Silicon Valley Observation: Seeking Certainty in the AI Wave

Author: ChichiHong, ScalingX Labs Co-Founder

Amid the hills and sea fog of San Francisco, AI is visibly rewriting the rhythm of the Bay Area. For Chichi, co-founder of ScalingX, who has long been deeply involved in Web3 and has now come to North America, the strongest impression is not that one place is racing ahead alone, but rather: the Bay Area is forming a "multi-point blooming" pattern composed of San Francisco, the South Bay, and surrounding cities.

In her daily routine, San Francisco gathers large model and AI infrastructure companies, the South Bay still hosts traditional tech giants and engineering communities, while nodes like Palo Alto are filled with various Demo Days, incubators, and startup events. As everything accelerates, iterates, 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 people still grasp—whether it's geographical choices, sector judgments, entrepreneurial paths, or one's own life and mindset.

I. Geographical Choice: Multi-Point Growth

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 major model companies like OpenAI and Anthropic. New stories, new companies, and new AI narratives mostly emerge from here.

At the same time, the South Bay remains the base for large tech companies like Google and Meta, as well as numerous chip and cloud infrastructure enterprises, gathering a vast number of mature engineers and underlying technical teams, continuously attracting and exporting global talent.

In the stories she hears, two sets of images often appear simultaneously: some sell their companies and buy multi-million-dollar homes in San Francisco, betting on AI and new wealth narratives; others, though affected by layoffs at their large companies, are quickly poached by other teams or new startups, and the housing prices and community atmosphere in the South Bay haven't noticeably cooled due to "AI stealing the show."

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

• San Francisco represents new stories, new companies, new opportunities, and is the most densely packed stage for AI narratives;
• The South Bay represents the old system, mature engineers, and stable infrastructure, still attracting and输送 talent;
• Neither side is losing; they just play different roles.

In such a landscape, the question is no longer "should I leave the South Bay and move to San Francisco," but a more nuanced choice: which type of resource do you need to be closer to—new tech companies and capital networks, or mature giants and engineering ecosystems. For those wanting to gain a foothold in the AI wave, this reality of "both old and new booming simultaneously"反而 provides a kind of predictable geographical security: no matter which side you're on, there are people and things worth connecting with.

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

• The geographical center of gravity is shifting towards San Francisco;
• The South Bay still carries the big companies and the存量工程师, but the话语权和想象力 are moving north.

For entrepreneurs and investors wanting to be close to the AI frontier, "being in San Francisco" itself is already the most basic geographical certainty choice.

II. Sector Choice: AI and Web3

Coming from a Web3 accelerator, Chichi is inevitably asked: is there really a new, sufficiently certain direction for the combination of AI and Web3. Her answer differs from many optimistic narratives—over the past year, she hasn't seen a new path that could be called a "paradigm shift"; most so-called "AI+Web3" projects still follow stories that were already told last year.

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

• The certainty of AI is much stronger than that of 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, user outreach, and even in risk control and data analysis, AI has obvious advantages;
• AI暂时 has no刚性 need for Web3. Proving that "AI can't run without blockchain" currently lacks sufficiently convincing cases.

She summarizes this asymmetric relationship with a memorable phrase: "Everyone needs AI, Web3 also needs AI, but AI doesn't need Web3."

This doesn't mean Crypto is completely marginalized. Over a longer cycle, many local US investors still believe the risk-reward ratio of crypto assets may not lose to any single AI track; what's truly intriguing is that stablecoins have quietly entered AI's "back-end system."

According to Circle's data, in the past 9 months, about 400,000 AI agents completed 140 million payments, totaling $43 million, with 98.6% settled via USDC, and an average transaction amount of only $0.31—this means that micro-transactions between machines are already continuously happening in a crypto-native way. In this sense, some AI practitioners aren't "believing in Crypto" verbally, but are using stablecoins as the default payment layer for agents, connecting the two tracks at the behavioral level.

However, at this point in time, if we're talking about "certainty in sectors," Chichi still prefers to view AI as the foundation for all industries, and Web3 / stablecoins as extremely suitable "infrastructure plugins" in certain scenarios, rather than强行 tying the two together and using a复合 narrative to explain all problems.

III. Certainty in the Entrepreneurial Path: Small Teams vs VC, Not an Either-Or

The impact of AI on the entrepreneurial path, Chichi概括 as "threshold reconstruction."

What impressed her most was the recent viral case of Medvi—a remote healthcare service company built around the weight-loss drug GLP‐1: founder Matthew Gallagher has an ordinary background, not a top-tier名校 graduate. In his home in Los Angeles, using about $20,000 and over a dozen AI tools, he spent two months building up the website, appointment process, consultation questionnaire, advertising materials, and customer service responses layer by layer.

The emergence of these "one-person companies" or "few-person companies" brings new certainty to the entrepreneurial path:

• It is certain that: by using AI well, the上限 of small teams is greatly raised, and starting a business no longer necessarily means first assembling a team of a dozen people;
• It is also certain that: not all projects therefore "no longer need VC."

Chichi emphasizes that she sees two coexisting realities:

• On one side, there are more and more cases of "building good companies without relying on funding"—a few tens of thousands of dollars can generate revenue, develop sustainably through rolling development, not necessarily following the traditional funding节奏;
• On the other side, there are directions that truly require heavy resources and investment: computing power, hardware, complex infrastructure, strong compliance scenarios—these projects很难切入 the window period without VC funding and resources.

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

• Trally excellent entrepreneurs who know how to use AI have lower dependence on money in the early stages,无需 compromising too much just to "make it";
• If VCs want to maintain their own certainty, they must shift from "giving money" to "giving resources," such as GPU, talent networks, channels, and brand endorsement.

She describes the current Silicon Valley: "Demo Day is almost every day." Large and small incubators and event spaces provide almost unlimited对接 opportunities for founders and investors; investors can directly留言 under posts on X or Product Hunt saying "want to invest in you," some funds even deliberately seek out "high school geniuses" for early bets.

In such an extremely active, extremely disintermediated financing environment, her advice to founders is:

• Don't rush to treat "whether to raise funds" as an either-or question;
• First use AI to get the product running, then judge whether you need "money," or "resources + brand + ecosystem";
• Treat VC as an amplifier, not a starting point.

IV. Conclusion: Amid Uncertainty, People Are Always Learning How to Adjust Themselves

Amid increasingly exciting technology and development, what Chichi sees is the same force refracted across different interfaces: AI is rewriting the existing order at an extremely high speed—company landscapes are shifting, sector 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 cities or valuations. The people she meets in HK and Silicon Valley—middle-aged finance professionals worried that "they're finished if they can't keep up with AI," big-tech engineers反复敲打 by layoff notices and visa deadlines—make her realize: insecurity has become the background noise of contemporary life. It doesn't disappear because you're at a big company or have stock options; instead, it's constantly amplified in an environment where information density is heightened and the pace is quickened.

Therefore, "seeking certainty in the AI wave" ultimately很难 remains solely at the level of discussing cities, sectors, or capital; it inevitably falls back to a more personal dimension: in such an environment, are people still willing, and still dare, to actively adjust themselves.