Don't rush to copy strategies from AI funding news

I don't understand the primary market, but I work on smart replenishment and pricing at Hema (Freshippo), and what scares me most is hearing "Capital is pouring into AI, so stores need to adopt it too." I tried using Kimi and Granola to break down this Huxiu article on AI financing.

Day one: I threw the original text into Kimi and asked it to organize where the money went, whether there were store-level actions, and which points were just investor judgments. The interface output looked very smooth, stating that global AI financing in the first half of 2026 exceeded 1.7 times the total for all of 2025, and mentioning that in five years, over 90% of capital would go to AI. It immediately listed smart pricing, demand forecasting, and inventory turnover.

But I got stuck. It directly translated the financing boom into "enterprises should purchase AI." Primary market financing means companies getting investors' money, not stores buying software. I asked it to separate facts, opinions, and actionable steps. Only on the second attempt did the output become clean: facts were the financing amounts and investor judgments; opinions were about bubbles and capital concentration.

Day three: I used Granola to listen to an internal meeting. It compressed the arguments between operations, procurement, and tech teams into to-do items, which was indeed convenient. However, there's a pitfall: it writes feelings, "shoulds," and "let's try" as conclusions. When I said, "Let's squeeze fresh produce loss a bit," it organized it as "AI loss prediction launch confirmed."

A week later, I made a basic table in Excel with fields for capital heat, store actions, and accountable metrics. The surprise was that it forced me to turn news into action. An AI financing boom shouldn't just result in "we deployed a large model," but rather in adoption rates of replenishment suggestions, stockout rates, loss rates, and rollback conditions for pricing experiments.

Kimi is good for quickly breaking down financial articles into product language, Granola is good for compressing meeting fluff into to-dos, and Doubao can help clarify terminology. The downsides are direct: they treat probabilities as facts and industry judgments as company decisions. If your question isn't narrow enough, it gives you a hallucination that looks complete. Especially regarding "over 90% of capital going to AI in five years," the AI will follow suit and say we must position ourselves now. But actual business impact depends on store feedback.

It depends. If you're a product manager, retail operator, or store manager, and you use it to read financial news, organize internal meetings, and break down implementation metrics, I'd recommend it. If you're an investor or want to directly generate a business plan, I wouldn't. It helps clarify problems but doesn't bear the results for you.

I'm not sure if the AI financing bubble will burst, but the tools that survive on the store side are likely those that can translate "capital optimism" into "who is responsible, how to verify, and how to roll back if it fails."