When Elon Musk and Jensen Huang are both redefining AI, the real opportunities may lie in these as-yet-unseen projects.
If the most critical keyword for AI in the past few years was "large model", the industry is now entering a more complex stage.
Model capabilities are still improving rapidly, but what truly determines industrial value is no longer just "who has a larger model", but who can turn technology into products, turn products into businesses that customers are willing to pay for continuously, and then turn businesses into scalable and replicable business models.
This is also why every move of industry leaders such as Elon Musk and Jensen Huang continues to attract the attention of entrepreneurs, investors and industrial players.
From AI infrastructure to agents, to autonomous driving, robotics and spatial computing, technology is accelerating its move from laboratories to the real industry.
However, one easily overlooked problem is:
As the industry gets increasingly heated, projects that are truly worth paying attention to may become harder and harder to be noticed.
AI is booming, but not all projects can be easily understood
When you browse investment, entrepreneurship and industry news today, you will find no shortage of AI projects.
Some people develop models, some build Agents, some make robots, some produce AI hardware, and others embed AI into traditional industries such as healthcare, manufacturing, education, finance and consumer sectors.
The problem is that as the number of projects grows, information has not become easier to judge as a result.
What investors want to know is not "AI is very important", but:
What exact problem does this project solve?
Why are customers willing to use it?
What progress has the product made so far?
Does it have real users and commercial validation?
Can its technical advantages really form a competitive moat?
Is the market large enough?
Is the team capable of making this happen?
This is exactly the part that many startup projects most easily overlook.
A project may have strong technical accumulation, but its overly technical expression prevents external readers from seeing its commercial value; another project may have already obtained customers and completed product validation, but due to the lack of clear public materials, it still stays in a state of "only known to insiders".
It is two totally different things that a project has no value, and that the value of a project is not understood.
As the AI industry enters a deeper stage of competition, this problem will only become more and more important.
Common enlightenment from Elon Musk and Jensen Huang: Technology must eventually enter the real world
Why can the public views of Elon Musk and Jensen Huang always attract such high attention?
One important reason is that what they discuss is never just a certain technical parameter.
Elon Musk focuses on how technologies such as AI, robotics, autonomous driving and aerospace can change the real world; Jensen Huang continuously emphasizes the great value of computing infrastructure, AI ecosystem and the penetration of technology into various industries.
The two are on different industrial paths, but they both point to the same trend behind:
The competition of AI is gradually shifting from "technology demonstration" to "industrial implementation".
This means that what is truly worth paying attention to in the future may not necessarily only be the companies that are best at telling technology stories.
It may also be those projects that have turned technology into products, products into orders, and orders into sustained growth.
They may not have as much exposure as giants, nor do they appear on hot search lists every day, but they may be completing key validation in a niche industry.
An AI solution in the manufacturing industry may be changing the efficiency of factories;
A robotics company may be taking its first step from the laboratory to commercial scenarios;
An AI application company may have found stable paying customers;
A new materials enterprise may be entering the next-generation hardware supply chain.
Most of the real industrial opportunities do not emerge after everyone knows about them.
Therefore, the first problem that projects need to solve is to be "understood" after being "noticed"
For early-stage projects, it is not difficult to get one-time exposure.
The hard part is whether others can understand you within a few minutes after the exposure.
This is exactly the problem that 36Kr "Project Recommendation" aims to solve.
It does not ask project parties to repackage a story, nor does it simply make the enterprise introduction more "polished".
More importantly, it reorganizes the existing products, technologies, customers, business models and development progress of a project in a way that is easier for investors and industrial players to understand.
What the project parties need to answer are still the most basic and most important questions:
Who are you?
What problem do you solve?
Why do customers need you?
What progress have you made so far?
Why now?
Compared with other solutions, what is your real difference?
When these questions are clearly explained, a company truly owns a content asset that can be used externally.
One submission turns scattered information into a complete project profile
Many entrepreneurs will encounter similar problems when they face the media or investment institutions for the first time.
The technical team talks about technology, the sales team talks about customers, the founder talks about strategy, and everyone is telling the truth, but after putting them together, external people still find it hard to form a complete judgment.
The submission method of "Project Recommendation" solves this problem first.
Project parties fill in key information in a structured way, including the basic situation of the enterprise, products and services, target customers, industry market, business model, competitive advantages and development stage.
Complete the facts first, then discuss how to express them.
AI can participate in information sorting, structure organization and draft generation to help projects reduce content production costs; at the same time, the content still needs to go through manual review and editing.
Because for project content, the most important thing is not to "write like an advertisement", but:
Accurate information, clear logic and clear value.
Technology is responsible for efficiency, and editors are responsible for content boundaries and expression quality.
The final content formed is not a simple self-introduction of the enterprise, but a project profile that allows external readers to quickly build cognition.
From one-time exposure to long-term content assets
For startups, the real value of a project profile should not only stay on the day of release.
During financing, investors can learn about the project through public content first;
When FAs recommend projects to investment institutions, they can have a more complete pre-introduction material;
When enterprises seek industrial cooperation, they can let partners understand their business faster;
When parks, incubators and industrial service institutions recommend projects, they also have more standardized public content to reference.
Especially in fast-changing industries such as AI, robotics, new energy, new materials and advanced manufacturing, there are more and more projects, but the market attention is getting more and more limited.
Whoever can make others understand themselves faster will have more opportunities to enter the next round of communication.
This is also a change taking place in today's entrepreneurial environment:
In the past, entrepreneurs needed to solve the problem of "whether there is a project";
Now, more and more projects need to solve the problem of "whether others can understand me".
Giants define trends, and startups create opportunities at the next level
Industry leaders such as Elon Musk and Jensen Huang can lead the industry in new directions.
But for every industrial trend to be truly implemented, countless startups are needed to complete the last mile.
When robots enter factories, a large number of software, sensor, component and solution companies are needed;
When AI enters enterprises, a large number of vertical applications and basic services are needed;
For autonomous driving to further develop, the entire supply chain needs to mature together;
The emergence of new computing paradigms will also create a number of new demands that did not exist before.
Therefore, what is truly valuable about industrial trends is never just "who is standing in the spotlight".
What is more worth paying attention to is:
Outside the spotlight, what other companies are solving real problems?
These companies may not have the exposure of giants, nor billions of users, but they may already have products, customers, technology and commercial validation.
Sometimes what they need is not to tell a bigger story.
It is an opportunity for more people to truly understand it.
Submit your project to 36Kr now
If you are starting a business, or serving a number of noteworthy startup projects, no matter you are a project party, FA, investment institution, park, incubator or industrial service institution, you can submit your project through 36Kr "Project Recommendation".
After the project information is reviewed, projects that meet the content requirements will enter the subsequent content production and release process.
If your project already has products, customers, technical accumulation and commercial progress, or you are verifying a new industry opportunity, then now may be the time to introduce it to more people seriously.
Not all good projects will be noticed automatically.
But a project that has been clearly explained at least has the opportunity to be further understood, connected and discussed.
Submit your project for free:
https://36kr.com/seek-report-new
36Kr Project Recommendation Contact Email:
aireport@36kr.com
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