through to be involved in the companies because every investment we make you know we're also on the board we live with the company um sometimes we live with the company longer than we expect to uh and uh and you're involved in all of these uh processes of finding your way through and understanding what impact does this have on the care pathway who do we need to work with to you know start to Define some of those changes and look ahead three or four or five years probably takes us back to that point of having the right people around the table yeah really interesting how you augment those Talent teams one of the things that obviously gets Bor up a lot is AI and you sent me a paper in advance of this which from 2015 which I found fascinating kind of predicting the future of AI but can you talk us through the the thesis of that paper about how quick trend is speeding up yeah uh and so first of all a qualifier I'm not an AI expert I just meet a lot of companies who are doing some things in AI so I picked bits and pieces up uh so apologies to any experts who may listen and criticize that's my uh that's my qualifier um yeah that that specific paper was actually a blog post uh and it was by a guy called Tim Urban who I think has always done a good job of picking up quite complicated things and breaking them down and explaining them and inserting enough profanity and and funny cartoons to make it entertaining uh but I thought that particular series was was very good and you know I pulled it out and you know shared it with my kids to get them to think about things uh but the uh I think the interesting um the interesting starting point and and I haven't read it for a while but the interesting starting point for me is is just articulating the accelerating pace of technological development and just that concept that actually it's not a constant thing yeah you know the progress that we make now is so much faster than the progress that you know we would have made in 2015 or you know in 2000 or in 1990 because we have more tools to apply yeah to developing things and so there's a constant acceleration which naturally drives more of an exponential type curve as humans we always think everything's linear yes uh and it's really hard for us to take a step back and think okay you know between here and here we made this much progress so you know it's going to you know look quite similar if we project out and it's also not perfectly exponential because actually you naturally get you know waves of technology and if we look back just in you know the last 20 30 years you the emerg of the internet uh and you know it changes a lot of things but then it feels like actually things are kind of steadying up now and and maybe maybe that's it for Innovation and there going to be no more breakthroughs and then bam some something else happens you know it always works that way so you get a sort of s-curve effect but it you could you could superimpose those s-curves over a sort of exponential growth curve over time but the thing that's I think really interesting is just starting with this concept of uh I forget the exact term um that the author uses for it but this concept that if you go back back in time uh to 1750 and you bring somebody from 1750 to today of course they're going to be astounded that things are so different today but they're probably astounded to the extent that they die you know it's just so shocking that they can't you know take it on uh and their heads explode uh if you go back to 1750 and say well how far back from there do you have to go to find somebody whose head would explode in 1750 you might have to go back to like 10,000 BC m yes a really long way back yeah and if you go back to 10,000 BC how far back beyond that do you have to go before you can find somebody who would be impressed by the fact that you have fire yeah you know uh yeah probably do a really big gap so key thing those gaps are shrinking the question then is how far into the Future Would we have to go before we' reach a point where our heads would explode because we wouldn't be able to comprehend the nature of how things are yeah uh and might be like 20 years yeah it's it's frightening to sort of think about it and I think that's one of the things of we think change people will never adopt things but then very suddenly there is sort of mass adoption of these things and that just continues to get sort of quicker and quicker in the world right yes and you know you can there lots of different uh categories maybe of innovation and I think there are the odd things where you the thing pops up and people think you oh my gosh you that that changes everything and tomorrow I will only do things that way um I don't think there are many things in that category if you take something fairly mundane like uber yeah think well you know Uber is fairly you know kind of ubiquitous now uh I honestly can't tell you how many business pctures I had from companies wanting to revolutionize transport or taxis yeah uh and you look at every single one of them and you think yeah all right you know good thanks well done I agree with agree with you on the problem not so sure about the solution too many obstacles to overcome uh but yet you know when somebody draws the boundary around a thing in the right way yeah it works and it often works pretty quickly but even you know it it's quick in retrospect when you're in the middle of it it's not particularly quick it's a an evolutionary change and you know I was in the middle of uh a lot of things to do with mobile technology when the iPhone came out uh and you know if you'd set lots of people down at the table and said in a perfect world if you actually were to build the perfect device to just make all of this work what would it look like they would have sketched out something you know similar to the eiel well yeah you need you need the right sort of Data Network you need to you know the the content people and the mobile operators to to collaborate play ball basically you need to hurt a bunch of cats uh and force everybody to do the right thing uh it's pretty clear what needed to be done done but to get somebody to actually do that yeah you know is is a revolutionary step in the context of an evolution and what do you think the the kind of like modernday rails are like you talk about those things that needed to come together to make the mobile work and for it to become such a uh important factor in all of our Lives what do you think of the modern day rails that we're now going to need for to work I mean Quantum Computing it must be one of the big ones right well maybe we can come back to Quantum Computing because I think it's a a different sphere altogether almost um but if you again if you look in more kind of mundane terms there's already a a kind of and actually let's look at life sciences specifically in healthcare there is already so much more that we can do technically than we are able to actually do practic Al because it requires uh the Health Care System you know the it requires the clinical people it requires the people who pay for stuff to all get behind and agree to do it and how do you actually Define around a specific technology yeah and ability to redefine the whole care pathway for a specific disease just because you've developed something that delivers you an early Insight uh how do you actually uh you know get the backing that you need to force something into the system uh and to actually accelerate the adoption rather than to just rely on that gradual process of evolution uh if you look and I can call out two or three of our portfolio companies um you there's a a standard view in the UK that actually it's so painful to work with the NHS that one shouldn't really bother yeah yeah um too much trouble uh too little money to be made uh and uh and it's just painful um even if you're try to give stuff away it's painful and uh you know in that context we've got uh two companies uh that are doing very well and a third I think that we're working on that might actually play into the same Dynamic where they've identified uh abstractly the problem of massive waiting list so a big waiting list is something that politicians care about uh big waiting list is something obviously patients care about but also clinicians and so if you take something like uh endoscopy um looking at somebody's esophagus because they've had heartburn for years uh uh might be a multi-year waiting list to see a clinician who will stick something down to your gullet and have a look um it's frustrating for the clinician because you you know that probably only about 10% of the people you examine are going to have something that actually needed to be worried about so you've got this massive waiting list big exercise not very nice for the patient uh but you know your yield is about 10% yeah um we've got a company called cited uh in our portfolio that we invested in last year and they have a very simple solution uh with a complicated test so the the Practical bit of collecting a sample is a pill on a string so you literally swallow the pill uh goes into the stomach it dissolves and a sponge pops out and then a nurse and ideally one that's done it a few times hoes it back up pretty quickly and you almost don't even notice uh and it collects cell samples all along the the wall of the esophagus uh they then do a fairly sophisticated test on that sample and we'll very quickly say whether a patient has something to worry about or not and so suddenly from a clinical perspective all you need to do is look at the 10% that they identify as probably having an issue that's really impactful yeah yeah yeah it is um gosh yeah you can really see how that uh changes um where do you think if you were starting your career now what excites you most where would you be looking if I think back uh uh you know in my own case you know what sort of drove my choices uh uh and I only reflected on this recently um I was probably transitioning from a a view that I just wanted to do music and be a musician uh to thinking actually maybe there are other things I can do and I so there was this moment where you think actually you know if I could do anything what would it be uh and I remember reading an article and I'm pretty sure was in Newsweek and it was in the 1980s and it was talking about uh the miraculous or potentially miraculous breakthroughs in super Computing uh not super conductivity uh and uh you know incredibly interesting if you think about the disruptive impact of superc conductivity and especially room temperature super conductivity you know is everything from distribution of power through the grid suddenly you're not losing so much power in distribution and so maybe you go from having a scarcity to having a glut of of power um because of the way you're consuming it um if you uh if I then project forward actually that drove me into the kind of physics and chemistry and Material Science which you defined the first part of my career took me into working with amorphous materials and working in opto electronics uh and then one thing leads to another and somehow I'm here working in life sciences and uh and investing um but I think you know for anyone uh I think having that opportunity to pause and just to reflect and to pause in the context of thinking actually I could do anything is maybe what we don't often get and what people aren't so often encouraged to do and I think particularly I'm struck by how the UK system streams people really quite early even if itver uh and I think actually for people it doesn't matter if you've just failed all of your gcss and you're not going to do a levels and you're doing something else or you've just failed all of your a levels and you're thinking okay I don't get any of my University places it doesn't matter even if you've done that and you've worked for two or three years I think for people who are very early on in their careers even to take that opportunity to just pause and think you know if I wanted to I could have a completely blank sheet of paper and if I had a completely blank sheet of paper what would I do even if it means I'm going back to some of the fundamentals and actually building a a new foundation's going to live for so long yeah yeah you know we're living for ages like there's no hurry yeah go back and do your gcses again if you want to you know uh do something completely different but do it based on having reflect and identified from a passion perspective actually that's incredibly interesting maybe incredibly impactful I'd love to be a part of solving that problem I think that is a uh brilliant way to finish the episode Tim and we may have to get you on again to talk about Quantum Computing SE sa that for another day but honestly it's so good talking to you about all different things and you give a really uplifting view in a world that can sometimes feel a bit sort of daunting Etc it really is fascinating talking to you about the future of energy and Healthcare Etc so thanks for coming on my pleasure it's all to play for [Music]
2024-06-23