Since the outbreak of AI, our work scenarios are all full of AI, and this article takes "Kujiale" and "Instant Design" as examples to see how AI large models are applied.
Recently, I've been thinking about why AIGC (generative artificial intelligence) has ushered in an explosion in the industry since last year.
In fact, AI technology has been used in the Internet industry for a long time, but this wave of AI is to make every ordinary person, especially advertisers and self-media people, feel the impact.
And these people are often novice technovices, and when a new productivity tool comes along, the first reaction is that this thing is going to make me unemployed, and I have to fight hard.
In fact, we all misunderstand AI.
AI understood abroad will be more crazy, such as the complementarity of brain-computer interface and AI technology, subverting the way of human-computer interaction, and the technical research and development of general large models.
However, the reality is that many domestic enterprises still regard AI as a tool to promote the upgrading of the Internet industry.
In other words, foreign countries really want to use AI to change the world, but our culture is more pragmatic, especially focusing on commercial applications in various industries.
The reason is simple, we are all realists who are small and rich, and rarely think about the grand future.
"Not hegemony" and "poverty alleviation and agricultural assistance" are the business values recognized by the main theme. Wanting to make money from the country is the direction of top talent research, and it has little to do with us.
Therefore, we will not talk about "human GG" and "the world is turned upside down" at every turn. And do these words really sound rational?
Therefore, you will find that the current commercial implementation of AI in all walks of life is still very cautious. One is that a large number of SaaS companies in the market have accumulated a large number of professional groups and vertical industry data in the last Internet era, so they naturally introduce AI interfaces and products to make the original user experience a little better.
Note that it's just a little better, because these professionals have managed to get used to the old SaaS platform, and if they are now using the new tools, the migration cost of the users will be very high. Therefore, customized development is the way out.
This is also in line with the market-oriented logic: where the user's work is used, AI can be used. For any company that wants to cut into the vertical model track, cooperating with SaaS is the most worry-free way to monetize technology.
Two examples that have touched me deeply are "Cool Home Music" and "Instant Design".
6 years ago, I used Kujiale to design and decorate the house myself, and now it has a new feature, which can generate various home styles at will, and can also train the model by itself:
And the real-time design is also amazing, it was originally just a platform for online painting product DEMO, and now it has launched "instant AI", which can generate a beautiful web UI with one click:
In addition to the huge volume of SaaS, the market is also flooded with a large number of individual developers and studios. They usually have a little self-entertaining spirit, make some single-point breakthrough gadgets, and earn membership fees and training fees for C-end users by relying on information asymmetry.
Some of the entrepreneurs are engineers, some are designers, and some are self-media people. The essential logic is to take advantage of the AI outlet and rely on personal influence to monetize.
For users, it's more than enough for a personal developer to solve some of my daily minor problems. But if the company doesn't reimburse me for the cost of the tool, what is my motivation to top up? Why should I do it myself? Why not leave it to the professionals? And professionals do it better than non-professionals, otherwise how can the premium of the profession be reflected?
Take training a LoRa model as an example. Training requires a certain programming foundation to write model code, define model structure, set training parameters, etc. At the same time, it is also necessary to understand deep learning, machine learning and other related knowledge in order to better understand and apply.
If I don't know how to train, my learning cost will be very high, and with this cost, maybe I would rather purchase professional training services;
If I find that I can't solve the problem of the customer and me after spending money on training, I may not rely too much on this technology, but choose other ways to make up for it for the time being.
Just like every owner who needs to decorate, many people will not choose to design by themselves, one is because they have no time, and the other is because they are not capable enough, so they will hand it over to professional design agencies and decoration companies;
In the same way, brand Party A will not implement the integrated marketing plan by itself, but he needs to purchase different services from different Party B, find creative hot stores for creativity, find public relations companies for public relations, and find specific platforms for advertising.
This is called specialization in the art industry, and only then can the flow of budget be promoted. Most customers are likely to trust people more than AI.
After all, you can't complain about problems that AI can't solve, but most customers can choose to complain about the person who serves them.
AI will never help you bridge the gap between professions, nor will it ever bridge the information gap between industries. Therefore, I now feel that it is more important to cultivate professional ability.
The origin of all business logic is the customer's basic perception of you and years of trust.
Now, in the retail, healthcare, and financial sectors, there are SaaS platforms that have integrated AI capabilities.
For example, some platforms use AI technology to provide intelligent consultation and medical record analysis to help doctors improve the efficiency and accuracy of diagnosis; in the retail field, some SaaS platforms use AI technology to achieve intelligent recommendation, inventory management and other functions to improve user experience and operational efficiency; in the financial field, AI technology also plays an important role in risk assessment and customer management.
Only by looking at AI in this way can it be truly "disenchanted". Because these technologies have already been applied in many Internet companies, only they can truly grasp valuable industry data and reduce the cost of training vertical industry models.
Finally, back to a marketing perspective, no matter how technology changes, "taking the first-mover advantage" will always be the moat of the enterprise. In the future, the technical threshold will only get lower and lower, and the competitive foothold of enterprises will still return to the brand mentality.
As the "Positioning" theory says, people will only remember the leading brands in each segment.
Author: Jin Xin YOYO;
This article was originally published by @一个符号工作室 on Everyone is a Product Manager. Reproduction without permission is prohibited
The title image is from the screenshot of Sora's video presentation.
The views in this article only represent the author's own, everyone is a product manager, and the platform only provides information storage space services.