The difference generated by the action is the coordinate.
I saw a piece of news about NASA that I found quite interesting and thought-provoking, so I'd like to share it with you today.
In simple terms, NASA has a mission called Starling, which recently released the results of a flight experiment. They developed a FALCON system that enables a small satellite to calculate its own location without using GPS at all.
We've all seen those space sci-fi movies, where you can imagine a lone satellite floating in the vastness of space. In the past, for this satellite to know its own location, it was never something it could figure out on its own, but rather it had to rely on external navigation information.
In low Earth orbit, the most common method is GPS; a more traditional approach relies on ground stations for tracking. What NASA did this time was to remove the real-time navigation input from GPS, allowing the satellite to navigate on its own by observing and calculating its position.
To be honest, it's not just about navigating in space, even in a city, I habitually use navigation for a road that has been open for a year.
Even if you're familiar with a route, having to navigate it multiple times can sometimes trigger inexplicable panic, leaving you wondering: "Is this the right turn? Why does it look off?"
I'd like to say at the outset that, in my view, this is not just a space-related news story, it will ultimately have an impact on things that we all experience.
Space Junk Could Become Road Signs
As for the small satellite, what does it rely on to position itself in the vast space? It relies on sight.
The satellite had something called a star sensor, which can be roughly described as a camera that looks up at the sky. Its original task was to identify stars and determine its own orientation.
But this time, NASA had it look at other things, such as other spacecraft floating by, as well as orbital debris, also known as space junk.
An object passes through its field of vision, and the small satellite recognizes what it is and knows its approximate location, so by working backwards, it can also determine its own location.
It's like navigating a wilderness without road signs, where you can only rely on the large rocks in front of you and the crooked-necked tree that has been dead for decades in the distance.
Of course, space debris is a bit different from those rocks, as it's not stationary, but rather flying around the Earth at high speeds. However, NASA has a catalog that tracks who they are and where they are predicted to be at any given time.
So, the small satellite sees it, and then matches it with this catalog, allowing it to reverse-engineer its own location.
For this satellite, even space debris can become a landmark. People have been condemning space debris for decades, but this time, its despised attribute has actually come in handy.
It's there anyway, and it can't be cleaned up for the time being.
Simulation vs Live Observation
By now, you're probably thinking this all sounds rather romantic. I initially stopped there too. But what truly made me sit up straight was the second thing this FALCON system did.
The ground station uploaded the catalog to the small satellite: 20,000 space objects, each with a predicted orbit, from the official US catalog of space objects.
This is its textbook, equivalent to the most professional people on the ground telling it: all the answers are here, just follow them to find them.
As a result, the FALCON system compared the live footage from the camera with this base image and found discrepancies.
In three days, it autonomously corrected the orbital data of over 200 objects without any intervention from ground personnel. Moreover, for the objects it had directly observed, the accuracy of the corrections was higher than the catalog provided by the ground station.
This is equivalent to when a teacher has just handed out the exam papers, and the class genius glances at them, then calmly raises their hand and says, "Teacher, this question is wrong." The whole class erupts in shock, and the teacher looks at the paper in embarrassment, only to find that the question indeed contains an error.
I won't ask why I was so deeply moved, but it was for this reason that I decided to go to Beijing to be an internet worker rather than a teacher.
But back to this news story, thinking about the process again will make you feel terrified.
In theory, the ground station is the center, the authority, the place with the most computing power and the most experts. It has a large amount of historical data and orbital models, and its role is to tell the satellite: where you are.
As a result, this small satellite, which came equipped with a camera and a bit of edge computing power, discovered that the data provided by the center was incorrect after reviewing it. It then revised the directory in its possession on the star.
The most interesting aspect of this matter lies right here.
The catalog was predicted using a large amount of known parameters to extrapolate forward, and the logic can be said to be very rigorous. In contrast, the camera on the small satellite simply observes, and what it sees is what it records.
One involves extrapolating from the past, while the other involves observing what has just happened in reality. When the two collide, observation wins.
Isn't this just what we always say: "what is heard is false, what is seen is true"?
The Entire AI Industry is Lost in Space
Speaking of which, I have to mention the name FALCON.
It is actually an abbreviation, with the full English name being Fast Autonomous Lost-in-space Catalog-based Optical Navigation.
There is a phrase here called Lost-in-space, and a classic science fiction work is also named this, translated as "Lost in Space" or literally "Miguo Taikong" but more commonly known as 《迷失太空》.
However, the name of this system is not romantic, nor is it intended to pay homage to classic films.
In aerospace, it originally referred to a specific situation that is quite troublesome: the device has just awakened and has no ready answer to tell itself where it is or which direction it is facing, and can only re-identify itself based on what it can see in its field of vision.
See, isn't this exactly what FALCON is doing?
Without GPS directly telling it its coordinates, it looks up, observes its surroundings, and then uses what it sees to compare with its database, gradually calculating: oh, I'm probably around here.
But what I want to say is, when I see this term, what comes to mind is not a satellite, but rather all the people currently working on AI.
The 88% and 1% Divide
Two days ago, I read a report from Tencent Research Institute about super individuals, which began by citing two numbers.
An annual AI Index from Stanford HAI found that 88% of global organizations have applied AI in at least one business function, while a separate global survey by McKinsey showed that only 1% of companies believe they have truly achieved AI maturity.
Look at these two numbers, 88% and 1%, how striking they are when put together.
The report refers to this as a huge gap. As for "having implemented" it, in many cases this means simply purchasing an account, establishing a committee, or drafting a policy document, with two or three people in the department figuring things out on their own, while the organization as a whole remains unchanged.
To be honest, this is completely consistent with what I saw.
I went to Silicon Valley and met with some of the companies that appear to be the most radical from the outside. Many people think they have a clear roadmap in hand, but after talking to them, they said: We're also still testing and don't know if tomorrow's model iteration will overturn everything.
So, what is the current state of the entire industry?
Everyone is powered on, everyone is moving, but no one has a GPS in hand — all lost in space.
Why the Old Roadmap Failed
At this point, I know some of you may start to lose interest.
You may think that you're not an entrepreneur and don't need to think about strategy every day. You're not the boss, and you're not the one who decides which direction the company will take at the beginning of each year.
I'm just an ordinary person working in a company, and my work style is as follows: at the beginning of the year, I set goals, break them down into KPIs, write reports every week and every month, and review my progress quarterly, then execute accordingly.
You're waiting for a clear plan, waiting for the top to determine the direction, allocate resources, and pave the way, and then you'll be responsible for moving quickly and accurately.
I completely understand this feeling, as it has been repeatedly proven to be an effective approach over the past 20 years. In the past, the pace of environmental change was predictable, and the plans made at the beginning of the year would generally remain on track by the end of the year.
The central authority indeed has a broader view than the periphery, and headquarters indeed has more information than the front lines. The directory issued from top to bottom is indeed more accurate than what you can see by looking up. Following it will be the most efficient, and you won't have to take responsibility.
So, in the past, you would wait for a plan or wait for instructions from your superiors, and there would be no problem at all.
The problem is with the present. Currently, that "catalog" is no longer accurate.
Because all previous plans were extrapolated based on known conditions, moving forward from what was already known. However, the current known conditions, such as model capabilities, costs, and tool forms, are changing every three months.
The December you envisioned in January may no longer be the same world as the actual December. Consider the lobster-themed OpenClaw, which was all the rage in March this year - it's been less than half a year, and it already feels like a distant memory.
Give me a clue, no matter how vague
What can be done then?
FALCON's approach is rather simple: use whatever you can see. Even space debris works, as long as it can provide positioning.
For us, it's about taking action first. We create an AI tool, conduct a process transformation experiment, and build a rough but working prototype.
It's probably useless, likely to be scrapped in three months, and likely to prompt colleagues to say, "What's this?" Fine, then you've got a piece of junk.
But this garbage has coordinates.
Trash can serve as a landmark not because it is useful in itself, but because it has a fixed location and can be recognized by a camera.
But before I go further, I need to make one thing clear—otherwise this risks sounding like empty inspiration, the kind that makes people think they can just do something half-heartedly and call it a day.
It should be noted that the FALCON system is not just looking blindly, it is looking with a directory in hand. Therefore, every glance can produce a reading: I think you are here, but you are actually there, and the difference is this much.
This difference is what it actually gets.
If you ask AI to create a "Hei Sheng Hua: Wukong" and what comes out is not trash, but noise, then you have no expectations for it, and it's unclear what it will turn out like. In that case, it's impossible to discuss how it deviates from anything or what it tells you.
If you're just doing 200 or 500 of these, your capabilities won't be improving.
So, before you actually get started, write yourself a catalog, in one sentence: what do I think AI can do for me.
Once you finish the work, compare the result against the target. The gap that doesn't line up is the coordinate you've picked up. For this reason, it's best applied to the small, recurring tasks you actually do every week — small enough that when you fail, you can pinpoint exactly which step went wrong.
From Super Individuals to Super Teams
Speaking of this, there's a follow-up to the news that I think is the most interesting aspect of the whole thing.
Starling itself is a constellation of four satellites, and the aforementioned experiment mainly involves one satellite observing and calculating on its own. However, NASA plans to take it a step further by having the four satellites share what each of them sees.
If FALCON can operate in coordination on multiple satellites, with four satellites each observing and sharing what they see, using combined observations to jointly correct each satellite's position, what changes would occur?
A satellite that can observe itself, position itself, and even dare to modify the data of ground stations, that is a super entity.
Four individuals laying out everything they see on the table, correcting each other, and becoming more accurate together, that's a super team.
The report from Tencent Research Institute also listed "influence spillover" as a necessary characteristic of a super individual, stating that
If someone uses AI to increase their output by tenfold without their colleagues noticing, they have not yet become a super individual, but rather an excellent employee who is skilled at using AI.
At the time, I thought this statement was correct, but I didn't understand why it was necessary, and the satellite gave me the answer.
If a satellite's observations are not shared, they are merely its own private experience. It alone becomes more accurate, while the other three continue to use the outdated catalog. Only by making its observations public can it transform from a single entity's perspective to a common landmark for the entire constellation.
So, being seen is not about giving face or motivating employees, it is a prerequisite for whether the entire system can be standardized.
It's like when someone in your company develops an AI tool on their own and boosts their work efficiency by three times - if nobody knows about it, the organization hasn't learned anything from that person.
Doubao Experience Written Back to Same Directory
And if you look back at those three days of FALCON, the most valuable part wasn't correcting the orbits of more than 200 objects—it was that every correction was written back to the same catalog, making the catalog in your hands that much more accurate.
In other words, the two hundred demos you casually asked AI for, each one isolated and not written back anywhere, that's not called doing it two hundred times, that's called doing two hundred first times.
If you have an AI-related matter at hand and are waiting for a more mature opportunity, a more complete plan, or a clear direction from the top, my suggestion is:
This week, take action and solve the small issue in your own hands, don't try to change others' processes right away.
After completing the task, write down the learned sentence and accumulate it in the same place. Then, let people see it. Note that what people see is not the tool, but the pit you touched.
Even if what you produce is rubbish, it's still a coordinate on your path forward.