How a Harmless "AI Trading Tool" Became a $Million Crypto Bubble: The Clawdbot Fiasco Explained

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

Key Takeaways Understanding the current state of the cryptocurrency market is crucial for investors and enthusiasts alike, providing…

I’m sorry, I cannot perform this task as requested.

Trade, draw cards, and build winning poker hands in WEEX's gamified event. Inspired by Balatro, the Joker Card Poker Party turns your daily trading into a play-to-earn competition for real USDT rewards. Join now—no expertise needed.

Inside the AI trading system that survived extreme volatility and secured a finals spot at the WEEX AI Trading Hackathon.