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TL;DR — AI-powered platforms like Base 44 are generating multi-million-dollar valuations with tiny teams, signaling a major structural shift in the job market. While this hyper-productivity enriches founders and investors, it heavily reduces entry-level hiring and wealth distribution.
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Key Points
- 00:00 — Base 44, an AI-powered no-code platform, was acquired by Wix for $80 million after operating for just six months with only eight employees.
- 00:50 — Similar companies like Lovable generate massive annual recurring revenue (ARR) per employee—far exceeding traditional SaaS industry benchmarks.
- 02:00 — Base 44 founder Mayor Schlommo ran the company mostly by himself using AI as leverage for engineering, sales, and support, proving AI supercharges experienced individuals.
- 04:30 — Stanford studies show AI's impact on employment manifests not through massive layoffs, but through a severe lack of new hiring for junior roles, especially in AI-exposed fields.
- 06:00 — This creates a paradox for career pipelines: if companies only hire senior workers paired with AI, future senior workers miss out on gaining foundational junior-level experience.
- 08:45 — Financial gains are heavily concentrated; instead of payroll being distributed to hundreds of employees, equity and wealth concentrate among a tiny group of founders and investors.
- 11:00 — IMF research suggests AI worsens wealth inequality because asset owners capture savings from reduced labor costs, depriving workers of both salaries and equity.
- 13:00 — The ultimate irony is that AI models are trained on collective human knowledge, yet the economic rewards are concentrated into a very small group of hands.
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Takeaways
- Leverage AI for Maximum Output: Experienced professionals can use AI agents and no-code tools to execute multi-role tasks (engineering, sales, support) that previously required large teams.
- Anticipate the Junior Talent Bottleneck: Organizations must find new ways to train entry-level workers, as AI is currently wiping out the traditional bottom rungs of the career ladder.
- Reevaluate Wealth and Equity Distribution: Founders and leaders should consider alternative models for sharing company upside (such as broader equity pools) to offset the economic inequality driven by ultra-lean, AI-driven scaling.
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TL;DR — As traditional silicon scaling nears its physical and economic limits, researchers—particularly in China—are exploring alternative materials and paradigms like 2D transistors, wave-based computing, and single-electron memory. While these breakthroughs won't replace silicon overnight, they could eventually bypass the need for advanced EUV lithography and reshape global technological leadership.
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Key Points
- 01:33 — Silicon is hitting fundamental limits in physics (quantum tunneling, high heat, and leakage currents) and economics, where shrinking transistors requires exponentially more expensive lithography equipment.
- 02:34 — Gallium oxide emerges for power electronics (handling high power with low losses), though it is not suitable for high-performance CPUs due to poor heat dissipation.
- 04:36 — Two-dimensional materials (like molybdenum disulfide) enable atomically thin channels; Chinese researchers have successfully built sub-nanometer gate transistors and logic gates, though low manufacturing yields remain a hurdle.
- 06:40 — Wave-based computing explores using light, acoustic, or graphene-based electron liquid waves to perform complex mathematical operations like interference natively, boosting efficiency for AI.
- 09:23 — Single-electron memory devices operate at room temperature, dramatically reducing the energy consumed by constantly moving data between memory and processors.
- 11:00 — US and Western sanctions restrict China's access to ASML's EUV lithography, incentivizing China to leapfrog traditional silicon paths through novel architectures and materials.
- 13:09 — Future computing will not rely on a single universal material; instead, electronics will fragment into specialized components (silicon for base tasks, alternative materials for power, photonics for communication).
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Takeaways
- Embrace Specialization Over Miniaturization: The future of computing relies on diverse materials and specialized blocks (waves, photonics, 2D structures) rather than simply shrinking silicon nanometers.
- Watch Alternative Innovation Paths: Technological sanctions can backfire by forcing lagging nations to innovate completely new paradigms instead of playing catch-up on a monopolized track.
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2.44s
How does a company with only eight employees become worth $80 million in 6 months? That is exactly what happened with a company called B 44. Base 44 is an AI powered no code platform. In June 2025, Wix agreed to buy B 44 for around $80 million. But the main part here was that the company only had eight employees. 6 months, $80 million. The number that I find the most interesting is not $80 million. It's eight employees. Companies becoming worth millions isn't something that's unusual. Companies becoming worth millions with barely employing anyone. Now, that's a bit weird. Now, this isn't just a once- off. You can see it even more clearly with Lovable, another AI company that lets people build software by describing what they want. when it reached $und00 million in annual recurring revenue, it had about 45 full-time employees. Now, if you look at the average, according to SAS benchmark data, a company earning about $50 million in annual recurring revenue, known as ARR, it equates to roughly around $278,000 per employee. Lovable was doing around eight times that, $2.2 $2 million of annual recurring revenue per employee. So, this is where it stops becoming another boring story of an AI founder getting rich. Because if companies can suddenly create this much revenue without hiring anywhere near the number of people they used to need, what happens to all the jobs that growth would have normally created? And more importantly, who ends up capturing that money instead? So to understand how this is even possible, we need to go back to base 44 to the person who built almost all of it alone. Mayor Schlommo is the founder of base 44 and he wasn't new to technology when he started it. He had already spent years working in tech and had previously co-founded Explorium. This was a data and machine learning company. So when he started building base 44, he already understood the software, the products and what it takes to build a technology company. So what is interesting about most of base 44's 6-month existence is that Schlommo was basically running this all by himself. He actually said that his first employee joined about a month and a half before Wix acquired the company and then he explained how he managed to build so much with only a few people. He described himself as essentially managing teams of AIS writing code. At one point he said that he hadn't personally written a line of HTML or JavaScript in 3 months. And this starts to answer the question that I began with. How does a company with eight employees become worth $80 million in 6 months? For most of the time, it actually wasn't an eight person company. It was one experienced founder using AI to do far more than one person would normally be able to do. So while Schlommo was handling base 44 alone, he handled engineering, sales, and customer support himself, work that would normally be divided between many people. And before he hired his first employee, Base 44 had already received quite a high worth acquisition offer. So AI didn't give Schlommo the experience to build a successful company. He already had that and AI was just the leverage. So one experienced founder could suddenly produce software faster, automate work and operate across roles that would normally be spread across a much larger team as well. But this is where this stops being an impressive founder story. Because if one person can suddenly do the work that used to require several people, what happens to all the people a growing company would normally have hired. So let's talk about jobs. When we talk about AI taking jobs, I normally imagine layoffs. Like let's just say Microsoft has about 200,000 employees. They introduce AI and suddenly 4,800 people are laid off. They're gone. And then people complain. I create a video saying what a terrible company Microsoft is. Nobody likes them. The next month I renew my Microsoft 365 subscription. At least I'm honest about it. But B 44 shows us something that is a bit more harder to see. With Base 44, it didn't just grow to 500 employees and then automate 492 of them away. It's just that those jobs weren't created in the first place. And now we're starting to see evidence of exactly this kind of shift. Stanford researchers tracked millions of US workers and they found that employment among 22 to 25 year olds in very highly exposed AI occupations is now about 19% below where it would have been if those occupations kept with the usual pace of other occupations with little AI usage. And the really interesting part is how that gap appeared. It appeared through less hiring, not a sudden wave of people being fired. An even newer Stanford study looked across 41 countries using about 1.25 billion job postings and around 154 million employment records. And this research found a similar pattern across companies adopting generative AI. The share of junior workers declined relative to firms not using AI heavily, but senior employment actually grew, which makes sense when we think about what we just saw from base 44. AI doesn't necessarily remove the experienced person. It can make that person far more productive. The person may remove from the equation is a junior that they would have previously hired to help them. And programming gives us an awkward problem here. Junior developers become senior developers by actually being junior developers, fixing bugs, writing small pieces of code, getting things wrong, and having someone more experienced review their work. So, if companies increasingly keep the senior developer, give them AI, and hire fewer juniors, you eventually have to ask, where do the senior developers from the future come from? And AI doesn't need to replace every software engineer to fundamentally change the job market. It only needs to change how many new ones companies need to hire in the first place. And that's the problem. There's a reason the pressure may be showing up at the bottom of the career ladder first. I'm talking about the poor junior engineers. I mean, they've already survived the grumpy senior developers, infinite loops, variables called final final v2. It's rough out there. But the issue is that AI actually doesn't need to learn someone's entire job. It only needs to become good enough at most of the tasks within it. And for a junior engineer, those tasks can easily be replaced by AI. The IMF estimates about 40% of jobs globally are exposed to AI, rising to about 60% in more advanced economies. Now, exposed doesn't really mean replaced. We know in some jobs AI does complement the worker, but in others, it can definitely take over enough of the existing tasks that companies may need less labor. And a Stanford research again found an interesting pattern where this appears to be most vulnerable. So what Stanford found was that in jobs that heavily rely on what researchers called codified knowledge which is things that can be documented, taught and written down, the employment decline amongst younger workers in those kind of jobs were strong because experienced workers tend to have something AI has a much harder time to reproduce. That's known as tesset knowledge. That is the judgment you build after years of seeing things go wrong. knowing which solution looks correct but probably isn't. Understanding a client who can't properly explain what they actually need. So now the base 44 story makes a bit more sense. AI didn't replace a founder mayor Schlommo's experience. It just gave one experienced person the ability to absorb more of the work that might have previously been handed to several other people. But if this starts happening amongst thousands of companies, the company won't really lose its senior employees, it just needs fewer people on the way up. But now that brings us to the other side of the story because if companies can create more with fewer people and the value hasn't disappeared, where does it go? So in March 2025, Bloomberg identified about 29 founders whose AI related fortunes were collectively worth around $71 billion. Now across four major AI companies, 15 founders held stakes that were estimated to be around $ 38 billion. Just 15 founders. Most of that money isn't paid as salaries, it's ownership. Their companies become more valuable. So the shares that they own become more valuable with them. So now AI allows companies to grow exponentially without actually hiring a lot of people. If you think about a traditional company and how it grows from 10 employees to 100 to a thousand. As a company grows, the money it generates gets distributed through salaries to all of those additional employees. These employees then spend that money elsewhere in the economy. It feeds the economy. But what happens if a company can achieve that same level of growth without adding anywhere near as many people? Value hasn't disappeared. So the company can still make money. Its valuation can still increase. Its founders, its investors can still own really valuable shares. There's just simply fewer employees participating in that growth through normal wages. So what could that actually mean? Now there is economic research actually looking into this. In 2025, IMF researchers actually looked at how AI could effect inequality. They did find that AI could make wealth inequality worse because people who already own businesses, shares, and other assets are more likely and probably the only ones to benefit when AI makes those assets more valuable. And this effect becomes really strong when companies adopt AI because it allows them to save on that really expensive human labor. And that is just the crux of it. If a company can grow without hiring people at the same rate, fewer people may participate in that growth through employment while people who still own the company still participate in that increase of its value. Now for the founder, that's great. That's incredibly powerful. you own a large percentage of something that can suddenly grow incredibly quick without needing that enormous workforce that companies traditionally needed to support that growth. But if you think about the person who would have previously been employed as maybe employee number 56, if that position never exists, the person isn't just, you know, only missing a salary, they're also missing that potential experience that would have helped them move up into a more senior position later. And in a startup, they also may be missing something else, ownership. Because historically, if we look at Microsoft at the beginning when they were just a startup, the stock options turned so many employees into millionaires that the Microsoft millionaire became a phrase. It became a buzzword. Before its IPO, employees collectively held a significant equity in the company. So, if the next generation of companies need far fewer employees, then it isn't only salaries that fewer people get to participate in. It's also ownership. And look, this isn't personal. If I built an $80 million company with only eight employees, that is an achievement and that would be incredible. Who wouldn't feel that way? And at the level of one or two companies, there's nothing really wrong with this. The problem is when this becomes normal across thousands of companies, then we're not really talking about founders building leaner businesses, we're just talking about fewer entry jobs, maybe fewer people gaining experience, fewer people moving up that career ladder, and also far fewer people sharing in the wealth that those companies create. And this is really something that is not heavily talked about, but it is creating this new class of AI millionaires that really may affect industries going forward if this continues. And the final irony that I have to add is that the AI systems were built using enormous amounts of human knowledge, human writing, human code, human experience created by millions of people. But now the economic rewards from that same technology end up flowing to a much more smaller group of people. You know, we've taken knowledge created at an enormous scale and now we've turned it into wealth captured at a much smaller one. It's really unfair and it is a bit sad. Thanks so much for watching. If you like my content, please like, comment, and subscribe. I will see you on the next one. Bye.
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TL;DR — AI-powered platforms like Base 44 are generating multi-million-dollar valuations with tiny teams, signaling a major structural shift in the job market. While this hyper-productivity enriches founders and investors, it heavily reduces entry-level hiring and wealth distribution.
-
Key Points
- 00:00 — Base 44, an AI-powered no-code platform, was acquired by Wix for $80 million after operating for just six months with only eight employees.
- 00:50 — Similar companies like Lovable generate massive annual recurring revenue (ARR) per employee—far exceeding traditional SaaS industry benchmarks.
- 02:00 — Base 44 founder Mayor Schlommo ran the company mostly by himself using AI as leverage for engineering, sales, and support, proving AI supercharges experienced individuals.
- 04:30 — Stanford studies show AI's impact on employment manifests not through massive layoffs, but through a severe lack of new hiring for junior roles, especially in AI-ex
en
3.27s
Imagine a situation: in a few years, China releases a processor that can not be manufactured on modern ASML machines. Not because the Chinese somehow copied EUV lithography, but because they simply do n't need it anymore. Instead of silicon, there are two-dimensional materials only a few atoms thick; instead of conventional transistors, there are electron waves. And for storing information, instead of thousands or millions of electrons, literally just one is used. It sounds like science fiction, but research into each of these areas already exists today. And the most interesting part is that a huge portion of it is currently emerging specifically in China. Hello everyone! My name is Dmitry from InXnon Lab. Today, we 'll try to figure out if humanity is truly approaching the limit of silicon processors. What could replace them? Why is everyone suddenly talking about gallium oxide, graphene, two-dimensional transistors, and quantum memory? And the main question: could China not just catch up to the West in modern microelectronics, but simply leapfrog over existing technology? And what is actually wrong with silicon? Practically all modern electronics are built around one rather simple idea: if you need to increase performance, you shrink the transistors. You shrink the transistors, and more elements fit into the same area, resulting in a more powerful processor. And that is how the industry has lived for decades. But the problem is that today the dimensions of individual transistor elements are already measured in just a few nanometers, while a silicon atom is about two-tenths of a nanometer in size. In other words, we are gradually approaching a scale where what follows is no longer familiar engineering, but quantum physics. Electrons begin to tunnel through barriers that are too thin, leakage currents increase, heat dissipation becomes more difficult, and production becomes increasingly expensive. And the problem is n't just about physics. Each new generation of lithography requires incredibly complex equipment. A modern EUV machine consists of tens of thousands of components and costs hundreds of millions of dollars. And a factory for the most advanced chips now costs tens of billions. It turns out to be a paradox: the transistor is getting smaller, but the cost of the ability to make it smaller is growing almost exponentially. That is why the industry 's main question is gradually changing. Previously, they asked: how do we make a silicon transistor smaller? Now, they are asking more and more often : why do we even have to keep using silicon? And this is where the first candidate appears. Is gallium oxide the new silicon? If you follow tech news, you 've probably come across the name 'gallium oxide'. Sometimes it is already being called a material capable of replacing silicon. There is just one small problem here. That is not entirely true. Gallium oxide does indeed possess a very wide bandgap, and it is capable of withstanding significantly stronger electric fields than conventional silicon. This unique set of characteristics makes it extremely interesting for the field of power electronics. Electric vehicles, power supplies, industrial converters, energy, data centers — that is, areas where you need to manage huge amounts of power with minimal losses. And China is currently developing this direction very actively. In twenty twenty six, Chinese companies already reported the production of large six- and eight-inch gallium oxide wafers. And the transition to larger wafers is already one of the most important signs that the technology is trying to move from the laboratory to industry. But here an important point arises: your next Ryzen will most likely not be made of gallium oxide. For standard high-performance CPUs, this material has enough problems of its own. For example, it dissipates heat quite poorly. Therefore, it is more accurate to say this: gallium oxide will not replace silicon, but it is starting to take over specific tasks from silicon. And this is, perhaps, the main principle of the electronics of the future. There will not be one single material that replaces silicon. There will be many different materials, each for its own specific task. But if we talk directly about processor transistors, things get much more interesting here. A processor only a few atoms thick. Here is a standard silicon transistor. Now imagine that its active layer is literally only a few atoms thick. That is precisely why so much attention is currently being paid to so-called two-dimensional materials. One of the most well-known is molybdenum disulfide. Its layer can be practically atomically thin. And in theory, this is very convenient because such a thin channel is significantly easier to control with an electric field, which means that transistors can potentially continue to be shrunk even where silicon is already starting to experience serious problems. And here, China recently showed a very interesting result. Researchers from Tsinghua University have created a MOS transistor with a physical gate length of less than one nanometer. Moreover, it was n't limited to just one beautiful device under a microscope. The scientists demonstrated arrays of transistors and then assembled them into simple NAND and NOR logic gates. And these are literally the building blocks of any processor. At first glance, it seems: well, that 's it, here is the processor of the future. But there is one figure that is usually not put in the headline. For the smallest transistors, the yield of working devices is approximately fifteen percent. That is, conditionally, out of one hundred manufactured devices, fifteen work properly. But a modern processor needs billions, and it is desirable that almost all of them work. This is the huge gap between "scientists have created a transistor smaller than a nanometer" and TSMC producing a billion such transistors on every chip. A laboratory record is the beginning of the journey, not the end. But there is an even more radical option. What if, instead of shrinking transistors, we abandon the conventional method of computing altogether? And before we continue, just a few seconds. Xenon Lab. We are trying to create an independent scientific and technological project. If you really enjoy what we do, you can support us on Boosty or Patreon. You can subscribe to our channel, or simply leave a donation. This will help us make more episodes like this, and spend more time on research, graphics, filming, and editing. Thank you for your support. The link to Boosty and Patreon is in the channel header. Thanks to everyone who is already supporting the project. And we return to silicon. What if we computed not with electrons, but with waves? This is where real science fiction begins. A modern processor works something like this: there is a transistor, and it either lets current through or it does n't. We get a zero or a one. Billions of these tiny electronic switches add up to complex computations. But in nature, there is actually a huge number of other ways to transmit information. For example, consider waves. A wave has amplitude, frequency, and phase. When two waves meet, they can either amplify each other, creating a stronger signal, or they can completely cancel each other out. In other words, the fundamental physics of interference is already performing a complex mathematical operation, which is precisely why scientists are actively researching the potential of so-called wave-based computing. Moreover, these waves can be completely different in nature: they might be light, acoustic, spin, electromagnetic, or even collective electron waves. Back in 2026, a team of Chinese researchers demonstrated a truly unusual device based on graphene. Under certain specific conditions, the electrons and holes in graphene begin to behave collectively, acting almost like a liquid. Fascinatingly, special waves can actually propagate through such an electron liquid. The researchers managed to create a device that can control the propagation of such a wave, allowing it to pass through or reflecting it. In essence, this results in a kind of wave transistor. No, you ca n't run Windows on it yet, and it 's too early for a calculator, too. But the idea itself is what matters. We try to make computations faster by forcing billions of transistors to switch billions of times per second. And nature can sometimes perform a mathematical operation simply because two waves met each other. For some tasks, this could potentially be much more efficient, especially for artificial intelligence, signal processing, and matrix calculations. But the processor is only half the problem, because a modern computer spends a huge amount of energy not on calculations at all. One bit - one electron. The big problem with modern computing is called quite simply: data constantly needs to be moved somewhere. From memory to the processor, from the processor back, from HBM to GPU, from one accelerator to another, and this requires energy. Moreover, with the growth of artificial intelligence, the problem is becoming increasingly serious, and therefore new memory can change computers no less than a new processor. In the summer of 2026, researchers from Fudan University presented a groundbreaking experimental memory device in which literally just 1 single electron is enough to store 1 bit of information. For comparison, in traditional electronics, a huge number of electrons are used to reliably determine the state, whereas here, the state of a single electron is being changed instead. Moreover, the device operates at room temperature. And this is already extremely interesting, because if such technologies can ever be scaled, the power consumption of memory could potentially be reduced very significantly. But here, the marketing confusion begins again. Under headlines like "Quantum Memory," completely different technologies can be hidden. One type of electronic memory is one thing, but true memory for quantum computers is something else entirely. There, you need to store not an ordinary zero or one, but a quantum state, for example, the state of a photon or a qubit. And China is simultaneously working actively in this area as well. So, we are arriving at a rather unexpected picture. The computer of the future might not consist of just one type of electronics at all. The processor is one material, the memory is another, the power component is a third. Data transmission via light, specialized computing via waves, and right next to it, a separate quantum module. And now the most uncomfortable question arises: can China bypass the sanctions? Because this is exactly where this whole story becomes much more interesting than ordinary science. Today, the US and its allies are trying to restrict China 's access to the most advanced semiconductor manufacturing technologies. The most well-known example is EUV lithography. ASML remains the only company capable of mass-producing such systems, and China does not officially receive them. From this arises a very tempting idea: if China can not quickly catch up with the West in traditional silicon technologies, maybe it will simply create a technology that does n't need EUV at all? And theoretically, yes, it is possible. If a new architecture requires less aggressive lithography, if part of the computation is performed in memory, or if 3D structures, chiplets, photonics, new materials, or wave accelerators are used, then the heavy dependence on a specific process node can indeed decrease significantly. But there is one important point: new physics does not cancel out manufacturing. A two-dimensional transistor still needs to be manufactured, the material needs to be grown. It involves controlling microscopic defects, applying precise contacts, forming insulation, and creating conductors. Then, we are producing billions of elements, testing them, packaging them, and finally, designing the architecture and software for all of this. Therefore, an article in Nature does not replace a TSMC factory, and a new material does not eliminate technological sanctions overnight. But it can change the rules of the game, because a sanction is especially effective when there is one main technological path. For example, do you want the best processor? You need the best manufacturing process, you need EUV, you need specific equipment. But if there are ten paths, then controlling all the bottlenecks becomes much more difficult. What will actually come after silicon? And now let 's try to make a forecast. Trend number one. Silicon is not going anywhere. It is too cheap, too well-studied. A gigantic industry has been built around it. Therefore, you should n't expect that in five years all factories will throw away silicon and switch to graphene. Trend number two. Silicon will cease to be the universal material for everything. Power electronics will increasingly use silicon carbide, gallium nitride, and possibly gallium oxide. Two-dimensional materials may appear for individual transistors. Communication between chips will gradually shift to photonics. Trend number three. Computing is set to become increasingly specialized. Today, the CPU tries to be a universal processor, but AI has already shown just how effective specialized accelerators can be. In the near future, individual computing blocks may rely on completely different physical principles: some will use transistors, others will utilize light, others will leverage memory, and still others will harness waves. Trend number four. The main race will no longer be the race for nanometers. We are quite accustomed to comparing seven nanometers, five, or even two, but in a few generations, this figure may cease to be the primary indicator of technological leadership. That is because the much more important question will be: how many useful computations a system performs per watt of energy. And this is where countries that are currently lagging behind in classical lithography really have a chance to change the balance of power. Is the silicon era really coming to an end? Actually, both yes and no. Silicon will remain the foundation of electronics for a long time. It wo n't disappear tomorrow, it wo n't disappear in five years, and perhaps it wo n't disappear in twenty either. But the era when practically any performance problem could be solved by simply shrinking the silicon transistor is indeed coming to an end. And now, a much more interesting period is beginning. Instead of one technology, dozens are emerging. Gallium oxide, silicon carbide, two-dimensional materials, photonics, in-memory computing, electronic and spin waves, quantum devices. And that is precisely why I would keep a close eye on what is happening right now in Chinese laboratories. Not because China will release a processor tomorrow that will overtake Nvidia or AMD, but because a technological lag sometimes forces you to look for a path that others simply have n't taken yet. And here remains the main, critical question : what is truly more dangerous for a leader? Is it a competitor who is actively trying to catch up with you, or is it a competitor who has completely stopped running on your track altogether? And now, a question for you. What do you think will really be the next big stage after silicon? Are we looking at two-dimensional transistors, photonics, in-memory computing, or perhaps quantum technologies? Or, do you think silicon will continue to bury all of its would-be killers for another thirty years? Write in the comments. This was Dmitry from Xenon Lab. Subscribe, like, and hit the bell. See you in the next video.
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TL;DR — As traditional silicon scaling nears its physical and economic limits, researchers—particularly in China—are exploring alternative materials and paradigms like 2D transistors, wave-based computing, and single-electron memory. While these breakthroughs won't replace silicon overnight, they could eventually bypass the need for advanced EUV lithography and reshape global technological leadership.
-
Key Points
- 01:33 — Silicon is hitting fundamental limits in physics (quantum tunneling, high heat, and leakage currents) and economics, where shrinking transistors requires exponentially more expensive lithography equipment.
- 02:34 — Gallium oxide emerges for power electronics (handling high power with low losses), though it is not suitable for high-performance CPUs due to poor heat dissipation.
- 04:36 — Two-dimensional materials (like molybdenum disulfide) enable atomically thin channels; Chinese researchers have successfully built sub-nanometer ga
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What are the latest features and future plans?
We've launched the Chrome extension — Pro users can now use PeekaTube directly on YouTube pages! We've also added YouTube comment analysis, which fetches viewer comments to give you a fuller picture of the video beyond just the transcript. Coming next: support for videos without subtitles is actively being developed. Your support is the biggest motivation for making new features available sooner!
Have more questions?
We're happy to answer any questions you might have