Alliance DAO Founder on Recent Trends: The Cycle Is Not Over — Now Is the Best Time to Go Long
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Alliance DAO Founder on Recent Trends: The Cycle Is Not Over — Now Is the Best Time to Go Long
The market only needs an excuse to adjust, and that excuse is tariff policy.
Navigating Web3 tides with focused insightsCompiled & Translated: TechFlow
Guests: Imran Khan, Founder of Alliance DAO; Qiao Wang, Founder of Alliance DAO
Podcast Source: Good Game Podcast
Original Title: How Long Will This Bear Market Last? | EP 72
Release Date: March 19, 2025
Key Takeaways
Imran and Qiao discuss how long this bear market will last, offering no-nonsense crypto insights for founders.
Highlights Summary
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This is a great time to go long.
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I don’t think this cycle is over. We’re actually still in the middle phase—possibly another six to eighteen months to go.
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The recent market downturn is actually beneficial—it provides more breathing room.
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The market just needed an excuse to correct, and tariffs provided that excuse.
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The worst part about tariffs isn’t the tariffs themselves—it’s the uncertainty they create.
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Many people on Twitter are influenced by others’ emotions. I believe most of them are wrong—every single one of the bears has been wrong.
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Institutional investors now prefer tokenized Treasuries over stablecoins.
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I’m very excited about the idea and products around tokenizing early-stage ventures—it opens up the entire domain of tokenized startups, equity, and even companies.
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Web2 giants like Robinhood and Kalshi could gradually squeeze out crypto startups. This is a concern worth watching and may significantly impact crypto founders.
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I believe the economy is actually doing quite well. Given that, and considering Bitcoin as a macro asset class, I expect Bitcoin to perform strongly over the next six to eighteen months.
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Typically, during the first 100 days of a presidential term, there’s significant economic adjustment or policy reorientation. After that, policies tend to stabilize.
Market Status?
Imran:
There’s someone named Ki Young Ju who recently tweeted that Bitcoin’s bull market cycle is over. He expects sideways or bearish price action over the next 6–12 months.
Qiao:
What’s his reasoning? What are their arguments?
Imran:
Let me read it. They say every key on-chain metric shows bearish signals. As new liquidity dries up, newly entering “whales” are selling Bitcoin at lower prices. CryptoQuant users subscribed to my alert service have received these signals.
This alert system uses Principal Component Analysis (PCA) to combine metrics like MVRV, SOPR, and NUPL, calculating a 365-day moving average to identify turning points in trend shifts.
Qiao:
I'm skeptical about how these data points hold up in this cycle because much of big data analysis now ties into ETFs.
Imran:
Others base their bearish outlook on macroeconomic factors—like tariff impacts. Inflation remains unresolved. He also mentioned broader AI-related macro shifts he believes could affect markets. These signs lead him to conclude we're already in a bear market.
Tariffs and the Economy
Imran: What factors support our bullish view? How does the overall economy look?
Qiao:
The economy is actually doing pretty well. Since the election—or over recent months—earnings forecasts for companies have been rising, not falling. That’s a positive signal for markets.
Imran: Are you focusing on macro-level indicators?
Qiao:
No, I'm mainly looking at domestic U.S. conditions. For example, high-yield bond spreads are near historical lows. Typically, high-yield spreads reflect credit risk in the economy. If a recession hits, credit risk rises as companies struggle to repay debt. While spreads have ticked up slightly recently, it's mostly due to tariff concerns. However, fears of inflation caused by tariffs have weakened. Inflation is actually declining, at least in the short term.
This contradicts expectations—people assumed tariffs would drive inflation, but instead, inflation is falling. Unemployment is also near historic lows. So, I see few cracks in the economy. Of course, risks are higher than three months ago—I’m not saying tariffs have zero impact. They do affect consumer and corporate spending. But uncertainty itself is the bigger risk—a major issue for businesses and individuals—yet we haven’t seen that materialize in data yet. Therefore, I believe the economy is actually doing quite well. Considering that, along with Bitcoin as a macro asset class, I expect Bitcoin to perform strongly over the next six to eighteen months.
Imran:
I’ve seen reports on the actual impact of tariffs on the U.S. For instance, automakers like BMW are temporarily absorbing some tariff costs to protect American consumers’ prices.
I’m unsure if this price protection will last long-term, but at least in the short-to-medium term, the auto industry may have some buffer. Other industries might follow suit. So even if tariffs fully take effect, consumers may not feel the impact immediately.
Qiao:
Yes, the longer this uncertainty persists, the greater the risk that inflation could gradually rise and become unmanageable. If Trump continues strong rhetoric on tariffs, the economy could crack. But right now, things aren’t that bad. Have you noticed Trump has been silent on tariffs lately? I think he might be approaching his personal “maximum pain point.”
Imran:
That’s typical of the first 100 days of a presidency. Presidents often make many economic adjustments or shift policy execution styles early on. After that, policy usually stabilizes. But we’ll need to watch. I heard he plans to announce new policies on April 2—I saw his tweet this morning suggesting that date might be a deadline for imposing universal tariffs on 20% of countries globally.
Qiao:
This brings me back to my earlier point: The worst thing about tariffs isn't the tariffs themselves—it’s the uncertainty they create. If Trump clearly states what the tariffs will be, that uncertainty disappears, and businesses can adjust accordingly. That would actually put the economy in a better position.
Imran:
Another goal of Trump is to bring manufacturing back to the U.S. He advocates prioritizing American-made goods over imports. But Anthony pointed out that the U.S. manufacturing base is very weak compared to Shenzhen or China—that’s a real problem, especially regarding labor costs. However, from a robotics perspective, the situation might differ.
Qiao:
But clearly, this is a long-term issue. Manufacturing revival will take years to show results—it’s not something resolvable within a short economic cycle.
Imran:
Because this process takes so long, consumers won’t feel the immediate effects of tariffs. So we need to observe how these tariffs eventually influence future product pricing.
Diverging Sentiments in Crypto Markets
Qiao:
I've also noticed a huge divergence between sentiment in the crypto space versus traditional finance. Honestly, since FTX collapsed, I've never seen crypto natives this pessimistic.
Yet institutionally, when I talk to traders, they're extremely optimistic. Banks are gradually relaxing rules, allowing clients more access to Bitcoin ETFs.
Imran:
I have data supporting your view. Since Trump’s election, the total market cap of stablecoins has increased by $50 billion. Meanwhile, tokenized Treasuries have grown from under $2 billion to $4.1 billion—an almost 50% increase. So anything tied to real-world assets is growing.
The growth in AUM is very evident. And we’re seeing that institutional investors increasingly prefer tokenized Treasuries over stablecoins. The reason? Stablecoins carry counterparty risk—Tether or Circle-related credit issues—while tokenized Treasuries offer a safer exit path, especially for traditional institutions.
You may also notice positive developments in real-world assets and trade finance. For example, Ethena and Securitize recently announced Converge, a Layer 1 blockchain. Ondo and others are showing bullish momentum around RWA applications in trade finance.
Qiao:
Yes, I think many on Twitter are emotionally swayed by others. Most of these people are wrong—every single one of the bears is wrong.
Imran:
Crypto Twitter has become an echo chamber—an echo loop where everyone gets sucked into a vortex of negativity. I think people should step away from Crypto Twitter and focus more on actual on-chain data and real growth dynamics across sectors. But today, very few do that.
"Where Are We Now? Where Are We Going?"
Imran:
What’s your take on crypto’s future? Where are we in the cycle? What’s ahead?
Qiao:
We did warn about this—maybe two months ago, or even earlier in January.
Imran:
Yes, psychologically, people are exhausted. Those who wanted to sell probably already have.
Qiao:
We did tell people to sell. But I was wrong—I thought we were nearing the peak. In hindsight, I don’t think the cycle is over. I believe we’re still in the middle phase—likely another six to eighteen months to go.
Imran:
So this market dip is actually good—it gives us more breathing room.
Qiao:
The recent correction was mainly driven by sentiment around tariffs. Bitcoin closely follows U.S. equities, which hit record highs in early January. On P/E ratios, U.S. stocks are trading at levels comparable to mid-2021—just before Jerome Powell began hiking rates—even approaching pre-2008 financial crisis levels. So valuations were extremely stretched.
The market just needed an excuse to correct—and tariffs provided that. Now, the market has adjusted: U.S. stocks are down at least 10%, Nasdaq by 13%. I think this correction was necessary. Valuations are still somewhat elevated, but now more reasonable.
Indeed. So Bitcoin tracks U.S. equities. But economically, there aren’t obvious problems. If Trump stops talking about tariffs constantly, the economy might improve. But I sense he’s nearing his personal “limit”—his maximum stress point.
Imran:
They always claim they don’t care if the stock market enters a recession or the economy tanks—but I don’t believe that.
Qiao:
So I still have 80% confidence that we haven’t hit the cycle peak. At current price levels, I’m not sure if we’ve reached a mid-cycle bottom, but this is a great time to go long.
New narratives might emerge—some themes could gain traction. But right now, no clear narrative stands out. Future catalysts may define the next phase.
Imran:
After heavy losses in meme tokens, people are now shifting toward tokens that generate real revenue.
Qiao:
That’s what people talk about online, but the market doesn’t fully behave that way yet. Still, we’ll keep watching. Right now, outside Bitcoin, I don’t see anything particularly attractive. Ethereum is near historically oversold levels, but I can’t find a strong enough reason to hold it. It’s hard to buy, yet fundamentals are decent.
I still see Bitcoin as my top asset. Recently, I bought some stocks—Google, TSMC, Tesla, and PDD—all companies we’ve discussed over the past two years. Except Tesla, I’ve now finally added a position. In November last year, I tested Tesla’s FSD (Full Self-Driving), which is truly a game-changer. So I waited for a pullback. After Tesla dropped 50% from its peak, I finally bought in.
Ethena and Ondo Launching Their Own Layer 1
Imran:
What do you think about Ethena and Ondo launching their own Layer 1 blockchains? It’s fascinating—like having permissioned validators from traditional financial institutions. This somewhat undermines Ethereum’s DeFi moat, given Ethereum’s strength lies in its massive asset pool built over years.
Recently, Standard Chartered published a report stating Base has “drained” about $50 billion in value from Ethereum’s ecosystem, right? So clearly, value is flowing from Ethereum L1 and L2 to other chains.
Qiao:
What do you think? I don’t want to argue about Ethereum anymore—this is simply the current reality.
Imran:
Clearly, this isn’t good news for Ethereum. I expect this trend to continue. RWA projects may choose to build their own blockchains—“permissioned chains”—because such chains offer more functionality.
They can “rollback” transactions, giving them greater control.
Regulation and Counterparty Risk
Qiao: But I don’t understand why they’d do it in a permissioned way. What’s the point? What benefit do they gain?
Imran:
Greater control over the ledger. For example, if North Korea launches a hack. On a permissionless chain, there’s little you can do. But on a permissioned chain, you can coordinate validators to mitigate damage.
Qiao:
But who are they targeting with these permissioned chains? If you want to buy their product—say, a money market fund—you could just open a brokerage account.
The value of tokenized funds on Ethereum is that they allow people who can’t open brokerage accounts to access these products. So what problem are they solving by creating a new permissioned chain?
Imran: Possibly global accessibility. Knowing your counterparties makes regulation easier. When you know all participants in the value chain, you reduce counterparty risk, avoid dealing with unknown entities, and mitigate security threats like hacks. Could that be part of their thinking?
Qiao:
But will this bring them more business? Every time you launch a new chain, user acquisition becomes a massive hurdle. You have to figure out how to attract users.
Imran:
Traditional financial institutions already have distribution channels. Maybe these traditional funds have partnerships with these two L1 projects, but I’m not sure how deep those relationships are. It might just be co-marketing—we don’t know the extent. I’m just trying to see if there’s real value here, or if it’s just “smoke and mirrors”—complex-looking but meaningless.
If they have distribution and operate on a permissioned chain, they might solve counterparty risk and hacking issues while offering a global product. That’s a valid argument. But against that, Ethereum is permissionless—anyone can build apps, anyone can use it, accessing global markets with just an internet connection, no KYC required.
Qiao:
I was surprised by Standard Chartered mentioning Layer 2. They seem to suggest Layer 2 can retain significant sequencing fees instead of passing all of them to Layer 1.
Tokenizing Assets on Base
Imran:
Have you seen their announcement about tokenizing assets on Base?
Qiao:
Are they really going to do it?
Imran:
I think they’ve already started or are testing it.
Qiao:
I believe globally, Coinbase and Base are among the best-positioned companies to succeed in tokenized stocks.
They have distribution, deep experience in traditional finance—they’re custodians for many large asset managers. Plus, being a public company adds credibility. Located in the U.S. and heavily regulated—that’s a huge advantage.
Imran:
So they likely have a stronger edge in RWA than Ethena, Ondo, or any competitors.
Robinhood and Kalshi
Qiao: Another company potentially ideal for tokenized stocks is Robinhood.
Imran:
If you look at their recent announcements, they mentioned not only tokenized stocks but also real estate.
Imran:
In fact, founder Vlad said he wants to build a platform where users can buy and sell real estate with just one click.
Imran:
They recently partnered with Kalshi. I remember we talked about this. Kalshi has signed an agreement with the CFTC to legally offer prediction markets in the U.S., including specific event-based contracts. Kalshi is currently the only federally authorized company allowed to operate such services nationwide.
Robinhood just partnered with Kalshi to offer full prediction market functionality to all its users. This raises some concerns.
Web2 giants like Robinhood and Kalshi could gradually squeeze out crypto startups. This is a notable concern and could significantly impact crypto founders.
At least in the U.S. But globally, I think crypto still has vast potential. For example, Polymarket performs very well internationally. What do you think?
Qiao:
I think they’re not allies—they’re external threats.
Imran:
Indeed, many startups and traditional firms pose threats to founders in our ecosystem. Our only response is speed and execution excellence.
State of Artificial Intelligence
Manus AI
Imran:
Lately, half of my feed is about AI startups. We touched on this last episode—we have four AI-focused startups in our portfolio. A lot is happening in AI. Have you used Manus?
Qiao: I don’t have access.
Imran:
I used both Manus and Operator for simple tasks. Operator is a tool from ChatGPT/OpenAI. I gave both the same task: go to YC’s website, get info on the latest batch, and filter for launched crypto startups.
Manus took about 4 minutes. It returned full results—startup names, founders, and business descriptions—essentially scraping complete relevant data.
Operator got stuck. After two minutes, it reported: “I checked YC’s site, found only one company, Lero, focused on trainable deep agents and AI.” It added there were no crypto startups in this batch. Compared to that, Manus impressed me.
Qiao: Do these tools run on your computer or in the cloud?
Imran:
I think they run in the cloud, not locally. Operator shows a desktop interface, simulating mouse movements and clicks—searching YC, browsing, extracting content.
But this highlights a trend—many Chinese startups appear ahead in certain areas. At least based on Twitter discussions, outside observers widely believe Chinese startups have surpassed the U.S. in some fields.
Qiao:
Last month, I reached a similar conclusion. I’m not sure if China leads across the board, but at least they’re on par. DeepSeek is a standout example—performing nearly as well as top U.S. models despite being one or two orders of magnitude smaller. Meaning it can run locally, very efficiently.
China vs. U.S. AI Video Models
Qiao:
Another interesting observation: several video-based AI startups in our incubator use three to four video models—one from the U.S., three from China. Chinese models reportedly offer better performance, lower cost, and higher product quality.
Of course, AI applications are broad. These are just scattered data points. For example, Tesla’s autonomous driving is a form of “physical AI,” right?
Of course, Tesla’s FSD is world-leading. So determining which country leads in AI is hard. But I believe China is at least on equal footing with the U.S. now.
Imran:
I agree. Especially after Biden restricted exports of advanced video chips to China, China has clearly ramped up investment in domestic chip manufacturing, particularly with SMIC. Did you notice that?
Qiao:
But their chip tech still lags by a few generations.
Imran:
Even so, companies like DeepSeek are already using existing chips and software to compete with U.S. startups in media. Not quite equal yet, but the gap is closing. With one or two more iterations, Chinese products could match U.S. levels. So expect continued investment in SMIC.
Qiao:
In fact, chip export restrictions may force innovation in model efficiency in China.
Imran:
Long-term, this could become a Chinese advantage—making them more resilient and innovative in the U.S. competition.
Qiao:
And models like DeepSeek are open-source.
Imran:
That’s interesting—I’ve barely heard about Llama 3 lately.
Qiao:
There’s a notable trend: the tech race between these two superpowers has become an arms race, each rapidly catching up.
Local Large Language Models (LLMs) and Privacy
Qiao:
Recently, our team highlighted an exciting trend: local LLMs.
As tech advances, LLMs are shrinking in size without sacrificing performance. Soon, they’ll run directly on local devices—PCs, even phones—enabling new applications based on local inference.
Imran:
Huge win for privacy.
Qiao:
Beyond privacy, local LLMs dramatically improve response speed. No need to send requests to the cloud and wait. Even for non-privacy-conscious users, faster speed is a major advantage.
Apple Intelligence and Privacy Issues
Imran: Could this benefit Apple and Google? Android too?
Right now, Apple faces hurdles with Apple Intelligence. They planned to launch key features months ago with heavy promotion, but canceled. Reportedly, privacy bottlenecks were the main cause.
Apple can’t use other companies’ models, so they rely on their own framework—but in this field, Apple is behind.
Qiao:
Apple is indeed behind. What puzzles me is that despite this, Apple’s P/E ratio exceeds 30—the highest among the “Magnificent Seven,” second only to Tesla. Tesla is essentially a meme stock. By the way, I bought some Tesla shares the other day.
But Apple’s valuation baffles me. They haven’t introduced groundbreaking innovations in years. Steve Jobs gave Apple a decade of glory, but since then, that brilliance seems to fade.
Imran:
Hopefully Cook can turn things around. Though Warren Buffett sold Apple stock at the peak.
Qiao:
As these powerful, compact models spread—and most are open-source—new applications based on local inference become highly feasible. Great for privacy and performance optimization.
Imran:
I see massive potential. We’ll likely see fascinating new apps. Know any startups exploring this space?
Qiao:
Not really. The biggest current issue is that when users download an app with local inference, they must also download the model file, which could take minutes.
Using Local Large Language Models
Imran:
I believe tech improvements will solve this. It’s a space worth watching. I’ve already seen potential use cases, like health applications—analyzing user health data to give personalized advice.
These models can track long-term health trends and suggest improvements—all processed locally, never uploaded to cloud LLMs. That makes me more comfortable. Sometimes, I don’t want to share sensitive info, especially for privacy reasons.
Qiao:
If there were an encrypted, fully private system where data belongs solely to me, I’d trust it more.
Vibe Coding
Imran: Have you used Lovable or other vibe coding platforms? I’m still wrapping my head around vibe coding—it’s essentially AI-assisted coding, applicable across domains. I built my own site—these tools are truly magical.
Qiao:
These tools are highly competitive. Remember Wix and Squarespace? They helped people build sites fast, but were clunky to use.
Imran:
Yes, poor UX. Lovable’s key advantage is greater freedom and customization. You can easily simplify designs or remove images. Very intuitive. I think these tools are now highly commercializable.
I think they suit almost everyone, especially founders—they boost efficiency significantly.
Qiao:
A key use case for Lovable is helping founders quickly build product demos. For those wanting to deliver prototypes without heavy dev costs, it’s practical. Also great for personal websites—like your own homepage.
Imran:
It lowers startup barriers. You mentioned on Twitter that founder technical skill may be lower now—because “creators” can build apps without coding.
Right now, the priority is shipping MVPs fast and finding product-market fit.
Qiao:
Yes, but we still favor technically strong teams. Though maybe one day, non-engineer teams could achieve PMF. I don’t know if it’s end of this year, two years, or five—but it’s possible.
But we’re not there yet. Current trend: teams of two or three, one non-tech co-founder, one tech co-founder using Cursor or Windsor for frontend. But they still need a strong engineer for backend, handling complex logic.
Imran:
Products grow more complex over time. But for rapid MVP, tools like Lovable work fine.
Qiao:
Some recent critiques say vibe coding introduces more bugs. Indeed, some users complain about Cursor and Windsor. But for startups, bugs aren’t critical. The biggest early risk is building something nobody wants—not minor code issues. Once you have a product people love, you can fix bugs later.
Imran:
Getting the product out and into users’ hands is what matters most.
I think this helps founders in emerging nations immensely. For example, a kid from Congo, living in a village without electricity, built a windmill from scrap metal to power his village—learning everything online. His actions mirror real-life vibe coding—energy infrastructure. From this angle, emerging nations and local communities can use these tools to build solutions and improve lives.
Emerging nations are leveraging these tools—I believe their impact may be greater there than in developed countries.
Qiao:
Because access to funding is harder there, and their business models may not suit venture capital.
Imran:
It excites me. I think this creates a level playing field for more founders—meaning we’ll see more novel products we’ve never seen before. I’m not attached to the term “vibe coding,” but the possibilities it enables are thrilling.
Tokenizing Startups and Equity
Imran:
Next, let’s explore the link between vibe coding and crypto.
There’s a startup called Taro Base—you’ve heard of it? Taro Base offers bytecode tools helping crypto startups build apps, focused on crypto-native use cases. We believe opportunities here are wide open. Beyond app-building, people can tokenize startup ideas into circular financing tools—helping monetization and user growth. So “turn your life philosophy into reality and eventually IPO” might become a new trend. Taro Base is heading there. We also have another startup exploring this model via gaming. We’re already seeing compelling real-world use cases—especially combining startup ideas with tokenizing those ideas.
Qiao:
Yes, I’m very excited about tokenizing early-stage ideas and products—it fundamentally opens up the entire field of tokenized startups, equity, and even companies, right? But broadly speaking, there are two main approaches so far: one is directly tokenizing early concepts; the other is tokenizing shares of existing companies—like putting Tesla or SpaceX stock on-chain. I think it’s feasible, but faces big hurdles. First, you need the shares. Where do they come from? You’d have to buy them as a company or acquire them from employees. Then, build a compliant legal framework to put them on-chain. Complex and friction-heavy.
Besides, demand seems low—users can already get these stocks through brokerage accounts. So a more promising path isn’t moving secondary market stocks on-chain, but bringing primary market stocks on-chain from day one. Meaning companies launch fully crypto-native—tokenizing equity and listing on-chain even before incorporation. That’s the future direction merging vibe coding and tokenization platforms.
Imran:
Essentially, bringing small and medium-sized startups directly onto blockchains.
Qiao:
Exactly—and creating a whole new market for them. I think that’s crucial.
BYD’s New 1,000 kW EV Charging Technology
Imran:
This could be a massive opportunity. Did you hear about BYD’s recent announcement? BYD is China’s largest EV maker, holding about 11% global market share—Tesla has 19%. Recently, BYD unveiled an advanced technology enabling EVs to charge fully in just one minute.
Qiao:
How is that possible?
Imran:
Their tech can fully charge a car in just 5 minutes, adding 250–300 miles of range. Specifically, they use a 1,000 kW ultra-fast charging system.
The core innovation combines a flash-charging battery platform with blade battery design. This accelerates ion transfer in electrolytes and reduces separator resistance, drastically cutting charge time. I don’t grasp all technical details, but this innovation enables 5-minute charging.
Qiao:
Is this tech in production or still in R&D?
Imran:
Expected to go live next month. I love this kind of innovation—short concept-to-reality timelines. I’m less interested in ideas taking 18–24 months to deploy. This tech comes from China. Another trend: more such breakthrough innovations are emerging from China, not the U.S.
Qiao:
I saw data showing Chinese car brands are rapidly gaining market share in Europe—very noticeable over the past three years. Though I’m not sure how accurate the data is.
Imran:
I recently watched a comparison video of BYD and Tesla interiors—BYD’s design was far more refined.
Imran:
I think EV competition isn’t just about charger availability—it’s more about ultra-fast charging capability.
Robotics
Imran: I’ve been researching robotics lately. It’s a fiercely competitive field. Robotics holds massive potential across industries. Humanoid robots for general-purpose tasks, factory robots boosting automation efficiency.
Qiao:
Isn’t that Amazon’s strength? Many see Tesla as leading in robotics, but I think Amazon is. Amazon has developed various robots, especially in industrial robotics, with decades of deep expertise—likely far ahead.
Imran:
I agree. You’ve probably seen Andrew Kim’s post—he compares robotics’ potential to early crypto opportunities. He cites Figure AI as an example of the field’s massive promise. I fully agree. It’s a huge opportunity. Interestingly, Elon Musk recently said future commodity costs could drop dramatically.
Yes, he even predicted commodity costs will approach zero, causing money itself to lose value. His logic: when everything is hyper-commoditized, money becomes unnecessary. Sure, it sounds exaggerated—more of a vision. But many believe it deeply.
Qiao:
I recall Elon once estimated Tesla Optimus’ market size using simple math: minimum wage × hours spent daily on chores like dishwashing and laundry × 365 days × global population. By that calculation, the humanoid robot market could reach $10 trillion annually—an astonishing number.
Qiao:
Of course, Elon loves exaggeration. But even achieving 10% of that would make Tesla worth $10 trillion—the most valuable company ever.
Imran:
And Tesla could dominate general-purpose humanoid robots. Based on what I’ve read and seen, progress here is truly exciting.
From what I’ve observed, robotics won’t just deeply impact crypto—it will transform the entire labor market. Whether optimizing factory workflows or widespread home applications. I’ve also noticed surgical units starting to use robotics. Some companies have raised funds and successfully built such devices.
Crypto and Robotics
Imran:
Recently, I’ve been exploring crypto-robotics integrations, but haven’t found many real examples.
Payments are a possible use case, but beyond that, I don’t see clear crypto-robotics synergies. Any thoughts?
Qiao:
If anything, maybe data applications? Using crypto incentives to collect and guide data for robot training.
Imran:
But do we really need that?
Qiao:
Hard to say. Tesla’s approach with EV data: release vehicles, collect massive data, use it to improve hardware and software. A virtuous cycle—hardware captures data, data improves hardware.
Imran:
Tesla might use a similar method for humanoid robots. My guess: offer early versions to willing adopters—robots that handle 10–20% of tasks, humans assist the rest. Gradually, robots adapt to broader scenarios.
I think this evolves into reinforcement learning over time—not reliant on crypto incentives. But who knows?
AI and Robotics in the Labor Market
Qiao:
I recently wondered—will contractor and freelance platforms like Fiverr and Amazon Mechanical Turk be replaced by AI? Smart agents can now do many of these tasks, right? We’ve seen this internally. Previously, we hired freelancers for simple jobs—finding great founders online. Now, AI agents do it faster and cheaper.
Imran:
Indeed. Fiverr feels like a poor experience—most providers are low-cost labor from emerging nations. Language barriers and miscommunication often lead to unsatisfactory results. You spend extra time explaining and coordinating. Now, AI completes these instantly. That’s amazing.
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