What we talked about
Joseph Kao is the CEO and co founder of Magnefy, a Stanford spin out building predictive monitoring for critical power assets like transformers, inverters, and power cables. He explains how Magnefy uses high frequency magnetic sensing, similar to an ECG for electrical equipment, to listen to the “heartbeat” of the grid and detect early anomalies months in advance. Joseph shares why this matters now: millions of transformers are aging, replacement lead times are stretching into years, and operators need earlier, more reliable signals to prevent costly outages and catastrophic events.
Show notes
Joseph Kao draws a sharp distinction between how the power industry currently monitors transformers, using dissolved gas analysis, which he compares to a blood test trying to predict a heart attack, and what Magnify does, which is more like an ECG: listening directly to the electrical heartbeat of the asset in real time. Sixty-five percent of the 70 million transformers in the US grid are reaching end of life, and lead times to replace them now stretch into years.
What we covered
- Magnify’s core technology is a Hall-Effect magnetic sensor invented at Stanford that measures the magnetic field generated by current flowing through a conductor, translating that signal back into precise electrical information without touching the asset. The sensor is non-invasive and asset-type agnostic, meaning it works on everything from pole-mounted distribution transformers to substation equipment to inverters in solar and wind farms.
- The specific failure class where Magnify outperforms incumbents most clearly is partial discharge and arcing, insulation failures where electrons leak from the conductor and which can eventually cause fires or catastrophic explosions. These failures develop slowly over months and occur at very high frequencies. Compared to traditional HFCT sensors, Magnify’s sensor is 10 times more sensitive, captures five times more data, and delivers nine to twelve months earlier detection.
- AI does two distinct jobs on top of the sensing data: first, it classifies fault types and separates them from environmental noise to eliminate false positives, which is the core credibility problem with existing monitoring systems; second, it performs data fusion across multiple sensor modalities, magnetic, gas, temperature, pressure, to generate a dynamic health index and rank the entire fleet of assets by urgency, giving operators a clear prioritization for maintenance intervention.
- Joseph’s path to Magnify runs from childhood neighborhood recycling in Taiwan with his grandparents, through a PhD in polymer science at UC Berkeley focused on biodegradable materials, to R&D at Dow Chemical, Apple, and Meta, where he led materials work on the Apple Vision Pro and next-generation AR/VR products. His time at Apple also included helping build the company’s battery recycling program, which he says connected directly to his original interest in circularity and resource efficiency.
- As an angel investor through Cal4One, Joseph invests $25,000 to $50,000 checks in very early-stage deep tech hardware founders, looking for people who combine technical expertise with genuine market understanding. He cited Taya, a company started by three Stanford classmates, which pivoted from copper cycling through multiple iterations to an AI wearable necklace and recently closed a fundraising round with Andreessen’s accelerator.
- His advice to his younger self was to have been bolder and pursued entrepreneurship directly after his PhD, rather than seeking the stability of corporate employment. He believes the research foundation he built could have translated into disruptive technology much earlier had he taken the risk sooner.
About Joseph
Joseph Kao is the CEO and co-founder of Magnify, a Stanford spin-out focused on predictive maintenance for critical power infrastructure. He holds a PhD in materials science and previously led hardware and materials R&D teams at Apple and Meta, including contributing to the Apple Vision Pro.
- LinkedIn: https://www.linkedin.com/in/josephkao-sfbay
- Website: https://magnefy.com
Episode 115 of the PreVetted Podcast.
Full transcript
Federico Ramallo (00:01) Welcome back to the pre-vered podcast where we spotlight extraordinary people and remarkable talent reshaping our world. Today we’re joined by Joseph Kao. He has a PhD and is the CEO and co-founder of Magnify, a Stanford spin-out working on predictive maintenance for critical power infrastructure using high frequency magnetic sensing plus edge AI with the goal of
earlier and more accurate failure detection for operators like data centers and utilities. Before Magnify, he led hardware and materials work across Meta and Apple, including managing an R &D team, commercializing optical materials for Apple Vision Pro and NextChain AR and VR. He’s also an active angel investor throughout Cal4One, backing deep tech founders in energy, climate, AI, and robotics.
and he earned a Stanford GSB MSX where he was a Stanford Impact Founder Fellow focused on climate entrepreneurship. Joseph, welcome to the show.
Joseph Kao (01:14) ⁓ thank you, Fedra Co. Really appreciate the opportunity to be here.
Federico Ramallo (01:19) I am
honored to have you here today. So let’s start by talking about what Magnify is.
Joseph Kao (01:29) Yeah, so you summarize actually really well. So we do predictive monitoring for critical power asset to prevent ⁓ very catastrophic failures. For example, we have transformers, inverters, power cable in the utility grid, in manufacturing sites, in data centers. They’re very critical for the power deliveries. What we do is to use our sensor ⁓ and predictive, AI predictive monitoring platform.
to unveil early anomaly and signals and insights months ahead of time very accurately to help the asset operator intervene very early with the maintenance and operations to prevent costly outages. So for example, this may be very important for someone like data center, our utility grids, ⁓ any mission critical ⁓ assets.
Federico Ramallo (02:25) Right, right. Very interesting. Yeah, because ⁓ with vibration, you can detect the failures before they happen so people can have more planning ahead. And particularly infrastructure, it’s so reliable that everything works until it doesn’t, right? Unless they follow a rigorous maintenance plan, which most people don’t, right? Most organizations don’t. ⁓
Joseph Kao (02:44) Yep.
Federico Ramallo (02:54) I think that’s a very interesting problem to solve.
Joseph Kao (03:01) Yeah, exactly. And in fact, in the US, we have 70 million transformers in the grid or in the mission critical facilities behind the meter. But 65 % of them are reaching the end of life, meaning they are pretty much like time bomb in our facilities. They can blow up a lot of times. Usually the average life is around 40 years, but a lot of them are even older than that.
So it’s definitely ⁓ a problem in our aging power infrastructure. We’re going to see a lot of replacement, especially for transformers in the next decades or so.
Federico Ramallo (03:43) Right. I understand they work OK until they start running out of oil. They have a little leak and then it goes super quickly, it goes down.
Joseph Kao (03:56) Yeah, and you can replace the oil. Actually, oil tells you a lot of problems inside the transformer. So people still use this dissolved gas analysis to assess the health of the transformer. But it’s a chemical approach. I always use this analogy. It’s like doing a blood panel, a blood test to detect heart attack.
rather than using ECG. ⁓ And with our approach, we’re using a magnetic sensor, ⁓ kind of like an ECG, but for transformer or any electrical assets. So we directly listen to the heartbeat so that we can get very ⁓ direct, accurate, and ⁓ early insights about the health of any electrical equipment to prevent this type of failure.
Federico Ramallo (04:48) So what made you start Magnify? How did you get into this ⁓ problem set?
Joseph Kao (04:57) Yeah, very good question. ⁓ Actually start, yeah, I will make the story short, but it’s actually related to my upbringing. So I grew up in Taiwan. I did a lot of neighborhood recycling, volunteering with my grandparents. And my grandma always taught me that, you know, every piece of resource is very valuable ⁓ if you can use them for the right purposes, even for ⁓ trash or anything.
So that inspired me to be a material scientist. I came to the US to study plastic polymer science and got my PhD from UC Berkeley. We invented a lot of biodegradable polymers, very unique polymeric materials, and that’s what you mentioned. I contributed to many research and development projects in Dow Chemical, Apple, Facebook, ⁓ and Apple was a transition point in addition to
developing the Apple Vision Pro and the future smart glasses ⁓ with my polymer expertise. I was also helping them to ⁓ build this battery recycling program because Apple tried to ⁓ really close the loop for their battery supply chain and that just resonated with me ⁓ on my original mission ⁓ to study material science. I was really motivated by this idea of circularity.
recycling, how to best utilize the existing resources. So I decided to come to Stanford to explore the energy industry, started with this hypothesis on using battery for say data center, energy storage to make our grid more resilient, and then use a very similar diagnostics technology, but pivoted for predictive monitoring for power assets. ⁓ It’s actually the same idea.
Federico Ramallo (06:46) and family.
Joseph Kao (06:55) It’s always the same North Star that guided me throughout the entire journey, this idea about how we can best utilize the existing resources in the power infrastructure to provide a more resilient ⁓ energy future. So from recycling or repurposing lithium-ion batteries to ⁓ gaining earlier insights on this very important yet aging.
Federico Ramallo (07:05) Thank you.
Joseph Kao (07:24) ⁓ asset in our power infrastructure. So that’s what motivated me to start this. And as I did deeper into this problem, that’s kind of what I just shared, you know, I realized that it’s such an intriguing issue about especially on transformer. It’s a very important asset to step up and down the voltage to transmit for power transmission and distribution.
But the problem is that, again, they are just getting very old. And also another important market pull is that the lead time for Transformers is getting longer and longer. So it takes like years to replace a transformer. The supply chain has been very stressed. that’s because of all this, what I learned, my original motivation and everything I learned from my market research at school at Stanford.
It really ⁓ all came together in the past few years to help me build Magnify.
Federico Ramallo (08:30) Right, right. That’s amazing. I grew up in Argentina and moved to Mexico 17 years ago. And they had the same issue in Argentina. We ⁓ not find spare parts for vehicles. Sometimes I would have to make ⁓ with other spare parts, build other cars, right? Or get used parts and just…
Joseph Kao (08:37) you
you
Federico Ramallo (08:58) know, refurbishment, know, in DIY kind of situation. ⁓ So I can see how that, you know, that becomes an issue, right? I mean, in the US, it’s easier to just buy something new. The labor is so expensive than, you know, for people, they usually do that, right? But, but ⁓
Joseph Kao (09:02) Yep.
Yep.
Yeah.
Federico Ramallo (09:26) coming from ⁓ an economy of scarcity, then you appreciate the value of reusing and being able to refurbish parts, right?
Joseph Kao (09:39) Yeah, it’s actually interesting refurbishing a transformer is now a booming business in the US as well. people will actually ⁓ just change some parts inside the failed transformer and the lead time can be shorter in that case rather than buying a new transformer. So yeah, it’s also a very good example of a circular economy.
Federico Ramallo (10:07) Yeah, I had a VW bus many years ago and I was a broke student and couldn’t get the parts to fix it. So the steering wheel has a block of plastic, right? Rubber to reduce vibration on the wheel. And I could not find it. I could not get it. So what I did is I got milk boxes and I cut them to the shape of the block. And then I did a sandwich.
with the existing rubber block that was kind of, ⁓ know, the original shape kind of got lost and it was not as hard as it used to be. And we put together a lot of those, we paste them together, we put it on again and it was amazing. mean, it was almost as new, right? It worked, yeah.
Joseph Kao (10:47) Yes.
Yeah, I know. ⁓
Federico Ramallo (11:06) So who need this most? mean, residential data centers, utilities.
Joseph Kao (11:15) Yeah, so they all have very different, I would say, perspective about our solutions. But I would say we have been working a lot with independent power producers. So these are the one.
who provide energy, power. They generate power using solar, wind, or even fossil fuels. But in the US at least, a lot of the IPP, we call it IPP, independent power producers, their transformers or even inverters are getting very old.
⁓ I think on average the ⁓ renewable projects are say 15, 20 years. Of course there are a lot of new projects but because we installed a lot of solar wind ⁓ projects ⁓ say a decade ago and these projects are getting old and they are getting worried about the aging transformers and the lonely time to replace them and so these are ⁓ from a lot of our market research.
⁓ and also customer engagement who are eager to get more data about their transformers so that they can plan for the replacement and avoid this type of outage on their aging transformers.
Federico Ramallo (12:36) I find this very interesting. So ⁓ what does your sensors measure?
Joseph Kao (12:45) So we are using a magnetic sensor called Hall-Effect sensor and some other sensors. So it’s actually a hypersensing platform including this Hall-Effect sensor that we invented at Stanford. And this sensor is a magnetic sensor that measures the magnetic signal from any conductor. Say for example, you have a wire ⁓ coming from your appliances.
In this case, transformers, inverters, switchgear, we can measure the magnetic field ⁓ generated by the current flowing through that wire ⁓ and translate that back to electrical information, in this case, the voltage. ⁓ So it is a magnetic sensor, but people also use it for current sensing because the current flow actually generates magnetism around the conductor.
So that’s why I call it, we are kind of like listening to the heartbeat of the electrical asset because we literally measure ⁓ the sine wave of the current flowing through any electrical equipment ⁓ using this magnetic sensor.
Federico Ramallo (14:06) Right, right. ⁓ I know those devices works like magic. It’s amazing how they work.
And what does the AI has to do with the product? Are you processing the data?
Joseph Kao (14:25) Yeah, yes, I think that’s another thing I’m very excited about. I think sensor only collects very high fidelity data, but how can you, you know, ⁓ really synthesize the insights based on this data? That’s where the analytics become important. Traditionally, ⁓ classification of different types of failure.
⁓ use on the electromagnetic waveform is very poor and hence people get a lot of false positive and it’s hard to understand you know the the actual faults ⁓ and how bad it is so that’s what exactly the first thing that AI does in our platform so it actually does a great job on classifying the fault type and decouple the fault from the noise in the environment
so that we can know what is the type of the fault and how bad it is. ⁓ And a second layer of the AI that is doing is in addition to our sensing data, if the operators are also collecting, say, the gas sample, the temperature, the pressure from the transformer, we can also use the AI to do data fusion to provide, we call it the dynamic health index.
basically a holistic assessment of the health of this transformer and even do the ranking of all the transformers the operators have to provide a very comprehensive view of the fleets, the health of the entire fleets of the asset. So in short, the AI is doing the fault, the signal classification ⁓ and to provide ⁓ the insights accurately.
Federico Ramallo (16:14) Okay.
Joseph Kao (16:17) without false positive. And the second thing is to do data fusion on multimodal data to translate all kinds of data into the actionable for the operators.
Federico Ramallo (16:32) Interesting, ⁓ Because the issue is false positives, right? Having false positives kind of reduces the trustworthiness of the alerts, right?
Joseph Kao (16:41) Yeah.
you
Exactly.
Federico Ramallo (16:51) Have you done a comparative analysis between assets? Being able to fit out patterns in different areas.
Joseph Kao (16:58) Yes.
Sorry, say that again. I miss you a little bit.
Federico Ramallo (17:06) Yeah, so are you doing also comparative analysis between assets, of being able to see patterns of behavior between one area versus another area or, ⁓ you use type, ⁓ you know, if it’s industrial, if it’s residential that could help you predict. ⁓
Joseph Kao (17:30) Yeah, that’s a very good question. So the sensor itself is acid type agnostic. So it actually works for different types of transformer from the one you see on the electric pole to substation or even in your manufacturing facilities. ⁓ But that’s exactly what we are working on now, the focus for this year. So we start to have the first paying pilot and more pilots in the pipeline.
The idea is to test the solution in different types of environment to validate the accuracy of our solution. So you’re totally correct. That’s the focus now before the full commercialization in the next few years to confirm that in what type of environments the sensor may work the best. And the idea is that with our very robust sensor and the AI analytics platform,
it can be a very robust solution for all kinds of environments.
Federico Ramallo (18:30) Right, interesting. What is one failure you can catch with this technology earlier compared to the old technology or the current technology?
Joseph Kao (18:41) Yeah, so that’s a great question as well. We actually really shine at very high frequency ⁓ failure, something such as partial discharges, arcing. So these are the failure basically meaning the electron leaking from the conductor and we have start to have say insulation failure in any type of electrical equipment.
This is the type of failure that can turn the asset into fire events or catastrophic explosion, but take a very long time to develop. And because of the high sensitivity and the amount of data we can collect with our sensor, we’re able to pick up very high quality information from this type of events very early on. So we’ve proven that compared to the incumbent solution, for example, the CT
HFCT sensor, we are 10 times more sensitive and capture five times more data, which translates to even nine to 12 months earlier detection than the traditional CT sensor. So the type of installation, high frequency installation failure, that’s very finicky to catch, but happens at very high frequency, takes a long time to develop. I think that’s where we really shine the most with this solution.
Federico Ramallo (20:05) Right. Right. Interesting. And what is the biggest challenge to install this technology in the real world?
Joseph Kao (20:17) Yeah, so the good thing is the sensor is non-invasive, but definitely on the product design side, we are also learning that how can we make an installation in the, the big transformer in the substation or the transformer on the pole very easy. ⁓ So that’s something we’re working with the customers on. I would say ⁓ because the transformer locations are usually not easy to access ⁓ in the grid.
So ⁓ that just makes it difficult to install. But the one that’s, sitting in the manufacturing sites or data centers, those are the one that ⁓ can be accessed easily compared to the other ⁓ assets in the utility grids.
Federico Ramallo (21:10) Right, right, very interesting. So changing a little bit of topic, how did Apple or Meta prepare you to be a founder?
Joseph Kao (21:24) Yeah, I’m forever grateful for my experience in biochemical, Apple and Facebook because I think they really helped me in, I would say two different aspects that’s very important to the founder. ⁓ How to de-risk? First of all, be very creative, resourceful when it comes to product development. ⁓ I think even in Apple, Facebook, oftentimes,
we are facing very challenging technical problem ⁓ and sometimes without a lot of resources in the very early stage. How do you use existing resources to prove that this problem can be de-risked and how? How to build that roadmap to effectively de-risk a very difficult technical problem in a short period of time and limited resources. I find that to really resonate with my
experience building a startup because we are really introducing new innovations to the market with very limited ⁓ human resources, financial resources, so how we can, you know, de-risk and ⁓ scale. ⁓ That’s something I think the Apple Meta experience was really helpful. Of course, the second big category, I would say, is definitely the leadership.
I grew my team from nothing to 10 people in Apple so quickly. And that experience just really taught me how to, first of all, think about the type of team, the type of people, the culture you want to build and cultivate as a leader. And from there, how do you and what do you do to attract the people who you want to hire, who share the same vision and culture?
⁓ That’s something I think another important thing ⁓ about building a startup. Because I think many people know that startup fails because of a few major reasons. One of them is the team. So how do you make sure you know the type of culture, the type of team you want to build, and more importantly, find the right tactics to attract and hire these type of people and keep them ⁓ in the team.
Federico Ramallo (23:42) Thank you.
Right, Amazing. ⁓ Yeah, I think that those experiences are great because with if you serve a company from nothing, you are going to make more mistakes, but you have limited resources, so you have less bandwidth to make mistakes, right? On the organizational side, and then you have the product side, right? You’re going to make mistakes on the product side that you’re going to have to figure out, right, and learn. So,
Joseph Kao (24:05) Yeah.
Yeah.
Federico Ramallo (24:19) ⁓ and you have a runway that you’re working with. So, ⁓ you have a very limited amount of moves that you can make, right? So it becomes much more important because that way you can reduce the amount of, you can focus your energy on building better products,
Joseph Kao (24:32) Yeah. Yeah.
Yeah, exactly. I find that in the early stage for any startup having a compelling product that resonates with the customers, which requires a lot of iterations and execution, fast execution, that relates to a strong team. Without a strong team, it’s really hard to have great execution. So I think the two really goes ⁓ hand in hand, and that’s where my Apple
Meta experience just becomes so valuable ⁓ for my founders experience.
Federico Ramallo (25:16) Right, Yeah, I think that’s amazing. ⁓ So you also had experience with AR and VR, right? Is that on the material side, on the software side? you tell us a little bit more about your experience?
Joseph Kao (25:36) Yeah, it’s on the material side. So I was trained as a material scientist. I joined both Apple and Meta to help them develop new display materials for their Apple Vision Pro and also future products and the VR products. that’s my core responsibilities.
Federico Ramallo (25:57) Right. Yeah. I find that amazing. I mean, it’s a shame that it hasn’t became so popular as, know, a few years ago, people were talking about AR is the future and it kind of didn’t, you know, didn’t scale as, you know, we thought it would. Right.
Joseph Kao (26:19) Yeah, but you know, I think that experience was also a great journey for me because actually all these companies ⁓ actually spent a lot of time doing customer research and demo. They tried to really understand, you know, how this new technology and ecosystem can benefit their existing or potentially new customers. in fact, when I was in Apple,
and Facebook, we spend so much time as an engineering manager and program manager, I spend so much time not just on developing the technology, but also really working with the product team to understand what is the potential use cases and how does that impact the customers, the users and why. And with that context and understanding, it really helped us.
who are providing the fundamental components for all these important products to make them to reach the specification and the requirements for the target user experience. So that’s a great ⁓ experience. ⁓ I’m still hopeful on what both companies can do with AR and VR, but I think that product experience translating
user requirements to engineering specification that really benefits my founder’s journey as well.
Federico Ramallo (27:51) amazing. And you’re also an angel investor, right? Can you tell us what do you look for in a founder?
Joseph Kao (27:58) Yes.
Yeah, ⁓ so my parents are very successful entrepreneur and I’m also working with them to invest in next generation hardware, deep tech founders. ⁓ So we do invest in around 25 to 50K check in the very, very early stage in this type of companies. We usually look for the founders who are not just technically sound, but they also
know why they’re building this at the personal level and also from the market perspective. ⁓ I think the founder who has that ⁓ technical expertise, acumen and also the business side, know, ⁓ yeah, the business understanding of the market and what the potential customers want and how to get there quickly even in a hardware and deep tech product development environment. I think that’s
while we’re looking for someone who has a very good balance of technical expertise and the business ⁓ acumen. So it can be from one single founder or a team of ⁓ founders with very complimentary skill sets.
Federico Ramallo (29:21) Right. Right. Yeah, I find very interesting. The ancient investment is is I one of the things that I appreciate the most about ancient investors is that they’re giving back to the community. It’s not only the investment is also the coaching, the support is, you know, finding smart money becomes more much more important than than the cash. Right.
Joseph Kao (29:52) Exactly. think that ⁓ you are pretty spot on on that because that’s what I think all of us are feeling great about this angel investment ⁓ efforts. It’s not just about the financial support. think how we can work, yeah, partner with the founders and support them when they need ⁓ the early stage investors from strategic planning to
even team building everything, right? So I think that’s a very rewarding experience and partnering with these founders to see them grow over the years. Even though we’ve only been doing this in the past three years, it’s already just such a very rewarding experience to grow with the founder and see how the business go from nothing to something. So ⁓ one very good example is Taya.
So actually, it started with three of my classmates in Stanford. ⁓ And they started working on copper cycling and then pivoted so many times and now they’re doing this AI wearable, AI necklace. And recently, Elena finished fundraising and Andreessen speed round accelerator. Yeah, it’s just ⁓ amazing to see how the team went through.
this entire journey with such a resilience and perseverance ⁓ and how we can support them throughout the journey is just a very interesting and rewarding experience.
Federico Ramallo (31:32) Amazing, amazing. think that’s a beautiful story. It’s hard enough to build something out of nothing, right? And then having a team that can support you, give you encouragement, ⁓ that is a game changer in my opinion.
Joseph Kao (31:54) Yeah, exactly.
Federico Ramallo (31:59) So talking a little bit about climate, what is one climate or energy trend you’re ⁓ excited about?
Joseph Kao (32:12) Yeah, I think everyone is hearing about data center and how it’s competing the power ⁓ demand, right, with everyone else. So I’m very excited about the behind the meter from energy, the alternative from energy from ⁓ say the small modular nuclear reactor to geothermal ⁓ to even just solar wind and battery storage.
how all these alternative energy sources and the energy storage assets can help us ⁓ have a more resilient energy supply and ⁓ power grid. I think that’s something I feel very excited about.
Federico Ramallo (33:00) Right, right. Yeah, I think that that’s amazing. ⁓ Have you heard about the nuclear power investment they’re doing in Argentina just for the data centers?
Joseph Kao (33:11) not really. Yeah. ⁓ okay.
Federico Ramallo (33:13) So
Argentina had a nuclear power station for, I don’t know, 40 years, 50 years, something like that, right? And the government decided to stop investing in that technology. So there were two big ones, Atucha 1 and Atucha 2. And recently, I think,
Joseph Kao (33:21) Uh-huh.
Mm-hmm.
Federico Ramallo (33:42) Less than a year ago, they announced that they’re going to invest heavily on atomic energy to power the future data centers for AI. So I think that’s going to be very interesting to see happen. Yeah.
Joseph Kao (33:59) Yeah, yeah, I totally agree. And there’s also geothermal, ⁓ yeah, all kinds of new alternative energy resources. just, yeah, very excited to see all these coming together.
Federico Ramallo (34:14) Yes, yes. That way we can have an array of different type of energies. And once we have energy abundance, then the data centers can leverage that to have more AI power hungry processes. So, know, you go ahead.
Joseph Kao (34:28) Exactly.
Yes, exactly. Yeah, I do. Yeah.
I was just saying that. Yeah, that’s why. And I think that extends to residential as well. In addition to the industrial data center setting, even in residential, ⁓ people start to have solar and battery storage in their home. I think that will also help stabilize the ⁓ grid as well. For example, we actually invested in a company called Raya Power, R-A-Y-A. ⁓ They are
their product is a battery storage system that you can put in the backyard. ⁓ And that’s also something that can help people, ⁓ especially underserved community, ⁓ using this very affordable solar battery to ⁓ get more affordable ⁓ energy.
Federico Ramallo (35:39) Yeah, we’re back.
Joseph Kao (35:41) Ha ha.
Federico Ramallo (35:43) So what advice would you give to a younger self before your PhD or before your first job?
Joseph Kao (35:56) ⁓ I would tell myself to be bolder, be more courageous and pursuing risk. think ⁓ I do have to say I was feeling, yeah, because PhD was very uncertain. It’s a very difficult time ⁓ for many researchers, Uncertain and ⁓ of course the salaries is not great. So I was pursuing stability right after PhD.
and hence I went to the corporate world. But looking back, I think with my research and backing the PhD and the energy, I definitely could have contributed a lot to entrepreneurship and build some very disruptive technology that makes an impact to the world. yeah, so if I, yeah, I would tell myself to just be bolder and.
to pursue something impactful.
Federico Ramallo (36:58) Right, right. Amazing, amazing. So we’re running out of time, but I truly appreciate it, Joseph, being you here today. We learned a lot. ⁓ I think that you have a lot of potential with Magnify. I’m looking forward to see where you take the company next. Any final remarks before we wrap it up?
Joseph Kao (37:20) ⁓ Yeah, I just want to say thank you, Vedrico, for hosting me. ⁓ Really enjoyed the conversation. It’s always good to share my experience and story, and hopefully it can be a motivator for many young entrepreneurs out there to make an impact to the world. yeah. Thank you.
Federico Ramallo (37:41) Thank you.
How did you feel?