1. Exploring and using a large number of AI products hands-on to understand the true capability increments that AI can provide to entrepreneurs without a coding background, and the current capability boundaries of AI tools. I have built a complete AI workflow and independently launched a SaaS product that has even started making money, currently in limited use, and I expect to share it with everyone in a few days.

2. A complete summary of the entrepreneurial insights and lessons learned over the past year. I have outlined the entrepreneurial principles I hope to always follow and the traps I hope to avoid in the future.

One of the most poignant entrepreneurial principles I hope to always follow comes from a recent public sharing by a well-known entrepreneur:

“Do not release your product too early.”

This was the response from Google's founder at a campus event at Stanford in December 2025.

Roughly the context is that in December 2025, during the closing event of the centennial celebration of the Stanford School of Engineering, Google co-founder Sergey Brin was invited back to school for a dialogue. The dialogue partners were Stanford President Jonathan Levin and School of Engineering Dean Jennifer Widom.

A student asked how to avoid traps in entrepreneurship:

Brin's core answer: Don't create a big spectacle to release your product before it's ready.——He directly used Google Glass as an example, implying that when you have a cool new hardware idea, you must first refine it thoroughly before organizing a cool launch event involving skydiving or airships.

This sharing was very sincere; most entrepreneurs in such events share politically correct views or high-level inspirational quotes that sound encouraging but lack specific actionable steps. But Brin provided a very practical point.

We spent a lot of time, made many mistakes, and incurred significant costs to understand the importance of this statement.

Because the conventional entrepreneurial viewpoint we have always accepted includes concepts like lean startup, lightning-fast startup, user-first, and rapid iteration.

Why this may be wrong, let’s first discuss Brin's core support: because once you release a product too early, it's very hard to distinguish whether you are on the right iterative path or just constantly patching various so-called user desires. Once you start sending signals externally, it’s like getting on a “treadmill” — you’re bound to a delivery timeline, but you may not have enough time to complete everything that needs to be done. Meanwhile, external expectations will snowball, and you haven't given yourself enough time to digest, judge, and handle those expectations.

Based on my personal entrepreneurial experience, another important reason is that when you release a product too early, it may indicate that you have not thought about two questions:

1. What currently drives the birth of winners in this track: is there still a product-driven opportunity.

2. Then, of course, you also want to clarify the next question: If it is product-driven, what are the actual functionalities, performance, or designs of the products that can indeed drive this?

1. We did not realize that there was still a product-driven opportunity in the market at that time (nor did we assess this possibility), we placed too much emphasis on the so-called time window, which fundamentally boils down to our excessive focus on opportunity-driven thinking along with a systemic laziness regarding strategy over hustle.

2. Since we did not assess whether there was still an opportunity driven by the product, we, of course, could not make optimal decisions regarding what truly drives the product. Hence, our differentiating point was the later disproven “multi-chain” concept. But the market proved that for trading terminal products, the underlying product-driven must rely on: information advantage (alpha, or at least, give users the sense of alpha) or time advantage (performance).

This mistake only became clear when Axiom emerged, relying on product performance, and quickly gained the largest market share despite being released later (in what seemed to be a highly competitive environment). We understood 80% of this. Why not 100%? Because we continued to make mistakes afterwards, failing to go all in on alpha and performance, still aligning and completing functionalities. And we continue to incur costs for this mistake as of today. Yes, we are currently spending time aligning performance (and this has been a year since we released the product, during which 90% of people already feel the trading terminal industry is meaningless).

In simple terms: when we started our business, we too easily took speed and iteration as unfailing truths, neglecting to consider where the decisive edge in market competition lies. Additionally, we too readily regarded any early feedback from users as positive motivation, which easily caused misalignments in the direction of iteration and increased the sunk costs (time + emotion) of swiftly adjusting or even shutting down the business later on.

In the AI era, I believe this is even more true. Tools have flattened productivity gaps and strengthened information equality. This means that the production costs of merely adequate products, as well as those lacking leverage in product design, have sharply decreased, rendering the term “hustle entrepreneurship” meaningless.

As the saying goes: When the magic lamps are everywhere, what wishes you make become more important.

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