Jon Krohn explores the rapid rise of solar energy from its origins at AT&T Bell Labs to its current global impact. He breaks down the factors driving its exponential growth and discusses the future potential of solar power, along with the role of AI and data science in this energy revolution.
In this week’s FMF, Jon talks about the amazing rise of solar energy since AT&T Bell Labs introduced solar cells 70 years ago. What started as a battery replacement for remote areas has turned into a global phenomenon, with solar panels now covering an area the size of Jamaica and providing about 6% of the world’s electricity. This rapid growth is impressive, with solar capacity doubling every three years. If this trend continues, by 2034, solar power could meet 60% of the world’s electricity needs.
Jon explains why this growth is happening so fast. As production increases, costs drop, which boosts demand and further increases production. Solar power relies on abundant resources like silicon-rich sand and sunlight, unlike fossil fuels that become harder to extract over time. Although challenges like storage solutions and electrifying heavy industries remain, advancements in battery tech and synthetic fuels are making progress. Data science and machine learning are also optimizing these technologies, from finding new materials to improving battery performance.
Lastly, Jon explores how AI and data science can further advance solar energy and combat climate change. AI can accelerate the discovery of new photovoltaic materials, optimize solar panel production, and improve the efficiency of wind farms and freight transport. The shift to cheaper, abundant solar energy will boost productivity and make essential services more accessible. If you enjoyed this episode, share it, leave a review, or subscribe for more insights. Thanks for listening, and we’ll catch you in the next episode!
ITEMS MENTIONED IN THIS PODCAST:
- The exponential growth of solar power will change the world
- SDS 789: ML for Wind-Powered Energy Generation, with Dr. Jason Yosinski
- SDS 773: Deep Reinforcement Learning for Maximizing Profits, with Prof. Barrett Thomas
- SDS 459: Tackling Climate Change with ML, with Vince Petaccio
- SDS 783: Generative A.I. for Solar Power Installation, with Navdeep Martin
- SDS 461: MLOps for Renewable Energy, with Sam Hinton
DID YOU ENJOY THE PODCAST?
- What role do you believe AI and data science will play in overcoming the challenges of energy storage and heavy industry electrification?
- Download The Transcript
Podcast Transcript
(00:05):
This is Five-Minute Friday on how AI x Solar Power = Abundant Energy.
(00:27):
Welcome back to The Super Data Science Podcast. I’m your host, Jon Krohn. Let’s start off with a couple of recent reviews of the show. Our first one today is a five-star review from Mila Vasiuk. She is a Strategic Analytics Vice President at Deutsche Bank in Berlin, and she says: “I have been listening to the Super Data Science podcast since forever and I’ve learned so much, hence I keep recommending it to people! Thank you for your hard work and great episodes.” Thank you Mila for that and we will do our best to keep it up. Thank you for continuing to recommend the show to people, really appreciate it
Welcome back to The Super Data Science Podcast. I’m your host, Jon Krohn. Let’s start off with a couple of recent reviews of the show. Our first one today is a five-star review from Mila Vasiuk. She is a Strategic Analytics Vice President at Deutsche Bank in Berlin, and she says: “I have been listening to the Super Data Science podcast since forever and I’ve learned so much, hence I keep recommending it to people! Thank you for your hard work and great episodes.” Thank you Mila for that and we will do our best to keep it up. Thank you for continuing to recommend the show to people, really appreciate it
(01:00):
And our second review comes from John Ryan Kivela, who’s a Director of Healthcare Analytics in California, he says: “I’m very grateful for the content you bring through the Super Data Science Podcast. It’s my daily drive! I’m sort of the one man data science show for my company, and so it’s helpful to hear stories from other folks in the community!” Nice John, great to be able to support you there a bit and make you feel like you are part of the broader data science team. Hopefully over time that one man data science show grows out there for you at your company.
And our second review comes from John Ryan Kivela, who’s a Director of Healthcare Analytics in California, he says: “I’m very grateful for the content you bring through the Super Data Science Podcast. It’s my daily drive! I’m sort of the one man data science show for my company, and so it’s helpful to hear stories from other folks in the community!” Nice John, great to be able to support you there a bit and make you feel like you are part of the broader data science team. Hopefully over time that one man data science show grows out there for you at your company.
(01:35):
Thanks to everyone for the recent ratings and feedback on Apple Podcasts, Spotify and all the other podcasting platforms out there that you use, and thanks as well for the likes and comments on our YouTube videos. Apple Podcast reviews in particular are helpful to us because they allow you to have written feedback, so when people are thinking about what shows to look at, they read those written reviews and so yeah, if you write an Apple Podcast review, I keep a close eye on those and will be sure to read it on air like the reviews I read today, if you do that.
Thanks to everyone for the recent ratings and feedback on Apple Podcasts, Spotify and all the other podcasting platforms out there that you use, and thanks as well for the likes and comments on our YouTube videos. Apple Podcast reviews in particular are helpful to us because they allow you to have written feedback, so when people are thinking about what shows to look at, they read those written reviews and so yeah, if you write an Apple Podcast review, I keep a close eye on those and will be sure to read it on air like the reviews I read today, if you do that.
(02:03):
All right, now let’s dig into today’s episode topic, which is a force that’s rapidly reshaping our world: the exponential growth of solar power. So, little bit of history. Seventy years ago now, the iconic AT&T Bell Labs unveiled cells that could transform sunlight into power. What started then as a potential replacement for batteries in remote locations has now become a global phenomenon. Today, solar panels cover an area the size of Jamaica and provide approximately 6% of the world’s electricity.
All right, now let’s dig into today’s episode topic, which is a force that’s rapidly reshaping our world: the exponential growth of solar power. So, little bit of history. Seventy years ago now, the iconic AT&T Bell Labs unveiled cells that could transform sunlight into power. What started then as a potential replacement for batteries in remote locations has now become a global phenomenon. Today, solar panels cover an area the size of Jamaica and provide approximately 6% of the world’s electricity.
(02:40):
Six percent may not sound like a lot, but we’ve gotten to this 6% terrifically rapidly thanks to the exponential growth of solar capacity. Words like “exponential” get thrown around pretty indiscriminately in tech these days, but in this case, it really is an accurate term to use. Every three years, the installed solar capacity on our planet doubles, resulting in a tenfold increase each decade. To put that into perspective, with some more concrete numbers, in just ten years, solar power has grown from being a tenth of its size, so ten years ago it provided half a percent of the world’s electricity, whereas today, it’s 10x more which is about 6% of the world’s electricity. If this trend were to continue, a tenfold increase over the coming decade would mean 60% of the world’s electricity needs would be covered by solar power by 2034. As a point of comparison, that’d be equivalent to multiplying the world’s entire fleet of nuclear reactors by eight, in less time than it takes to typically build a single nuclear fission reactor.
Six percent may not sound like a lot, but we’ve gotten to this 6% terrifically rapidly thanks to the exponential growth of solar capacity. Words like “exponential” get thrown around pretty indiscriminately in tech these days, but in this case, it really is an accurate term to use. Every three years, the installed solar capacity on our planet doubles, resulting in a tenfold increase each decade. To put that into perspective, with some more concrete numbers, in just ten years, solar power has grown from being a tenth of its size, so ten years ago it provided half a percent of the world’s electricity, whereas today, it’s 10x more which is about 6% of the world’s electricity. If this trend were to continue, a tenfold increase over the coming decade would mean 60% of the world’s electricity needs would be covered by solar power by 2034. As a point of comparison, that’d be equivalent to multiplying the world’s entire fleet of nuclear reactors by eight, in less time than it takes to typically build a single nuclear fission reactor.
(03:52):
So, by the mid-2030s, the projections are that solar cells are likely to become the single biggest source of electrical power on our planet. If these exponential projections manage to continue further, that means that by the 2040s solar could be the largest source of all energy, not just the largest source of electricity. Results of other factors on that kind of timeline that could come in to the mix, for example if we manage to crack nuclear fusion and make that affordable than maybe we could be diverting most of our resources into that. But if that doesn’t end up happening, we don’t end up cracking nuclear fusion, we still have the huge amount of potential here in solar power. I’m going to go into that in a bit more detail.
So, by the mid-2030s, the projections are that solar cells are likely to become the single biggest source of electrical power on our planet. If these exponential projections manage to continue further, that means that by the 2040s solar could be the largest source of all energy, not just the largest source of electricity. Results of other factors on that kind of timeline that could come in to the mix, for example if we manage to crack nuclear fusion and make that affordable than maybe we could be diverting most of our resources into that. But if that doesn’t end up happening, we don’t end up cracking nuclear fusion, we still have the huge amount of potential here in solar power. I’m going to go into that in a bit more detail.
(04:36):
The reason why we can continue to have more and more solar power, even those kinds of crazy numbers like 60% of the world’s electricity in 10 years is because current trends suggest that the all-in cost of solar-produced electricity could drop to less than half of today’s cheapest options. So it keeps on getting cheaper and so that makes it easier to install more and more and more of it.
The reason why we can continue to have more and more solar power, even those kinds of crazy numbers like 60% of the world’s electricity in 10 years is because current trends suggest that the all-in cost of solar-produced electricity could drop to less than half of today’s cheapest options. So it keeps on getting cheaper and so that makes it easier to install more and more and more of it.
(05:03):
Now, you might be wondering: Is this too good to be true? Can solar’s exponential growth really continue like it has over the past decades? The economics suggest “yes”. And the first reason why is that as production of solar cells increases, costs decrease. We get better and better and better at it, and I’m going to talk about some reasons why AI factors into that, a little bit in the episode. And as costs decrease that drives up demand, which in turn increases production, further reducing costs. You have this virtuous cycle, production increases costs decrease, lower costs increase demand which increases production and further reduces cost. This virtuous cycle shows no signs of slowing down. Indeed, this virtuous cycle is integral to solar’s continued ongoing exponential growth and its great promise.
Now, you might be wondering: Is this too good to be true? Can solar’s exponential growth really continue like it has over the past decades? The economics suggest “yes”. And the first reason why is that as production of solar cells increases, costs decrease. We get better and better and better at it, and I’m going to talk about some reasons why AI factors into that, a little bit in the episode. And as costs decrease that drives up demand, which in turn increases production, further reducing costs. You have this virtuous cycle, production increases costs decrease, lower costs increase demand which increases production and further reduces cost. This virtuous cycle shows no signs of slowing down. Indeed, this virtuous cycle is integral to solar’s continued ongoing exponential growth and its great promise.
(05:56):
And, critically, unlike previous energy transitions that we made in the world — so we made from wood power to coal power, and then coal power to oil power, and then oil to natural gas — unlike any of those previous energy transitions, solar power faces no significant resource constraints. So all those historical energy sources — wood, coal, oil, gas — those resources became more scarce the more popular that energy source became. So in those previous instances, as you wanted to make more use of wood or coal or oil or gas, it meant that the resources were depleting faster and costs went up, so there was a negative feedback loop. So this solar transition is different because it has just a positive feedback loop. The main ingredients for solar’s growth are silicon-rich sand, sunny places, and human ingenuity — all of which are abundant. So that combined with the virtuous cicle we already talked about, where production of solar cells increases, cost decrease, lower costs drive up demand which in turn increases production which further reduces cost, so that can just keep going and going and going, we are not going to run out of sand, sunny places, those are the kind of resources. Even the energy required to produce solar cells itself is becoming increasingly available through solar power.
And, critically, unlike previous energy transitions that we made in the world — so we made from wood power to coal power, and then coal power to oil power, and then oil to natural gas — unlike any of those previous energy transitions, solar power faces no significant resource constraints. So all those historical energy sources — wood, coal, oil, gas — those resources became more scarce the more popular that energy source became. So in those previous instances, as you wanted to make more use of wood or coal or oil or gas, it meant that the resources were depleting faster and costs went up, so there was a negative feedback loop. So this solar transition is different because it has just a positive feedback loop. The main ingredients for solar’s growth are silicon-rich sand, sunny places, and human ingenuity — all of which are abundant. So that combined with the virtuous cicle we already talked about, where production of solar cells increases, cost decrease, lower costs drive up demand which in turn increases production which further reduces cost, so that can just keep going and going and going, we are not going to run out of sand, sunny places, those are the kind of resources. Even the energy required to produce solar cells itself is becoming increasingly available through solar power.
(07:24):
Naturally, challenges do remain; we can’t continue to have that exponential growth without, for example the human ingenuity part. But, luckily, many of the challenges we face can be at least partially addressed by data science and machine learning. For example, solar power needs to be complemented with storage solutions, energy storage solutions and other technologies to meet our 24/7 energy demands, and that’s because the sun isn’t always shining, it’s not always daytime and not everyone lives in a place that’s always sunny, and so we need energy storage and alternative energy source for during those times, potentially at least for the time being. And so if you want to learn more about how data science can make a difference in that space, particularly in energy grid management and battery storage, you check out Episode #461 of this podcast with Sam Hinton. All right, so that’s one opportunity for you there.
Naturally, challenges do remain; we can’t continue to have that exponential growth without, for example the human ingenuity part. But, luckily, many of the challenges we face can be at least partially addressed by data science and machine learning. For example, solar power needs to be complemented with storage solutions, energy storage solutions and other technologies to meet our 24/7 energy demands, and that’s because the sun isn’t always shining, it’s not always daytime and not everyone lives in a place that’s always sunny, and so we need energy storage and alternative energy source for during those times, potentially at least for the time being. And so if you want to learn more about how data science can make a difference in that space, particularly in energy grid management and battery storage, you check out Episode #461 of this podcast with Sam Hinton. All right, so that’s one opportunity for you there.
(08:19):
Another obstacle that we have with this exponential growth of solar power is that there are particular industries, like heavy industry, aviation, and freight where electrifying presents obstacles. If self driving trucks that are battery powered, they had to be stopping every few hundred kilometres to recharge, it would be a huge drag on freight. And same thing with trains and planes, and yeah, heavy industry, so big factories, all those kinds of places, electrifying those could be tricky. But, advancements in battery technology and particularly something called electrolysis-created fuels are gradually addressing these issues. And you can leverage data science to help on these fronts as well. For example, in terms of battery technology advancements, AI can accelerate materials discovery by rapidly screening and predicting properties of new materials for better electrodes and electrolytes. Machine learning models can simulate and optimize battery architectures for specific use cases. And AI can enhance battery management systems for better performance and longevity. So that’s pretty cool, there’s lots of opportunities there for you where you can make a big impact in the world.
Another obstacle that we have with this exponential growth of solar power is that there are particular industries, like heavy industry, aviation, and freight where electrifying presents obstacles. If self driving trucks that are battery powered, they had to be stopping every few hundred kilometres to recharge, it would be a huge drag on freight. And same thing with trains and planes, and yeah, heavy industry, so big factories, all those kinds of places, electrifying those could be tricky. But, advancements in battery technology and particularly something called electrolysis-created fuels are gradually addressing these issues. And you can leverage data science to help on these fronts as well. For example, in terms of battery technology advancements, AI can accelerate materials discovery by rapidly screening and predicting properties of new materials for better electrodes and electrolytes. Machine learning models can simulate and optimize battery architectures for specific use cases. And AI can enhance battery management systems for better performance and longevity. So that’s pretty cool, there’s lots of opportunities there for you where you can make a big impact in the world.
(09:39):
And then let’s talk a bit more about those synthetic fuels, those electrolysis created fuels. This is something I just learned about recently but we can use electricity to create synthetic fuels. And so a synthetic replacement of natural gas for example is something called e-methane, so it’s like e-mail, you put e in front of it an e-, and that’s the kind of like electrically produced version of that. So synthetic replacement for natural gas is e-methane. A replacement for powering powering airplane turbines is e-kerosine, and so on. And so that’s a really exciting opportunity space there. And again data science can make a difference.
And then let’s talk a bit more about those synthetic fuels, those electrolysis created fuels. This is something I just learned about recently but we can use electricity to create synthetic fuels. And so a synthetic replacement of natural gas for example is something called e-methane, so it’s like e-mail, you put e in front of it an e-, and that’s the kind of like electrically produced version of that. So synthetic replacement for natural gas is e-methane. A replacement for powering powering airplane turbines is e-kerosine, and so on. And so that’s a really exciting opportunity space there. And again data science can make a difference.
(10:22):
So AI can optimize electrolysis processes for higher efficiency and lower costs of synthetic fuel production. Machine learning can help identify more effective catalysts for electrolysis. And data science can predict demand for electrolysis-created fuels, aiding in production planning. So those are the kinds of big problems that we would face if we would continue solar exponentially and how we can kind of address them.
So AI can optimize electrolysis processes for higher efficiency and lower costs of synthetic fuel production. Machine learning can help identify more effective catalysts for electrolysis. And data science can predict demand for electrolysis-created fuels, aiding in production planning. So those are the kinds of big problems that we would face if we would continue solar exponentially and how we can kind of address them.
(10:48):
But there are also other opportunities for your to leverage data science for supporting solar energy production directly. And that includes using AI to accelerate the discovery and development of new photovoltaic materials. Hard to say that, photovoltaic materials. Another thing is generative AI can predict where solar projects will be successful – you can listen to Episode #783 with Navdeep Martin for more on that. And AI can also optimize solar-panel production processes, reducing defects and improving yield.
But there are also other opportunities for your to leverage data science for supporting solar energy production directly. And that includes using AI to accelerate the discovery and development of new photovoltaic materials. Hard to say that, photovoltaic materials. Another thing is generative AI can predict where solar projects will be successful – you can listen to Episode #783 with Navdeep Martin for more on that. And AI can also optimize solar-panel production processes, reducing defects and improving yield.
(11:22):
And, if you’re looking to make an impact with AI on climate change more generally, not just solar power space, three more ways examined in recent episodes of this show include episode #789 with Dr. Jason Yosinski – he provided a number of ways that AI can be used to make wind farms and their associated power grids markedly more efficient. In episode #773 with Prof. Barrett Thomas, he talked about how deep reinforcement learning can calculate more energy-efficient freight-transport routes and even leverage autonomous vehicles and drones to save energy usage. And then last recommendation for you going back a couple of years is episode #459 with Vince Petaccio for a broad range of ideas for using machine learning to tackle climate change.
And, if you’re looking to make an impact with AI on climate change more generally, not just solar power space, three more ways examined in recent episodes of this show include episode #789 with Dr. Jason Yosinski – he provided a number of ways that AI can be used to make wind farms and their associated power grids markedly more efficient. In episode #773 with Prof. Barrett Thomas, he talked about how deep reinforcement learning can calculate more energy-efficient freight-transport routes and even leverage autonomous vehicles and drones to save energy usage. And then last recommendation for you going back a couple of years is episode #459 with Vince Petaccio for a broad range of ideas for using machine learning to tackle climate change.
(12:04):
Hopefully something amongst all these opportunities is inspiring to you. The implications of this energy transition, particularly the currently exponentially-growing solar revolution, are profound. Cheaper energy will boost productivity across all sectors. It will make essential services like lighting and transportation more accessible to billions of people on this planet. We could see breakthroughs in water purification, desalination, and the energy hungry AI systems like GPT-5 that will come in the future, GPT-6, and all of these things are powered by abundant energy.
Hopefully something amongst all these opportunities is inspiring to you. The implications of this energy transition, particularly the currently exponentially-growing solar revolution, are profound. Cheaper energy will boost productivity across all sectors. It will make essential services like lighting and transportation more accessible to billions of people on this planet. We could see breakthroughs in water purification, desalination, and the energy hungry AI systems like GPT-5 that will come in the future, GPT-6, and all of these things are powered by abundant energy.
(12:37):
So water limitation problems we have around the planet and AI number crunching limitations, those can be alleviated by abundant energy, so many things you imagined – schools that are better powered, hospitals, all around the world, powering agriculture, having more water for agriculture, there’s so many ways that abundant energy could make this world a better place in the coming decades. And, perhaps most exciting, are the innovations we can’t even imagine yet in an era of energy abundance — perhaps even your mind through listening to something like this episode has been unlocked now to dream about what new innovations you could dream up and deliver in this new era of energy abundance.
So water limitation problems we have around the planet and AI number crunching limitations, those can be alleviated by abundant energy, so many things you imagined – schools that are better powered, hospitals, all around the world, powering agriculture, having more water for agriculture, there’s so many ways that abundant energy could make this world a better place in the coming decades. And, perhaps most exciting, are the innovations we can’t even imagine yet in an era of energy abundance — perhaps even your mind through listening to something like this episode has been unlocked now to dream about what new innovations you could dream up and deliver in this new era of energy abundance.
(13:24):
All right, that’s it for today’s episode. If you enjoyed it or know someone who might, consider sharing this episode with them, leave a review of the show on your favorite podcasting platform, tag me in a LinkedIn or Twitter post with your thoughts, I’ll respond to those, and if you aren’t already, be sure to subscribe of course to the show. Most importantly, however, we hope you’ll just keep on listening. Until next time, keep on rockin’ it out there and I’m looking forward to enjoying another round of the Super Data Science podcast with you very soon.
All right, that’s it for today’s episode. If you enjoyed it or know someone who might, consider sharing this episode with them, leave a review of the show on your favorite podcasting platform, tag me in a LinkedIn or Twitter post with your thoughts, I’ll respond to those, and if you aren’t already, be sure to subscribe of course to the show. Most importantly, however, we hope you’ll just keep on listening. Until next time, keep on rockin’ it out there and I’m looking forward to enjoying another round of the Super Data Science podcast with you very soon.
