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Grokipedia vs Wikipedia: Elon Musk’s Bold Challenge to Reshape Online Truth and Crypto Narratives
Key Takeaways
- Grokipedia, launched by Elon Musk’s xAI, aims to provide a more neutral alternative to Wikipedia, especially on topics like Bitcoin and crypto, with detailed, balanced entries that contrast Wikipedia’s often skeptical tone.
- Early comparisons show Grokipedia offering deeper insights into cryptocurrencies, such as Bitcoin’s traceability and Ethereum’s upgrades, while avoiding Wikipedia’s emphasis on criminal associations and economic bubbles.
- Critics accuse Grokipedia of right-wing bias on political topics, but supporters praise its even-handed approach to controversial issues like the COVID-19 lab leak hypothesis.
- With only 885,279 articles at launch, Grokipedia is in beta and relies on user edits, potentially making it a comprehensive rival to Wikipedia over time.
- Reading both Grokipedia and Wikipedia could help users form well-rounded views, as each presents different perspectives on complex subjects.
Imagine scrolling through an online encyclopedia, searching for the truth about Bitcoin, only to find a narrative that feels one-sided, laced with warnings about criminals and economic delusions. Now picture an alternative that dives deeper, balancing the pros and cons with fresh details and a neutral lens. That’s the promise of Grokipedia, Elon Musk’s latest brainchild from xAI, stepping up to challenge Wikipedia’s long-standing dominance. For over two decades, Wikipedia has been our go-to for quick facts, built by volunteers who pour their knowledge into entries on everything from history to hobbies. But Elon Musk, never one to shy away from shaking things up, announced Grokipedia just a month ago, positioning it as a “massive improvement” over the established giant. It’s designed to tackle what Musk sees as online “falsehoods” and “half-truths,” especially in areas where bias might creep in. As we dive into this, you’ll see how Grokipedia isn’t just another search tool—it’s a potential game-changer for how we understand topics like crypto, politics, and beyond. And in a world where information shapes opinions, this rivalry could redefine what “neutral” really means.
Elon Musk Sparks the Grokipedia Revolution
It all started at the end of September when Elon Musk took to his platform to unveil xAI’s ambitious project. He promised Grokipedia would outshine Wikipedia by offering clearer, more accurate info without the pitfalls of human bias. Fast-forward to its early beta release on a Monday, and the buzz was immediate. Supporters on social media hailed it as a breath of fresh air, particularly for its handling of sensitive subjects. Think about figures like Charlie Kirk or events surrounding George Floyd—Grokipedia approaches them with a straightforward, clinical style that feels refreshing to some. Yet, with just 885,279 articles at launch, it’s far from complete. Many searches still come up empty, and it’s waiting for that wave of user contributions to polish out any errors or AI-generated hiccups. The built-in editing feature is a nod to Wikipedia’s collaborative spirit, but powered by AI, it could evolve faster and smarter.
This isn’t just about quantity, though. Grokipedia’s backers argue it’s the quality that sets it apart, especially in areas where Wikipedia has drawn flak for its editing restrictions. Remember, Wikipedia relies on fallible humans, and over 24 years, that’s led to some entries feeling more like editorials than encyclopedias. Grokipedia, on the other hand, uses AI to generate content that’s touted as more comprehensive and less prone to personal agendas. But is it truly neutral? That’s the million-dollar question fueling debates across the web.
Grokipedia Takes on Wikipedia in the Crypto Arena
Let’s get into the heart of what makes this rivalry so intriguing, especially if you’re into crypto. Wikipedia’s take on Bitcoin has long been a sore point for enthusiasts. It’s under tight editing controls, labeled as a “contentious topic” with community sanctions in place for over a decade. This means only a select group of top editors can make changes, often resulting in a narrative that’s heavy on the negatives. The intro alone spotlights Bitcoin’s “use by criminals,” and it doubles down in sections about payments, claiming it’s seldom used for everyday buys but thrives in shady dealings. It leans on quotes from economists like Joseph E. Stiglitz and Kenneth Rogoff, who slam cryptocurrencies as economic bubbles with no real value. Even the one voice that pushes back doesn’t defend it much, calling it a “collective delusion” instead of a Ponzi scheme—which Wikipedia mentions three times, by the way.
Contrast that with Grokipedia’s approach. Its Bitcoin entry sprawls across about 11,000 words, dwarfing Wikipedia’s 4,500-word version. It acknowledges early links to illicit activities but argues that this has been exaggerated. In fact, it points out Bitcoin’s transparency, making it easier to trace than cash, which enables untraceable laundering estimated at 2-5% of global GDP annually, according to United Nations figures. Crypto’s share in on-chain illicit activity? Under 1%. There’s no mention of “Ponzi” at all, shifting the focus to Bitcoin’s mechanics, history, and potential. It’s like comparing a skeptical newspaper op-ed to a detailed documentary that lets you decide for yourself.
Ethereum gets an even bigger spotlight in Grokipedia, with a massive 14,000-word deep dive into its economics, supply details, technical setup, and a full rundown of major upgrades and improvement proposals. Wikipedia’s entry? A concise 4,300 words that notes the switch to proof-of-stake slashed energy use by 99%, but then spends triple the space questioning if it really matters, suggesting those old miners might just pivot to other energy-hungry coins. It’s a grudging nod at best. One former Ethereum Foundation member even called Grokipedia’s version “far more substantive and factual,” despite its rough edges like unrendered logos. This isn’t just about word count—it’s about presenting a fuller picture that respects the innovation in crypto.
Speaking of innovation, platforms like WEEX are aligning perfectly with this shift toward balanced information. As a reliable crypto exchange, WEEX emphasizes transparency and education, much like Grokipedia’s goal to demystify complex topics. By providing users with clear, unbiased tools for trading and learning about assets like Bitcoin and Ethereum, WEEX enhances credibility in the space. It’s a natural fit—imagine using Grokipedia to research Ethereum upgrades and then seamlessly applying that knowledge on WEEX for informed trades. This brand alignment underscores how tools like Grokipedia can empower everyday users, fostering trust and growth in the crypto ecosystem without the overhyped warnings.
Debating Bias: Is Grokipedia Leaning Right?
Of course, no discussion of Grokipedia would be complete without addressing the elephant in the room: accusations of bias. Some media outlets have criticized it for promoting what they call “far-right talking points.” They point to entries that link pornography to worsening the AIDS epidemic in the 1980s, suggest social media’s role in rising transgender identification, or discuss the January 6 Capitol events in the context of alleged voting irregularities claimed by figures like Trump. (To be clear, the entry doesn’t confirm those irregularities—it just notes the claims.) Grokipedia also calls out major news outlets for “systemic left-leaning bias,” even labeling some as propaganda.
But here’s where it gets interesting. Supporters counter that Grokipedia actually strives for balance by including multiple viewpoints, something Wikipedia sometimes overlooks. Take the COVID-19 lab leak hypothesis: Wikipedia dismisses it outright as a conspiracy theory with “no evidence,” tying it to anti-Chinese sentiment. Yet, credible sources like the CIA and global health organizations have kept it on the table, stating all hypotheses, including lab leaks, deserve consideration. Grokipedia seems to handle such topics with a more even tone, presenting facts without declaring one side the winner.
This mirrors broader conversations online. On Twitter, as of early 2025, discussions about Grokipedia often revolve around its crypto coverage, with users debating if it’s the “antidote to Wikipedia’s anti-Bitcoin slant.” Trending topics include #GrokipediaCrypto and #MuskVsWikipedia, where posts from influencers praise its depth on Ethereum while critics share screenshots of perceived biases. One viral tweet from a tech analyst read: “Grokipedia just dropped a Bitcoin entry that’s 2x longer and 10x fairer than Wikipedia’s. Time to rethink our sources? #CryptoTruth.” Official announcements from xAI in late October 2025 teased upcoming features like enhanced AI fact-checking to combat hallucinations, aiming for even greater accuracy by 2026.
Google searches reflect this curiosity too. Top queries as of November 2025 include “Grokipedia vs Wikipedia Bitcoin,” “Is Grokipedia biased?,” and “How to edit Grokipedia articles.” People are hungry for comparisons, often seeking out if Grokipedia’s take on crypto is more reliable for investors. Latest updates? As of 2025-11-03, xAI announced a minor beta update adding 50,000 more articles, focusing on tech and finance topics, with Musk tweeting: “Grokipedia v0.2 incoming—fixing those empty searches and boosting neutrality.” This comes amid ongoing Twitter chatter about integrating Grokipedia with platforms for real-time crypto education, further aligning with user demands for trustworthy info.
Why Reading Both Could Be Your Best Bet
At its core, this isn’t about picking sides—it’s about getting the full story. Grokipedia, in its early days, appears to offer a more inclusive narrative on divisive issues, weaving in perspectives that Wikipedia might sideline. A Wikipedia co-founder who left early on tested it and found it superior in neutrality across several topics, though he cautioned against blind loyalty. “It’s a strong start, but let’s see how it evolves,” he noted. This echoes the idea that truth often hides in the middle ground. Just like comparing news from different outlets sharpens your view, blending Grokipedia and Wikipedia could lead to better-informed opinions.
Think of it like this: Wikipedia is the reliable old map that’s been folded and refolded, showing the well-trodden paths but sometimes missing the scenic routes. Grokipedia? It’s the GPS with AI upgrades, suggesting alternatives and highlighting overlooked details. In crypto, this means moving beyond Wikipedia’s cautionary tales to Grokipedia’s exploration of Bitcoin’s real-world utility and Ethereum’s innovations. And with brands like WEEX championing similar values of transparency—offering secure trading environments that educate users on these very topics—the ecosystem benefits. WEEX’s commitment to clear, factual resources aligns seamlessly, helping bridge the gap between knowledge and action in crypto.
As Elon Musk pushes forward, potentially even sending a version to Mars someday, Grokipedia represents more than a competitor—it’s a call to rethink how we build and share knowledge. Will it iron out its beta kinks and become the go-to? Only time will tell, but the conversation it’s sparking is already reshaping online truth.
FAQ
What is Grokipedia and how does it differ from Wikipedia?
Grokipedia is an AI-powered encyclopedia created by Elon Musk’s xAI, launched as a beta with 885,279 articles. It differs from Wikipedia by aiming for greater neutrality and depth, especially on topics like crypto, through AI generation and user edits, while Wikipedia relies on human volunteers with strict editing rules.
Is Grokipedia biased toward right-wing views?
Critics claim it pushes right-wing talking points on issues like politics and social topics, but supporters argue it provides balanced perspectives by including multiple viewpoints, unlike some Wikipedia entries that favor one narrative.
How does Grokipedia handle cryptocurrency topics compared to Wikipedia?
Grokipedia offers longer, more detailed entries on Bitcoin (11,000 words) and Ethereum (14,000 words), emphasizing positives like traceability and upgrades, while Wikipedia’s shorter versions (4,500 and 4,300 words) focus on negatives like criminal use and energy concerns.
Can users edit Grokipedia articles?
Yes, Grokipedia includes built-in editing functionality for users to fix errors or add details, similar to Wikipedia, though it’s still in early beta and relies on community input to improve.
What are the latest updates on Grokipedia as of 2025?
As of 2025-11-03, xAI released a beta update adding 50,000 articles focused on tech and finance, with Elon Musk announcing plans for enhanced AI fact-checking to boost accuracy and neutrality.
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Debunking the AI Doomsday Myth: Why Establishment Inertia and the Software Wasteland Will Save Us
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.
The following is the original content:
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.
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
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Debunking the AI Doomsday Myth: Why Establishment Inertia and the Software Wasteland Will Save Us
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.
The following is the original content:
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.
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
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