In today's technological signals, there are several news items that hide the same larger theme: Behind technological progress, who is really taking risks and who is harvesting dividends? This issue is reflected in the AI wave, consumer security and capital games, and is worth digging deeply.
Let's first talk about the changes in the wind direction of AI office and programming. Big manufacturers suddenly flocked to the AI office track. It may seem lively, but in fact it reveals an embarrassing reality: although AI programming consumes a large part of token consumption, the commercialization prospects are unclear. Tencent WorkBuddy's increase in monthly visits to 20 million in half a year is behind the anxiety of major manufacturers in the knowledge worker market. The high consumption of programming scenarios means high costs, and although the office market has a large user base, the paying propensity and conversion rate are far less optimistic than expected. More importantly, what are the core competitiveness of AI office? Bean bags integrate the Flying Book ecosystem. Byte wants to win this battle by splitting products, but Ali and Tencent have already been deployed for a long time. The essence of this competition is not a technological breakthrough, but a competition for traffic and ecology. Who is the real payer of AI office? It may still be the companies and individuals who are forced to become involved in corruption, rather than the users who actually benefit from it. Historically, every iteration of office software has been accompanied by a similar bubble. From the early OA systems to later collaboration tools, what ultimately wins is often not the most advanced technology, but the ecosystem that best binds users. Will AI office repeat the same mistake?
Let's look at the black hole of consumer security. The dichlorvos incident in green tea restaurants is not an isolated case. It exposes the unspoken rules of the entire service outsourcing industry. The killing company used dichlorvos instead of regular drugs. The restaurant claimed that it did not know about it, and the regulatory authorities intervened afterwards, but who ultimately bore the risk? Consumers. This is the same as the hygiene problem of takeout a few years ago, except this time the harm is more direct. What's even more ironic is that this low-cost, high-risk operation may have long been an open secret in the industry. Employees themselves dare not eat restaurants that they kill, but they can still operate openly. Behind this is the ambiguity of responsibilities in the entire service chain: killing companies, restaurants, and regulatory authorities, each party is shirking responsibility, and the cost of consumer rights protection is extremely high. Similar incidents are common in fields such as food safety and the sharing economy. Every time, accountability is held after the fact and fines are settled, but there are few systematic solutions. Technological advances have led to more efficient services, but they have also hidden risks deeper. When major AI office manufacturers are competing for the market, who will ensure the most basic consumer security?
Finally, let's talk about the game of capital. Earlybird regretted missing Lovable, so he wrote judgment into the AI system and tried to use technology to solve human problems. Behind this is the general anxiety in the VC industry: How to avoid being eliminated in the bubble? AI systems can screen 95% of financing opportunities, but what really determines the success or failure of a project are often factors that cannot be quantified-the resilience of the founder, the contingency of the market, and even luck. Historically, VC has tried more than once to use data-driven investment. From early quantitative funds to later big data analysis, it has finally proved that investment is still an art, not a science. Earlybird's approach is more like a defense mechanism, trying to cover up people's uncertainty with systematic processes. But the real question is, when all VCs start using AI to screen projects, will the market fall into the homogenization trap? Will innovations that do not conform to the AI model be systematically ignored?
These events are connected together to reveal a common dilemma: while technological progress improves efficiency, it is also amplifying risks and uncertainties. The prosperity of AI offices may be just a carnival of capital, loopholes in consumer security expose regulatory lags, and the systematization of VC reflects fear of uncertainty. In this game, it is often the users and consumers at the lowest level who really pay the bill, and those seemingly glamorous technologies and business models are nothing more than old wine in new bottles. History is always strikingly similar, with every wave of technology accompanied by bubbles and disillusionment, and we always seem to be making the same mistakes.