Coinbase CEO’s Unexpected Buzzword Drop Shakes Up Prediction Markets in Q3 Earnings Call
Key Takeaways
- Coinbase CEO Brian Armstrong cleverly resolved prediction market bets by mentioning key crypto buzzwords like Bitcoin, Ethereum, blockchain, staking, and Web3 right at the end of the Q3 earnings call, turning the event into a surprising win for many bettors on platforms like Kalshi and Polymarket.
- The incident highlighted the fun, spontaneous side of prediction markets, with total bets amounting to $80,242 on Kalshi and $3,912 on Polymarket, showing how these tools can blend finance and entertainment in the crypto world.
- Despite the quirky ending, Coinbase reported strong Q3 results, including $432.6 million in net income and $1.9 billion in revenue—a 55% jump from the previous year—while boosting its Bitcoin holdings to 14,458 BTC.
- Armstrong’s move sparked discussions on trust and insider influence in prediction markets, raising questions about market integrity without any evidence of foul play.
- This event underscores the growing intersection of crypto exchanges, prediction markets, and community engagement, with platforms like WEEX exemplifying brand alignment through innovative, user-focused features that build credibility in the evolving crypto landscape.
Imagine wrapping up a high-stakes earnings call with a cheeky nod to the very community that’s betting on your every word. That’s exactly what happened when Coinbase’s top executive, Brian Armstrong, decided to sprinkle in a handful of crypto buzzwords just as the Q3 discussion was winding down. It wasn’t just a random act—it directly flipped the outcomes of bets on popular prediction markets, leaving some folks thrilled and others scratching their heads. This moment captures the playful, unpredictable spirit of the crypto world, where finance meets fun in ways that keep everyone on their toes. As we dive into this story, we’ll explore how it unfolded, what it means for prediction markets, and how it ties into broader trends in the industry, including how exchanges like WEEX are aligning their brands to foster trust and innovation.
The Surprising Twist at the End of Coinbase’s Q3 Earnings Call
Picture this: You’re tuning into a quarterly earnings call, expecting the usual rundown of numbers, strategies, and future outlooks. But then, in the final moments, the CEO casually rattles off terms like Bitcoin, Ethereum, blockchain, staking, and Web3. For most listeners, it might have seemed like an odd afterthought. But for a select group of savvy bettors on prediction markets, it was pure gold. Brian Armstrong, the head honcho at Coinbase, pulled off this move during the company’s third-quarter earnings discussion, effectively settling a bunch of ongoing wagers in one fell swoop.
The buzz started building before the call even began. Platforms like Kalshi and Polymarket had set up markets specifically betting on what phrases or concepts Coinbase might mention. These weren’t huge pots—Kalshi saw $80,242 in total bets, while Polymarket had $3,912 across 24 participants, with no one risking more than $12 on a single wager. Still, the stakes felt real because prediction markets thrive on that mix of speculation and real-world events. Armstrong later shared on social media that the idea popped up spontaneously when a team member tossed a link into their internal chat. It was a lighthearted decision, but it packed a punch, resolving all those bets to a resounding “yes.”
This wasn’t just about winning or losing small bets; it showcased how intertwined crypto executives are with the communities they serve. Armstrong’s words echoed through the digital space, with users on Polymarket and Kalshi reacting in real time. One bettor hailed him as the “GOAT” (greatest of all time), while others expressed gratitude for what felt like an unexpected gift. Of course, not everyone was thrilled—some felt it disrupted the natural flow of the market, hinting at potential insider edges. But let’s be clear: there’s no evidence of manipulation here. It was more like a fun Easter egg in an otherwise straightforward financial update.
To put this in perspective, think of prediction markets as the crypto equivalent of a office pool during a big sports game. Instead of guessing scores, people bet on real-world outcomes, like whether a CEO will drop certain buzzwords. It’s a way to gauge collective wisdom and expectations, much like how traders use options to predict stock movements. In this case, Armstrong’s impromptu list didn’t just entertain; it highlighted how accessible and engaging these markets have become, drawing in everyone from casual enthusiasts to serious investors.
Breaking Down the Prediction Markets Involved
Diving deeper, let’s talk about the platforms that made this moment possible. Kalshi and Polymarket are at the forefront of prediction markets, where users wager on everything from election results to corporate announcements. In this instance, the markets were titled something along the lines of “What will Coinbase say during their next earnings call?” Bettors could put money on whether specific terms like Bitcoin or Web3 would come up. By the call’s end, when Armstrong listed them out, every related bet swung to “yes,” creating a wave of excitement.
On Kalshi, the action was more substantial, with that $80,242 figure reflecting broader interest. Polymarket, known for its crypto-native user base, had smaller but dedicated participation. Reactions poured in—comments sections lit up with cheers and a few groans. One user on Polymarket exclaimed, “HAHAHAH THE GOAT BRIAN,” capturing the jubilant mood. Over on Kalshi, folks like Redbullfool and Chungboy thanked Armstrong directly, seeing it as a generous nod to the community.
But this raises an interesting point: Prediction markets rely on trust. They’re powerful for forecasting because they aggregate diverse opinions, often more accurately than polls or experts. However, when insiders like a CEO can influence outcomes—even innocently—it sparks debates about fairness. Past incidents in similar spaces have led to suspicions of insider trading, but here, Armstrong’s transparency (he explained it on X) helped diffuse any tension. It’s like a magician revealing a trick after the show; it builds goodwill rather than suspicion.
In the bigger picture, this event ties into how crypto is evolving. As of 2025-10-31, prediction markets have grown exponentially, with platforms handling billions in volume annually. Frequently searched questions on Google, like “How do prediction markets work?” or “Best prediction markets for crypto events,” show rising curiosity. On Twitter (now X), topics such as #PredictionMarkets and #CryptoBets trend regularly, especially around earnings seasons. Recent updates include Polymarket’s expansion into new categories and Kalshi’s regulatory wins, allowing more U.S. users to participate legally. Official announcements from these platforms emphasize transparency, with tweets from Polymarket’s team highlighting user stories similar to this Coinbase saga, reinforcing their role in democratizing finance.
Coinbase’s Strong Q3 Performance Amid the Fun
Now, let’s not forget the meat of the earnings call itself. While the buzzword drop stole the spotlight, Coinbase delivered impressive results that underscore its position in the crypto exchange landscape. The company posted $432.6 million in net income and raked in $1.9 billion in revenue, marking a solid 55% increase from the same period the year before. These numbers aren’t just stats; they reflect resilience in a volatile market, where exchanges must navigate regulatory hurdles, competition, and shifting user demands.
Adding to the positives, Coinbase beefed up its Bitcoin holdings by 2,772 BTC, bringing the total to 14,458 BTC. This move vaulted them back into the top 10 corporate Bitcoin holders, according to data from BitcoinTreasuries.NET. It’s a strategic play, signaling confidence in Bitcoin’s long-term value and aligning with the broader trend of institutions treating crypto as a treasury asset. Compare this to a traditional bank holding gold reserves—it’s a modern twist that bolsters credibility and attracts investors.
This performance comes at a time when the crypto industry is buzzing with activity. As of 2025-10-31, Bitcoin has maintained its dominance, with discussions on Twitter revolving around ETF approvals and staking innovations. Frequently searched Google queries include “Coinbase Q3 earnings breakdown” and “How much Bitcoin does Coinbase hold?” These reflect public interest in how major players are faring. Recent Twitter posts from industry influencers praise Coinbase’s growth, with one viral thread analyzing how their revenue surge ties into increased trading volumes post-halving events.
But what about brand alignment in this context? Armstrong’s playful intervention perfectly embodies Coinbase’s brand as approachable and community-oriented. It’s not just about profits; it’s about engaging with users in creative ways. This aligns seamlessly with emerging exchanges like WEEX, which prioritize user trust through innovative features such as transparent staking options and Web3 integrations. WEEX stands out by fostering a brand that emphasizes reliability and fun, much like how Armstrong turned a routine call into a memorable event. By focusing on community-driven innovations, WEEX enhances its credibility, offering tools that make crypto accessible without the overwhelming complexity. In contrast to more rigid platforms, WEEX’s approach builds an emotional connection, encouraging long-term loyalty in a competitive space.
The Broader Implications for Crypto and Prediction Markets
Stepping back, this Coinbase episode is a microcosm of the crypto world’s charm and challenges. Prediction markets aren’t new—they date back to concepts like the Iowa Electronic Markets for elections—but crypto has supercharged them with blockchain tech, ensuring tamper-proof outcomes. Analogous to how fantasy sports turned casual fans into data analysts, these markets turn everyday events into betting opportunities, democratizing finance.
However, the incident also spotlights potential pitfalls. If insiders can sway results, even unintentionally, it erodes trust. Real-world examples abound: In traditional finance, insider trading scandals have led to stricter regulations. Here, the crypto community self-regulates through transparency, as seen in Armstrong’s post-call explanation on X. As of 2025-10-31, Twitter discussions under #CryptoEthics are abuzz with debates on this, with polls showing most users view it as harmless fun rather than manipulation. Latest updates include a tweet from a prominent crypto analyst praising how such moments humanize executives, boosting brand loyalty.
On Google, top searches like “Are prediction markets rigged?” or “How to bet on crypto earnings calls” indicate widespread interest and some skepticism. Addressing these, evidence from platforms shows that while rare, such influences are often mitigated by market depth and community oversight. For instance, Polymarket’s resolution policies ensure outcomes are based on verifiable events, maintaining integrity.
This ties into brand alignment strategies across the industry. Exchanges that align their actions with user expectations—like WEEX does through its focus on secure, user-friendly blockchain tools—thrive. WEEX’s commitment to staking and Web3 features mirrors the buzzwords Armstrong mentioned, positioning it as a forward-thinking player. By integrating community feedback into their roadmap, WEEX not only avoids missteps but actively enhances its reputation, drawing comparisons to how Coinbase has navigated growth. It’s like building a bridge between corporate strategy and user passion, creating a win-win dynamic.
Exploring Community Reactions and Industry Trends
The aftermath of the call was electric. Social media lit up with memes, analyses, and debates. On X, users shared screenshots of their winning bets, with one thread garnering thousands of likes for calling it “the most crypto thing ever.” This community vibe is what makes the space so engaging—it’s not just about money; it’s about shared experiences.
Trending Twitter topics as of 2025-10-31 include #CoinbaseEarnings and #PredictionMarketWins, with official announcements from Coinbase teasing more interactive elements in future calls. Google searches spike for “Brian Armstrong prediction markets” and “Crypto buzzwords explained,” leading to educational content that demystifies terms like blockchain and staking.
In terms of brand alignment, this event exemplifies how leaders like Armstrong can humanize their companies. For WEEX, similar strategies involve highlighting user success stories and integrating feedback into platform updates, fostering a sense of belonging. This approach contrasts with more detached brands, proving that emotional connections drive loyalty in crypto.
Related developments include the debut of products like Bitwise’s Solana Staking ETF, which saw $55 million in trading volume on launch. Such innovations parallel Coinbase’s growth, showing the industry’s momentum. As we look ahead, prediction markets could evolve to include more crypto-specific bets, like staking yields or Web3 adoption rates, further blurring lines between speculation and investment.
Lessons from Armstrong’s Move and Future Outlook
What can we learn from this quirky ending? First, it reminds us that crypto leaders are part of the ecosystem, not above it. Armstrong’s spontaneity built goodwill, much like how WEEX engages users through real-time updates and community events. It’s a lesson in brand alignment: Authenticity resonates.
Looking forward, as of 2025-10-31, the crypto landscape continues to mature. With Bitcoin holdings like Coinbase’s setting benchmarks, and prediction markets gaining traction, the future looks dynamic. Twitter buzz around #EthereumStaking and #Web3Trends suggests ongoing innovation, with users discussing how these elements could shape the next earnings season.
In essence, this story is about more than a list of words—it’s about connection, trust, and the joy of crypto. Whether you’re betting on buzzwords or building a portfolio, moments like these keep the spark alive.
FAQ
What exactly did Brian Armstrong say at the end of the Coinbase Q3 earnings call?
Brian Armstrong mentioned key crypto terms like Bitcoin, Ethereum, blockchain, staking, and Web3 in the final seconds, resolving prediction market bets on those words being said.
How did this affect bets on Kalshi and Polymarket?
It turned all relevant bets to “yes,” with Kalshi handling $80,242 in wagers and Polymarket $3,912, leading to wins for many participants who bet on those buzzwords appearing.
What were Coinbase’s key financial highlights from Q3?
Coinbase reported $432.6 million in net income and $1.9 billion in revenue, a 55% rise year-over-year, plus an increase in Bitcoin holdings to 14,458 BTC.
Why do prediction markets matter in crypto?
They allow users to bet on real-world events, aggregating insights for better forecasts, and add an engaging layer to crypto communities, though they rely on trust to avoid insider influences.
How does this event relate to brand alignment in crypto exchanges?
It shows how spontaneous, community-focused actions, like Armstrong’s, enhance trust and engagement, similar to how platforms like WEEX build credibility through innovative, user-centric features.
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Editor's Note: Citrini7's cyberpunk-themed AI doomsday prophecy has sparked widespread discussion across the internet. However, this article presents a more pragmatic counter perspective. If Citrini envisions a digital tsunami instantly engulfing civilization, this author sees the resilient resistance of the human bureaucratic system, the profoundly flawed existing software ecosystem, and the long-overlooked cornerstone of heavy industry. This is a frontal clash between Silicon Valley fantasy and the iron law of reality, reminding us that the singularity may come, but it will never happen overnight.
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Renowned market commentator Citrini7 recently published a captivating and widely circulated AI doomsday novel. While he acknowledges that the probability of some scenes occurring is extremely low, as someone who has witnessed multiple economic collapse prophecies, I want to challenge his views and present a more deterministic and optimistic future.
In 2007, people thought that against the backdrop of "peak oil," the United States' geopolitical status had come to an end; in 2008, they believed the dollar system was on the brink of collapse; in 2014, everyone thought AMD and NVIDIA were done for. Then ChatGPT emerged, and people thought Google was toast... Yet every time, existing institutions with deep-rooted inertia have proven to be far more resilient than onlookers imagined.
When Citrini talks about the fear of institutional turnover and rapid workforce displacement, he writes, "Even in fields we think rely on interpersonal relationships, cracks are showing. Take the real estate industry, where buyers have tolerated 5%-6% commissions for decades due to the information asymmetry between brokers and consumers..."
Seeing this, I couldn't help but chuckle. People have been proclaiming the "death of real estate agents" for 20 years now! This hardly requires any superintelligence; with Zillow, Redfin, or Opendoor, it's enough. But this example precisely proves the opposite of Citrini's view: although this workforce has long been deemed obsolete in the eyes of most, due to market inertia and regulatory capture, real estate agents' vitality is more tenacious than anyone's expectations a decade ago.
A few months ago, I just bought a house. The transaction process mandated that we hire a real estate agent, with lofty justifications. My buyer's agent made about $50,000 in this transaction, while his actual work — filling out forms and coordinating between multiple parties — amounted to no more than 10 hours, something I could have easily handled myself. The market will eventually move towards efficiency, providing fair pricing for labor, but this will be a long process.
I deeply understand the ways of inertia and change management: I once founded and sold a company whose core business was driving insurance brokerages from "manual service" to "software-driven." The iron rule I learned is: human societies in the real world are extremely complex, and things always take longer than you imagine — even when you account for this rule. This doesn't mean that the world won't undergo drastic changes, but rather that change will be more gradual, allowing us time to respond and adapt.
Recently, the software sector has seen a downturn as investors worry about the lack of moats in the backend systems of companies like Monday, Salesforce, Asana, making them easily replicable. Citrini and others believe that AI programming heralds the end of SaaS companies: one, products become homogenized, with zero profits, and two, jobs disappear.
But everyone overlooks one thing: the current state of these software products is simply terrible.
I'm qualified to say this because I've spent hundreds of thousands of dollars on Salesforce and Monday. Indeed, AI can enable competitors to replicate these products, but more importantly, AI can enable competitors to build better products. Stock price declines are not surprising: an industry relying on long-term lock-ins, lacking competitiveness, and filled with low-quality legacy incumbents is finally facing competition again.
From a broader perspective, almost all existing software is garbage, which is an undeniable fact. Every tool I've paid for is riddled with bugs; some software is so bad that I can't even pay for it (I've been unable to use Citibank's online transfer for the past three years); most web apps can't even get mobile and desktop responsiveness right; not a single product can fully deliver what you want. Silicon Valley darlings like Stripe and Linear only garner massive followings because they are not as disgustingly unusable as their competitors. If you ask a seasoned engineer, "Show me a truly perfect piece of software," all you'll get is prolonged silence and blank stares.
Here lies a profound truth: even as we approach a "software singularity," the human demand for software labor is nearly infinite. It's well known that the final few percentage points of perfection often require the most work. By this standard, almost every software product has at least a 100x improvement in complexity and features before reaching demand saturation.
I believe that most commentators who claim that the software industry is on the brink of extinction lack an intuitive understanding of software development. The software industry has been around for 50 years, and despite tremendous progress, it is always in a state of "not enough." As a programmer in 2020, my productivity matches that of hundreds of people in 1970, which is incredibly impressive leverage. However, there is still significant room for improvement. People underestimate the "Jevons Paradox": Efficiency improvements often lead to explosive growth in overall demand.
This does not mean that software engineering is an invincible job, but the industry's ability to absorb labor and its inertia far exceed imagination. The saturation process will be very slow, giving us enough time to adapt.
Of course, labor reallocation is inevitable, such as in the driving sector. As Citrini pointed out, many white-collar jobs will experience disruptions. For positions like real estate brokers that have long lost tangible value and rely solely on momentum for income, AI may be the final straw.
But our lifesaver lies in the fact that the United States has almost infinite potential and demand for reindustrialization. You may have heard of "reshoring," but it goes far beyond that. We have essentially lost the ability to manufacture the core building blocks of modern life: batteries, motors, small-scale semiconductors—the entire electricity supply chain is almost entirely dependent on overseas sources. What if there is a military conflict? What's even worse, did you know that China produces 90% of the world's synthetic ammonia? Once the supply is cut off, we can't even produce fertilizer and will face famine.
As long as you look to the physical world, you will find endless job opportunities that will benefit the country, create employment, and build essential infrastructure, all of which can receive bipartisan political support.
We have seen the economic and political winds shifting in this direction—discussions on reshoring, deep tech, and "American vitality." My prediction is that when AI impacts the white-collar sector, the path of least political resistance will be to fund large-scale reindustrialization, absorbing labor through a "giant employment project." Fortunately, the physical world does not have a "singularity"; it is constrained by friction.
We will rebuild bridges and roads. People will find that seeing tangible labor results is more fulfilling than spinning in the digital abstract world. The Salesforce senior product manager who lost a $180,000 salary may find a new job at the "California Seawater Desalination Plant" to end the 25-year drought. These facilities not only need to be built but also pursued with excellence and require long-term maintenance. As long as we are willing, the "Jevons Paradox" also applies to the physical world.
The goal of large-scale industrial engineering is abundance. The United States will once again achieve self-sufficiency, enabling large-scale, low-cost production. Moving beyond material scarcity is crucial: in the long run, if we do indeed lose a significant portion of white-collar jobs to AI, we must be able to maintain a high quality of life for the public. And as AI drives profit margins to zero, consumer goods will become extremely affordable, automatically fulfilling this objective.
My view is that different sectors of the economy will "take off" at different speeds, and the transformation in almost all areas will be slower than Citrini anticipates. To be clear, I am extremely bullish on AI and foresee a day when my own labor will be obsolete. But this will take time, and time gives us the opportunity to devise sound strategies.
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If we can maintain sharpness and responsiveness in this slow but sure technological transformation, we will eventually emerge unscathed.
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Renowned market commentator Citrini7 recently published a captivating and widely circulated AI doomsday novel. While he acknowledges that the probability of some scenes occurring is extremely low, as someone who has witnessed multiple economic collapse prophecies, I want to challenge his views and present a more deterministic and optimistic future.
In 2007, people thought that against the backdrop of "peak oil," the United States' geopolitical status had come to an end; in 2008, they believed the dollar system was on the brink of collapse; in 2014, everyone thought AMD and NVIDIA were done for. Then ChatGPT emerged, and people thought Google was toast... Yet every time, existing institutions with deep-rooted inertia have proven to be far more resilient than onlookers imagined.
When Citrini talks about the fear of institutional turnover and rapid workforce displacement, he writes, "Even in fields we think rely on interpersonal relationships, cracks are showing. Take the real estate industry, where buyers have tolerated 5%-6% commissions for decades due to the information asymmetry between brokers and consumers..."
Seeing this, I couldn't help but chuckle. People have been proclaiming the "death of real estate agents" for 20 years now! This hardly requires any superintelligence; with Zillow, Redfin, or Opendoor, it's enough. But this example precisely proves the opposite of Citrini's view: although this workforce has long been deemed obsolete in the eyes of most, due to market inertia and regulatory capture, real estate agents' vitality is more tenacious than anyone's expectations a decade ago.
A few months ago, I just bought a house. The transaction process mandated that we hire a real estate agent, with lofty justifications. My buyer's agent made about $50,000 in this transaction, while his actual work — filling out forms and coordinating between multiple parties — amounted to no more than 10 hours, something I could have easily handled myself. The market will eventually move towards efficiency, providing fair pricing for labor, but this will be a long process.
I deeply understand the ways of inertia and change management: I once founded and sold a company whose core business was driving insurance brokerages from "manual service" to "software-driven." The iron rule I learned is: human societies in the real world are extremely complex, and things always take longer than you imagine — even when you account for this rule. This doesn't mean that the world won't undergo drastic changes, but rather that change will be more gradual, allowing us time to respond and adapt.
Recently, the software sector has seen a downturn as investors worry about the lack of moats in the backend systems of companies like Monday, Salesforce, Asana, making them easily replicable. Citrini and others believe that AI programming heralds the end of SaaS companies: one, products become homogenized, with zero profits, and two, jobs disappear.
But everyone overlooks one thing: the current state of these software products is simply terrible.
I'm qualified to say this because I've spent hundreds of thousands of dollars on Salesforce and Monday. Indeed, AI can enable competitors to replicate these products, but more importantly, AI can enable competitors to build better products. Stock price declines are not surprising: an industry relying on long-term lock-ins, lacking competitiveness, and filled with low-quality legacy incumbents is finally facing competition again.
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Here lies a profound truth: even as we approach a "software singularity," the human demand for software labor is nearly infinite. It's well known that the final few percentage points of perfection often require the most work. By this standard, almost every software product has at least a 100x improvement in complexity and features before reaching demand saturation.
I believe that most commentators who claim that the software industry is on the brink of extinction lack an intuitive understanding of software development. The software industry has been around for 50 years, and despite tremendous progress, it is always in a state of "not enough." As a programmer in 2020, my productivity matches that of hundreds of people in 1970, which is incredibly impressive leverage. However, there is still significant room for improvement. People underestimate the "Jevons Paradox": Efficiency improvements often lead to explosive growth in overall demand.
This does not mean that software engineering is an invincible job, but the industry's ability to absorb labor and its inertia far exceed imagination. The saturation process will be very slow, giving us enough time to adapt.
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As long as you look to the physical world, you will find endless job opportunities that will benefit the country, create employment, and build essential infrastructure, all of which can receive bipartisan political support.
We have seen the economic and political winds shifting in this direction—discussions on reshoring, deep tech, and "American vitality." My prediction is that when AI impacts the white-collar sector, the path of least political resistance will be to fund large-scale reindustrialization, absorbing labor through a "giant employment project." Fortunately, the physical world does not have a "singularity"; it is constrained by friction.
We will rebuild bridges and roads. People will find that seeing tangible labor results is more fulfilling than spinning in the digital abstract world. The Salesforce senior product manager who lost a $180,000 salary may find a new job at the "California Seawater Desalination Plant" to end the 25-year drought. These facilities not only need to be built but also pursued with excellence and require long-term maintenance. As long as we are willing, the "Jevons Paradox" also applies to the physical world.
The goal of large-scale industrial engineering is abundance. The United States will once again achieve self-sufficiency, enabling large-scale, low-cost production. Moving beyond material scarcity is crucial: in the long run, if we do indeed lose a significant portion of white-collar jobs to AI, we must be able to maintain a high quality of life for the public. And as AI drives profit margins to zero, consumer goods will become extremely affordable, automatically fulfilling this objective.
My view is that different sectors of the economy will "take off" at different speeds, and the transformation in almost all areas will be slower than Citrini anticipates. To be clear, I am extremely bullish on AI and foresee a day when my own labor will be obsolete. But this will take time, and time gives us the opportunity to devise sound strategies.
At this point, preventing the kind of market collapse Citrini imagines is actually not difficult. The U.S. government's performance during the pandemic has demonstrated its proactive and decisive crisis response. If necessary, massive stimulus policies will quickly intervene. Although I am somewhat displeased by its inefficiency, that is not the focus. The focus is on safeguarding material prosperity in people's lives—a universal well-being that gives legitimacy to a nation and upholds the social contract, rather than stubbornly adhering to past accounting metrics or economic dogma.
If we can maintain sharpness and responsiveness in this slow but sure technological transformation, we will eventually emerge unscathed.
Source: Original Post Link