Episode #70: Three Breakthroughs: More on AlphaFold

Frequently Asked Questions

FAQ

• Where is the transcript for this webinar?

  Sam Herrick:
  Welcome to The Tech Optimist. This is a podcast brought to you by Alumni Ventures. This is a show where we sit down and chat about the people, companies, and innovators cultivating tomorrow.

  Mike Collins:
  If 2024 was the year of AI, I think 2025, 2026 could be the year of the robots.

  Sam Herrick:
  That is Mike Collins, co-founder and CEO of Alumni Ventures.

  Naren Ramaswamy:
  And suddenly, it’s doing that much better and much quicker in a matter of minutes than scientists would’ve taken months or years to figure out.

  Sam Herrick:
  And on the other side of the table, Mike’s partner for the next few weeks is Naren Ramaswamy, senior principal at Alumni Ventures. And that’s me. My name is Sam Herrick, and I am the tech note writer and editor for the show. So hello everyone. Welcome to this really awesome Three Breakthroughs episode, and happy Halloween actually, because this episode is coming out on Halloween or a day or two after, around Halloween.

  We here at Alumni Ventures all hope you had a wonderful holiday and had a bunch of candy. So this episode is pretty sweet. Get it? Candy pun. Okay. Okay. Okay, I’m done. But this Three Breakthroughs episode—Naren and Mike really break down some really awesome stuff. And I know that I might be a little biased, but seriously, the stuff that they bring up today in their breakthroughs is… they’re cool breakthroughs. I don’t know how else to describe it.

  Again, I liked what we did last week where I didn’t tell you what the breakthroughs were because I think it’s fun to sort of get a snippet and be surprised about what the breakthroughs are. But I can give you some hints. So the first breakthrough has to do with the Nobel Prize. The second breakthrough has to do with Elon Musk. And the third breakthrough also has to do with Elon Musk. So I’m going to let the guys take it away. But seriously, please, please enjoy this episode.

  Before we hop into the interview, however, we’re going to take a few seconds for an ad and a disclaimer. So sit back, relax, enjoy the rest of your commute, or enjoy the rest of your workday or wherever you are listening to us, and we’ll be right back.

  Announcer:
  Do you have a venture capital portfolio of cutting-edge startups? Without one, you could be missing out on enormous value creation and a more diversified personal portfolio. Alumni Ventures, ranked a top-20 VC firm by CB Insights, is the leading VC firm for individual investors. Believe in investing in innovation? Visit av.vc/foundation to get started.

  Sam Herrick:
  As a reminder, The Tech Optimist podcast is for informational purposes only. It’s not personalized advice and it’s not an offer to buy or sell securities. For additional important details, please see the text description accompanying this episode.

  Mike Collins:
  Hello and welcome to Tech Optimist. This week’s Three Breakthroughs episode. Naren, how are you doing?

  Naren Ramaswamy:
  Doing great, Mike. How are you?

  Mike Collins:
  I’m excellent. So I think you’re up first this week again. Just every week is kind of shake-your-head cool new stuff taking place. A lot of exciting stuff to talk about.

  So we’re here, obviously, to talk about things that are at the intersection of technology, entrepreneurship, venture capital—just things that we’re excited about, we think are going to change the world. Obviously, we have a lens of investing as venture capitalists, but there’s a lot to be super excited about. How are you going to kick it off today, Naren?

  Naren Ramaswamy:
  Well, I’ll start off by saying that it was really hard to pick just three breakthroughs this week. It seems like there’s a lot happening across robotics, biology, AI, semiconductors, etc. And so we have to punt a couple topics to next week, but I think I’ll start with just the field of biology, AlphaFold, and the recent Nobel Prizes that AI has seemed to pervade.

  And just to give the audience a little bit of background on AlphaFold: this is an AI algorithm developed by Google DeepMind, the research division of Google. And their work recently contributed to research that led to a Nobel Prize for understanding protein folding structures.

  What’s really cool about this is that AlphaFold, the algorithm, can accurately predict protein folding, which is a major breakthrough because scientists had to laboriously do trial and error to find the way proteins actually fold and what their structure is.

  And just for a little bit of context, proteins are produced by a series of molecular beads from amino acids—we might’ve learned this in high school biology—and these proteins fold into a mechanical shape, which is crucial for how they function.

  Sam Herrick:
  So let’s dive into AlphaFold a little bit because they have had a very interesting history. As we know, as Naren told us, this is a research division out of Google, and recently AlphaFold 3 has come out with a crazy breakthrough with AI and pattern matching.

  First off, a bit about AlphaFold’s history: it’s an AI system developed by Google DeepMind to predict the three-dimensional structure of proteins. Naren gave a really awesome little conclusion and summary earlier about what it does, but I think providing some history can really put some emphasis on the breakthrough that they’ve made recently.

  So, in AlphaFold 1 from 2018 to 2020, AlphaFold proved that neural networks can model the protein folding mechanism. They achieved this through a significant leap in protein structure prediction accuracy and won the CASP13 competition in 2018, outperforming all of the other methods that were there.

  And then let’s jump to AlphaFold 2, from 2020 to 2022. They made a fundamental breakthrough in protein structure prediction. They achieved near experimental accuracy for many proteins. They released predictions for nearly all cataloged proteins known to science, and it has been used by millions of researchers globally for discoveries in areas like malaria vaccines, cancer treatments, enzyme design, and it just goes on and on. The list goes on and on.

  So now what we’ve all been waiting for: AlphaFold 3 of this year, 2024. AlphaFold expanded beyond proteins to predict structures and interactions of various biomolecules, including DNA, RNA, and small molecules, achieving at least a 50% improvement in predicting protein interactions with other molecule types compared to existing methods. It doubled prediction accuracy for some important categories of interactions, and it surpassed physics-based tools for biomolecular structure prediction without needing structural input information.

  Now, I’m not really a scientist and I don’t know all of what that means, but I know that it’s all good, right? Many green flags for AlphaFold 3 here.

  So, some key capabilities of AlphaFold 3—let’s just scratch the surface on this. It can predict the structure and interactions of all life’s molecules with unprecedented accuracy. I’m going to say that again: It can predict the structure and interactions of all life’s molecules with unprecedented accuracy. It models large biomolecules—proteins, DNA, and RNA—as well as small molecules, which are called ligands. It can model chemical modifications to molecules that can control cellular functions, and it achieves higher accuracy in predicting drug-like interactions, including protein-ligand and antibody-target protein binding.

  This is insane. These breakthroughs have significant implications for various fields, including drug discovery, understanding disease mechanisms, developing biorenewable materials, creating more resilient crops, and accelerating genomics research.

  AlphaFold has been recognized with numerous awards, including the Breakthrough Prize in Life Sciences, and has been cited over 20,000 times in scientific literature. Do you know how insane that is? 20,000 times in scientific literature. It’s impressive. My fiancé is in the biological field and he sort of broke it down for me a little bit. That number—20,000 times in scientific literature—is crazy. It’s a great achievement for scientists to even produce one piece of literature in the space that they’re working in. So the fact that there are 20,000 other scientists, biologists, innovators, engineers out there that have cited this work for Google and AlphaFold 3 is crazy. It’s just absolutely crazy.

  So, well done to Google. And I think we need to sort of deep dive into this a little bit more. We also found a YouTube channel called TheAIGRID, and they produced a video titled Google’s AlphaFold 3 Just Changed Everything: AlphaFold 3 Explained. If you want to see it, we’ll provide a link in the show notes. It’s about 13 minutes long. Obviously, we’re not going to play the whole thing, but here are some really exciting snippets from it.

  And yeah, just enjoy—let’s sort of get the whole accurate resemblance on this thing because it’s pretty crazy. You’re not going to want to miss this.

  Speaker 5:
  You have to think about it like this: In every cell of every living thing—plants, animals, even us—there are literally billions of these microscopic machines. And these machines are made up of proteins, DNA, and other funky molecules. And the thing is that none of these pieces work alone. They’re all kind of interacting, combining in millions of ways. And only by seeing that interaction can we actually understand how life works.

  And this is where AlphaFold 3 comes in. So how does this actually work? Given a list of input molecules, AlphaFold 3 generates their joint 3D structure, revealing how they all fit together. It models large biomolecules such as proteins, DNA, and RNA, as well as small molecules—also known as ligands—a category encompassing many different drugs. And it can model chemical modifications to these molecules, which control the healthy functioning of cells that, when disrupted, can lead to disease.

  We also see that AlphaFold 3’s capabilities come from its next-generation architecture and the training that now covers all of life’s molecules. At the core of the model is a module that learns the grammar of protein folding by studying evolutionary examples, and then uses that knowledge to predict the 3D structure of new amino acid sequences—much like how we can predict the meaning of a new sentence after learning the grammar of a language.

  And it’s a deep learning architecture that underpinned AlphaFold 2’s incredible performance. After processing the input, AlphaFold 3 assembles its predictions using a diffusion network akin to those found in AI image generators.

  Now here’s where we have one of the predictions. In this example, what we have here is the spike of a protein from a common cold virus. The spike protein is a part of the virus that helps it infect our cells. The AI model accurately predicted how this spike protein interacts with antibodies, which are the immune system’s defense proteins that attack the virus and neutralize it, as well as with simple sugars.

  In this prediction that you’re currently seeing on screen, the spike protein is in blue. The antibodies, which try to stop the virus, are shown in turquoise. The simple sugars are also shown in yellow. These predictions closely match what scientists have observed in real-life experiments, which are shown in gray.

  AlphaFold 3 saves so much time by providing accurate predictions that would otherwise require lengthy and expensive laboratory equipment. AlphaFold 3 can predict these structures in literally hours or days. Google actually launched this tool that helps scientists do this work for free—no fancy subscription needed. With just a few clicks, any biologist can use AlphaFold 3 to make models of proteins, DNA, RNA, and other important molecules. This is huge because the AlphaFold server lets scientists quickly come up with new ideas to test out in the lab, cutting out wasted time and guesswork.

  Naren Ramaswamy:
  So what this breakthrough has actually enabled is helping us understand how proteins function. It reveals the science of the human body—

  Mike Collins:
  —how we work as human beings, right, as machines or cells.

  Naren Ramaswamy:
  Exactly. It’s critical.

  Mike Collins:
  If you really shrink everything down, how do things work? This is at the core of it. And if you’re going to have a drug that solves a problem or explains how things go wrong, these are really the “tinker toys” that are involved.

  Naren Ramaswamy:
  Exactly.

  Mike Collins:
  So it’s keeping us alive, it’s making us sick, and there are drugs that make us healthy. This is a huge piece of the roadmap in understanding all of it. And again, it was awarded the Nobel Prize. People understand this is a huge deal in life sciences.

  Naren Ramaswamy:
  Yeah. We’re still in the very early innings of AI being applied to biology. What’s really inspiring about this work is that it proves that AI is so good at pattern matching. When you think about ChatGPT and text, you give ChatGPT a corpus of text data from the internet, and it can predict the next word really well.

  In this situation, scientists have been collecting characteristics and data about proteins for years in a database. AlphaFold came in and said, “All right, let’s ingest this data and try to predict how the next protein is going to fold.” And suddenly, it’s doing much better and much quicker—in a matter of minutes—what scientists would have taken months or years to figure out. It’s an order of magnitude faster and more efficient. And to your point, we can understand the human body in a way that just wasn’t possible before. That’s the first step to developing therapies.

  Mike Collins:
  And again, I think that underlying point, Naren, I just want to emphasize: the world is moving from deterministic plans and rules in software to one that’s much more about pattern recognition.

  The analogy I think is easiest for people to understand is self-driving cars. If you have to write software that says “If this, then that,” the complexity of that system to drive a car is really, really tough. If, on the other hand, you just upload tons of information into a neural network—video, how the steering wheel moved, all of that—you aren’t writing rules. You’re creating a garden that allows the AI to learn, observe, create patterns, and test things.

  This is a fundamental change in the way work is being done. Here’s another example: making understanding of the human body and biochemistry much more of an engineering problem. We can load all this data, we know enough to know what we’d like to happen, but now we’re creating a model of a human being. In the same way we’ve created a model of language that is so good it feels like a human talking back to you, we’re beginning to create models of the human brain and now proteins that will really strengthen our understanding of how we work.

  If we understand how things work, like we did with the genome project, solving problems starts with understanding them. These models are really profound. The applications—like AlphaFold itself—are huge. There’s a business unit at Google now focused on drug discovery (not talked about much), and there are startups from former AlphaFold researchers doing this.

  It’s a ripe area in health tech investing that’s really exciting—about making human disease, those “four horsemen,” something we can put a stake in the heart of.

  Naren Ramaswamy:
  Absolutely.

  Mike Collins:
  It’s what we all want in our lifetime.

  Naren Ramaswamy:
  And just the last thing to add: because this is so groundbreaking, we’re seeing a lot of activity in this space. We invested in a company called Q Bio—Quantified Biology—alongside Vinod Khosla at Khosla Ventures. This pioneering company’s problem statement is that healthcare has been reactive with regard to technology. How do we make it proactive?

  You’re able to take a full-body MRI scan, understand the parameters, and that can become the basis of a preventative healthcare system in the future. With algorithms like AlphaFold, you can have many new therapies that were previously undiscovered. Healthcare is definitely one area where we’re going to see massive improvements and optimizations.

  Mike Collins:
  Yeah, this is one of the three for today. But I was just reading a paper about a new application where little lesions in lungs can be identified at the precancerous stage—like we now do with colonoscopies. If you can get in early and snip these things out, these precancerous lesions can be treated right away.

  What I heard and read was that these systems were deployed in a medical center expecting to find about 500 lesions. Once AI was applied to all of it, they quadrupled the number of lesions identified. That creates more work and more things to remove, but these are things that at a very high rate become cancerous. So you’re saving lives by getting AI and data involved earlier.

  Again, this points to where we want to be: catching these things before they become cancerous. Nanobots and all of that are just around the corner. Very exciting.

  Sam Herrick:
  Okay, we’ll be right back to the Three Breakthroughs episode right after this. Don’t go anywhere.

  Announcer:
  Exceptional value creation comes from solving hard things. Alumni Ventures’ Deep Tech Fund is a portfolio of 20 to 30 ventures run by exceptional teams tackling huge opportunities in AI, space, energy, transportation, cybersecurity, and more. These game-changing ventures have strong lead venture investors and practical approaches to creating shareholder value. If you’re interested in investing in the future of deep tech, visit av.vc/deeptech to learn more.

  Mike Collins:
  So just flipping gears a little bit—what a week for Elon Musk, right? Love him or hate him, the guy is an N of one. I’m going to focus again on the continuing progress of robotics, specifically self-driving vehicles getting better and better.

  I think we’re looking at robots now that are serving drinks, having conversations, solving problems. If 2024 was the year of AI, I think 2025 and 2026 could be the year of the robots. We’re going to start seeing commercially viable robots coming into homes. Early adopters will face high prices, but these robots will have a huge wow factor. They’re going to solve real problems.

  I think we’ll see deployment in a lot of areas. Amazon just had a big release in the robotics space, more on the industrial side. But again, it was another great week for robotic innovation. The dexterity, the AI combination—we’ve talked a lot about how combining hardware and software is powerful. With AI now available on the edge, you can have visual recognition, voice interaction, and the dexterity to solve real day-to-day problems.

  This is a form of agency that’s gaining traction, and I think we are right on the cusp of seeing these deployed in the next 12 months.

  Sam Herrick:
  Okay, so Mike’s breakthrough focused on Elon Musk’s robot called Optimus. Recently, Optimus had its newest Gen 3 release. I have a 90-second video for you from Tesla, showing Optimus walking, working, putting itself to sleep, recharging, navigating an industrial complex, and interacting with people. Before we dive into the tech note, we’re going to play that video. Here’s the video titled Optimus Navigating Around Tesla by Tesla.

  Now, some more information on Optimus Gen 3: Elon Musk has confirmed that Gen 3 Optimus will be available by the end of 2024 or early 2025. It’s described as the final version, significantly different from Gen 2. The design is more sleek.

  If you want to see visuals, watch this on our LinkedIn and YouTube page, or Tesla’s YouTube channel.

  Gen 3 has improved mobility and speed compared to earlier versions. It has enhanced hand dexterity and manipulation skills, can perform a wider range of tasks—including household chores—and features improved AI and responsiveness. It can answer questions in real time, has better human interaction capabilities, autonomous navigation, obstacle avoidance, IoT integration, and supports remote software updates.

  Some possible applications: in homes, it could water plants, carry packages, and open car trunks. In factories, it can handle industrial work. In service industries, it can take on repetitive roles.

  Tesla and Elon Musk continue to innovate at an incredible pace. Pricing is expected around $20,000 per unit, with mass production planned for 2025. Leasing and rental options are also being considered.

  The robot uses technology from Tesla’s electric vehicles, including battery systems and motors, with AI at its core. It supports Wi-Fi 6 and 5G, has improved battery life for up to eight hours of continuous operation.

  Note: While these details are based on recent reports and Tesla statements, some may be speculative or subject to change since the product is still in development. But yes—crazy breakthrough number two. Let’s see what’s next.

  Naren Ramaswamy:
  There’s been some negative press about the humanoid robot Optimus, with claims it was remotely controlled from afar. But the reality is this is a prototype. It’s only a matter of months—or a couple of years—before we see these performing real tasks in industrial settings and households.

  Elon said during a presentation, “This robot can be your teacher, your kid’s babysitter. It can walk your dog, water your plants.”

  Mike Collins:
  Please—walk my dog, right?

  Naren Ramaswamy:
  Think about the applications that free up time for human connections by automating administrative tasks. Our lives will change once these robots become more ubiquitous. This is just the tip of the iceberg.

  Mike Collins:
  And I’ll point out: this is the journey of prototypes. The first Optimus version was literally a guy in a suit. The first rocket blew up on the launchpad. Entrepreneurs can overpromise—Elon especially with timing—but at the end of the day, he delivers.

  He’s introduced incredible innovation in space, robotics, and the EV market with Tesla. My 30 years of experience says: don’t bet against the one-in-a-billion people like Steve Jobs or Elon Musk. These rare individuals push humanity forward by being audacious, taking huge risks, and betting the farm.

  Even though not everything ships on time and not everything works immediately, it’s easy to criticize from the sidelines. But he’s in the arena, making things happen.

  Naren Ramaswamy:
  That’s a great segue to the third breakthrough you mentioned—Starship Flight 5 being caught by “chopsticks,” as folks are calling it.

  Mike Collins:
  Crazy.

  Naren Ramaswamy:
  This contraption caught a skyscraper-sized rocket after it launched into space. Over the last 10 years, Elon has been working to democratize space. A key part of that is reusing rocket components.

  Mike Collins:
  Yeah—because of cost.

  Naren Ramaswamy:
  Exactly. This demonstration proved they can successfully catch and reuse rockets. Starship is huge—it can carry massive payloads, enabling Mars and Moon missions.

  Beyond this engineering feat, think about the future applications. If SpaceX is the infrastructure layer for the space economy, what applications and payload technologies can be built on top of it?

  We’ve seen this in software: infrastructure platforms like OpenAI spawned countless application-level companies. This will be another platform. It’s an exciting time to be a venture investor in space tech.

  Sam Herrick:
  All right, I told you Elon Musk would come up twice. Starship Flight 5 being caught by chopsticks is just insane. My motto for this breakthrough is “say less, show more.”

  I found an awesome YouTube video by CSI Starbase titled Starship Flight 5 – Cinematic Experience from October 13th, 2024. It’s cinematic and gives you goosebumps watching Tesla engineers celebrate this massive achievement.

  The footage is amazing. We’ve cut it down here to highlight the most important parts. If you haven’t seen it yet, here’s Starship Flight 5.

  Speaker 6:
  The command was sent to the tower. We are go for catch.

  Mike Collins:
  I think it’s a big milestone. The barrier to space has been cost—cost per kilogram to get stuff up there. There are applications with communications that are fairly provable. There’s also a bunch of sensor applications for the planet, weather, obviously defense, a lot of potential for material science, mining, and natural resources.

  But the truth is, you’ve had to build a railroad every time you wanted to get up there. Imagine if you had to rebuild the railroad every time you wanted to run a train from St. Louis to Seattle.

  Naren Ramaswamy:
  Yeah, great analogy.

  Mike Collins:
  So we’re building the railroad, and we need to drive down the cost per kilogram to get stuff up there. One of the big ways to do that is reusability. The sheer scale of a massive rocket that was able to go up, come back down, and be caught—and we’ll include the video for anyone who hasn’t seen it—is mind-boggling.

  It’s an incredible feat of engineering. Not inventing new science or changing the rules of physics—just a really, really hard engineering problem that they cracked and executed on.

  The possibilities are enormous. Elon has already figured out how to get stuff into space at one-tenth the cost of others, and now he’s continuing to break through.

  I’ll also say this is unleashing the venture economy. There are other startups with different approaches that we’re going to see. We’ve got Blue Origin from Bezos, and there are other startups funded with different niches and strategies. We just need more—more ways to get into space, more reliably, and cheaper.

  The day is coming—we’re not there yet—when three young engineers in a garage can have a startup idea without needing $100 million. We’re not there yet; we’re still building the internet, still building the infrastructure.

  But we’re talking about a market with infinite upside, infinite potential, and infinite possibilities. Space is an area that, for people in their 20s and 30s, as investors, you really need to be watching and investing in because it’s still going to be around in 30 to 50 years.

  Naren Ramaswamy:
  Absolutely.

  Mike Collins:
  So you want to lean in.

  Naren Ramaswamy:
  Yeah. Today we talked about space, biotech, and robotics. Typically, venture investing over the last 20 years has been primarily around software, but here we have three breakthroughs that are really opening up new markets in venture—and yeah, it’s inspiring.

  Mike Collins:
  Yeah. And that’s just a week, right?

  Naren Ramaswamy:
  Yeah. That’s one week.

  Mike Collins:
  Yeah. Naren, we’ll do it again next week. We’ll see what crazy stuff will happen.

  Naren Ramaswamy:
  Exactly.

  Mike Collins:
  May you live in interesting times. Okay. Thank you, Naren. Talk again.

  Naren Ramaswamy:
  Thanks, Mike.

  Mike Collins:
  Yeah. Bye.

  Naren Ramaswamy:
  See you. Bye.

  Sam Herrick:
  Thanks again for tuning into the Tech Optimist. If you enjoyed this episode, we’d really appreciate it if you gave us a rating on whichever podcast app you’re using—and remember to subscribe to keep up with each episode.

  The Tech Optimist welcomes any questions, comments, or segment suggestions. Please email us at [email protected] with any of those, and be sure to visit our website at av.vc. As always, keep building.