At this time, the industry is facing a move. That is: how to turn an ordinary AI scientist into a "heroic hero".
Lee Kai-fu, who began researching artificial intelligence three decades ago, felt that his "technical range" innovation workshop had the ability to promote this move and gain a solid strategic advantage in this move.
Kai-fu Lee said: AI entrepreneurship is now the world of scientists, followed by the world of mathematicians, the future is the common people's world.
The following is a closed-door sharing of Kai-fu Lee's "White Paper on Artificial Intelligence Strategy for Innovation Workshop". Lei Fengnet compiled it into "Lee Kai-fu: The Ten Truths of AI Entrepreneurship" and presented it to readers.
AI scientists are super otaku
The main organization of the innovation workshop itself is the investment and post-investment organization. Of course, we look at the project and look at the founders. They have ideas and directions. We will use funds to invest in it.
The Internet entrepreneurial model of the past has been described in a classic way by Lean Ventures:
Several children casually make a product, can raise funds for financing, and can't pull back when they can't. How to benefit the user, iterate the product, and then realize it, become a classic model.
This mode of entrepreneurship, its dividend era has passed. Of course there will be later, but not as much as before. The threshold for entrepreneurship has been greatly improved, because artificial intelligence is the next batch of entrepreneurial directions, and the core figure in artificial intelligence entrepreneurship is actually an AI scientist. The fact that AI's company does not have AI scientists is useless.
But AI scientists are often super otaku. They live in their rooms and do experiments all day. Suddenly you throw him into a brutal and terrible world. His own success rate is not very high.
Many AI scientists have never thought of starting a business in this life. Now they suddenly want to start their own business. They then find themselves with long boards and short boards:
He may be very skilled but may not perform enough;
Maybe his product is a good demonstration, but it's a Bug;
It is also possible that his product is doing very well, but he does not understand the market;
Or understand the market but do not know how to sell it.
In particular, the AI ​​itself is a ToB business, so it is not so easy to get a game. So AI scientists need to understand business people, ToB people, he needs engineers.
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