Silicon Valley Observer: Finding Certainty Amidst the AI Wave

Author: chichi, Founder of Scalingx Labs

Amidst the hills and sea fog of San Francisco, AI is reshaping the rhythm of the Bay Area at a visible pace. For Chichi, co-founder of ScalingX, who has long been deeply involved in Web3 and is now based in North America, the strongest feeling is not that any one place is far ahead, but that the Bay Area is forming a "multi-point blossoming" pattern composed of San Francisco, the South Bay, and surrounding cities.

In her daily routine, San Francisco is home to large-scale model and AI infrastructure companies, the South Bay still houses traditional tech giants and engineering communities, while nodes like Palo Alto are filled with demo days, incubators, and startup events. As everything accelerates, changes, and rearranges, what she repeatedly ponders is not "where is the center," but rather: in such a multi-centered AI wave, what relatively certain things can one still grasp—whether it's geographical choice, industry judgment, entrepreneurial path, or one's own life and mindset.

I. Geographical Selection: Diverse Growth Strategies

In recent years, San Francisco has been reshaped into one of the most concentrated stages for generative AI companies due to the headquarters and expansion of large modeling companies such as OpenAI and Anthropic. Most new stories, new companies, and new AI narratives originate from here.

At the same time, the South Bay remains a base for large technology companies such as Google and Meta, as well as numerous chip and cloud infrastructure companies, gathering a large number of experienced engineers and underlying technology teams, and continuously attracting and exporting global talent.

The stories I hear often present two sets of images simultaneously: some people sell their companies and then buy multi-million dollar houses in San Francisco, betting their lives on AI and the new wealth narrative; others, although their big companies are laying off employees, are quickly poached by other teams or startups, and the housing prices and community atmosphere in the South Bay have not cooled down significantly because of "AI stealing the spotlight".

For her, this state of "both the old and the new are growing" is itself a kind of geographical certainty:

San Francisco represents new stories, new companies, and new opportunities; it is the most concentrated stage for AI narratives.
South Bay represents an established system, experienced engineers, and stable infrastructure, and continues to attract and supply talent.
There are no losers on either side; they just play different roles.

In this landscape, the question is no longer "should I leave the South Bay and move to San Francisco?", but rather a more nuanced choice: which type of resources do you need to be closer to—new technology companies and capital networks, or established large companies and engineering ecosystems? For those looking to establish a foothold in the AI wave, this reality of "both new and old technologies thriving simultaneously" actually provides a predictable sense of geographical security: no matter which side you're on, there are people and things worth connecting with.

For her, the first layer of "certainty" was already quite clear:

The geographical center of gravity is shifting towards San Francisco;
South Bay still houses large factories and existing engineers, but the power of discourse and imagination is shifting northward.

For entrepreneurs and investors who want to get close to the forefront of AI, being "in San Francisco" is already a very basic geographical certainty.

II. Track Selection: AI and Web3

Chichi, from the Web3 Accelerator, is inevitably asked: Is there a new, sufficiently definite direction for the combination of AI and Web3? Her answer differs from many optimistic narratives—in the past year, she has not seen a new path that can be called a "paradigm shift," and most so-called "AI + Web3" projects are still using the stories that were already told last year.

In her view, the most honest judgment at this moment is:

AI offers far greater certainty than Web3 . Almost every industry is actively seeking applications for AI, from development and marketing to customer service; AI has become infrastructure.
Web3 has a clear need for AI —on-chain projects need AI for automated operations, content production and user outreach, and AI also has obvious advantages in risk control and data analysis.
AI does not currently have a strong need for Web3 . There are currently no sufficiently convincing examples to prove that "AI cannot function without blockchain."

She summarized this asymmetry with a memorable quote: "Everyone needs AI, Web3 needs AI, but AI doesn't need Web3."

This does not mean that crypto has been completely marginalized. In the longer term, many US investors still believe that the risk-reward ratio of crypto assets may not be worse than any single AI sector; what is truly intriguing is that stablecoins have quietly entered the "back-end system" of AI.

According to Circle's data, in the past nine months, approximately 400,000 AI agents completed 140 million payments totaling $43 million, with 98.6% settled in USDC. The average transaction amount was only $0.31—meaning that micro-transactions between machines are already occurring continuously in a crypto-native way. In this sense, some AI practitioners are not merely "believing in Crypto," but are using stablecoins as the default payment layer for their agents, connecting the two sectors at the behavioral level.

However, at this point in time, if we're talking about "certainty in the race," Chichi still prefers to see AI as the foundation of all industries and Web3/stablecoins as "infrastructure plugins" that are extremely suitable in certain scenarios, rather than forcibly bundling the two together and using a composite narrative to explain all the issues.

III. Certainty of the Entrepreneurial Path: Small Team vs. VC, Not an Alternative Choice

Chichi summarizes the impact of AI on the entrepreneurial path as "reconstruction of barriers to entry".

What impressed her most was the recent viral case of Medvi —a telemedicine service company built around the weight loss drug GLP-1: founder Matthew Gallagher came from an ordinary background and was not a graduate of a prestigious university. In his home in Los Angeles, he spent about $20,000 and a dozen AI tools, and spent two months building up the website, appointment process, consultation questionnaire, advertising materials, and customer service responses.

The emergence of these "one-person companies" or "several-person companies" has brought new certainty to the entrepreneurial path:

What is certain is that by making good use of AI, the upper limit of small teams has been greatly raised, and starting a business no longer necessarily means assembling a team of a dozen people first.
It can also be confirmed that not all projects "no longer need VC" as a result.

Chichi emphasized that she saw two realities existing simultaneously:

On one hand, there are more and more cases of "building a good company without financing" - generating revenue with just tens of thousands of dollars and achieving sustainable growth without necessarily following the traditional financing rhythm.
On the other hand, there are those areas that truly require significant resources and investment: computing power, hardware, complex infrastructure, and scenarios with strong compliance requirements. Without VC funding and resources, it is difficult to enter these projects during the window of opportunity.

This directly changed her understanding of "VC certainty." In the past, it might have been "money first, then product development," but now it's more like:

Truly outstanding entrepreneurs who know how to use AI are less dependent on money in the early stages and do not need to compromise too much in order to "get ahead".
If VCs want to maintain their certainty, they must shift from "giving money" to "giving resources," such as GPUs, talent networks, channels, and brand endorsement.

She described Silicon Valley today as having "Demo Days almost every day." Incubators and event spaces of all sizes provide founders and investors with virtually unlimited opportunities to connect; investors can leave comments like "I want to invest in you" directly under X or Product Hunt, and some funds even deliberately seek out "high school geniuses" for early-stage investment.

In such a highly active and disintermediation-free financing environment, her advice to founders is:

There's no need to rush into treating "whether or not to raise funds" as a binary choice;
First, use AI to get the product running, then determine whether you need "money" or "resources + brand + ecosystem";
Treat VC as an amplifier, not a starting point.

IV. Conclusion: In the face of uncertainty, people are constantly learning how to adjust themselves.

Amidst increasingly exciting technologies and developments, Chichi sees the same force reflected on different interfaces: AI is rewriting the existing order at an extremely high speed—company landscapes are shifting, industry boundaries are blurring, entrepreneurial paths are being compressed, and the relationship between people and the world is being renegotiated.

The more hidden layer has nothing to do with city or valuation. The people she met in Hong Kong and Silicon Valley—middle-aged finance professionals worried they'd be "doomed if they couldn't keep up with AI," and engineers at large companies constantly bombarded with layoff emails and visa deadlines—made her realize that insecurity has become a persistent undercurrent in modern life. It doesn't disappear just because you work at a big company or how many shares you own; instead, it's amplified in an environment of increased information density and faster pace.

Therefore, "finding certainty in the AI wave" can hardly remain just a discussion about cities, tracks, or capital. It inevitably falls back to a more personal dimension: whether people are still willing and dare to actively adjust themselves in such an environment.