I Asked Google's New AI (Gemini Argon) to Fix My Golf Swing. It Built a Simulator Instead in less than 30 minutes.
From a 10-second video to an interactive engineering application—and a glimpse of what AI could do next.
Two days after I finished teaching an executive course on AI strategy, Google released its new flagship AI model, Gemini 4 Argon.
Naturally, I wanted to put it to the test.
Instead of asking it to write an essay or analyze financial statements, I uploaded a ten-second video of my rather imperfect golf swing.
My question was simple: Where am I losing power?
What happened next was considerably more interesting than receiving a few golfing tips.
Argon analyzed my movement, identified possible mechanical problems, and helped create an interactive HTML application where I could inspect my swing, adjust hypothetical body movements, and explore what might happen to the ball.
As an engineer, I found the software-development achievement more exciting than the golf advice itself.
1. What's So Special About Argon?
Argon belongs to a class of advanced AI systems known as frontier models—models operating near the leading edge of AI capabilities.
One of Argon's notable strengths is spatial intelligence: the ability to reason about objects, their positions, and their movements in physical space.
Recognizing a golfer in a video is relatively easy. Understanding how the golfer's hips, shoulders, wrists, and club interact is much harder.
Imagine the difference between recognizing a photograph of a moving car and understanding how its steering, suspension, and tires affect its movement.
To measure such capabilities, researchers use standardized tests called benchmarks.
One relevant benchmark is ERQA (Embodied Reasoning Question Answering). Think of it as an examination testing an AI's ability to reason about physical and spatial situations.
Google's October 2026 results show:
AI model | ERQA score |
Google Gemini 4 Argon | 87.0% |
Anthropic Claude Opus 5.5 | 82.3% |
OpenAI GPT-6 Astra | 81.0% |
These are provider-reported results, not my own independent tests. A score of 87% means the model answered 87% of the benchmark questions correctly—not that it understands 87% of physics.
Alibaba's Qwen3-VL, for example, is designed to interpret both images and language. A June 2026 paper reported 83.4% for Qwen3-VL-32B on ERQA-Plus, a related but different evaluation.
2. My Golf Swing: Where Was the Power Going?
My video included measurements from a Foresight Sports launch monitor—a device that measures what happens when the club strikes the ball.
Here were my results:
Measurement | My shot | Explanation |
Carry | 136 yards | Distance through the air |
Total distance | 146 yards | Including the ball's roll |
Launch angle | 19.2° | How steeply the ball initially rises |
Smash factor | 1.31 | Ball speed divided by clubhead speed |
Argon examined key moments in my swing and suggested three possible problems.
First, early extension. My hips appeared to move toward the ball during the downswing, reducing the space available for my arms.
Second, loss of posture. It estimated that my forward spine angle changed from approximately 36° at setup to 26° near impact.
Third, early wrist release. My wrists appeared to release the club sooner than desirable, potentially changing how efficiently I delivered the clubhead to the ball.
Argon proposed a connection between these movements: the hips move forward, the body changes posture, and the hands compensate to strike the ball.
A fascinating diagnosis—but still a hypothesis. One smartphone video cannot accurately measure every three-dimensional movement or the forces generated by my body.
Then I decided to push the experiment further.
3. From Golf Video to Working Software
I asked Argon a different question:
Can you turn this analysis into an interactive webpage?
It generated an HTML application combining several functions that would ordinarily require quite different programming skills.
The application can:
Play my original video in slow motion, with controls for stepping through individual frames.
Draw colored lines over the video to highlight the estimated positions of my hips, shoulders, hands, and club.
Jump directly to important moments, such as the transition and impact.
Allow me to manually correct the positions of graphical markers.
Display an animated, two-dimensional stick-figure representation of my swing alongside a hypothetical corrected movement.
The webpage uses ordinary HTML, CSS, and JavaScript, without requiring a separate application server.
But the most intriguing feature came in the next version.
4. What If I Could Correct My Swing?
I asked Argon to add a What-If Simulator.
Suppose I could stop my hips from moving excessively toward the ball.
What might my swing look like? And could I hit the ball farther?
The resulting webpage includes a slider that changes the simulated hip movement. As I move it, the animated golfer changes, and the application displays hypothetical performance figures.
Original | Simulated correction | |
Carry | 136 yards | 150 yards |
Launch angle | 19.2° | 16.2° |
Smash factor | 1.31 | 1.38 |
Fourteen extra yards? I would certainly welcome that!
But there is an important catch.
These improvements are illustrations generated using simplified formulas. The current application is not a scientifically validated golf simulator. It cannot guarantee that changing my hip movement will actually produce these results.
That is precisely where further engineering and real-world measurements would be needed.
Nevertheless, the concept is powerful.
Instead of simply asking AI what went wrong, I now have a tool that lets me experiment with what might happen if something changed.
This is an early example of a digital twin approach: using a computer representation of something real to explore its behavior under different conditions.
5. Why This Impressed Me as an Engineer
Having spent decades working in electronics, software, and technology businesses, I know that building even a modest analytical application can involve several disciplines.
This one combines video processing, geometry, mathematics, computer graphics, user-interface design, and domain knowledge.
For example, the program must synchronize a moving video with graphical annotations and calculate intermediate positions between selected frames.
It must also respond immediately when I move a control.
All of this was generated and refined through natural-language instructions.
The breakthrough is not simply that AI can write code. It is that AI can connect an unfamiliar real-world problem to a working software application.
Of course, the generated program still requires human judgment. Attractive graphics and precise-looking numbers do not necessarily imply accurate science.
But the ability to build and test an initial idea has become dramatically more accessible.
6. Forget Golf for a Moment. What Else Could This Do?
This is where I see the real opportunity.
Imagine applying the same approach to other problems.
Manufacturing: Record a production line, identify bottlenecks, and build an interactive model for exploring changes to machine schedules or workstation layouts.
Healthcare: Record a rehabilitation exercise, visualize changes in movement, and help clinicians explain possible adjustments—with appropriate medical validation.
Robotics: Observe how objects move in the real world, construct simulated environments, and test robot actions before deploying them.
Education: Turn an ordinary physics experiment into an interactive lesson where students adjust parameters and explore the consequences.
Business: Generate decision-support applications where managers can change assumptions about staffing, inventory, pricing, or energy use and explore different outcomes.
The general idea is straightforward:
Observe → Analyze → Simulate → Experiment → Validate → Improve.
The difficult part is ensuring that the underlying mathematical model represents reality. But AI can increasingly help create the software needed to investigate these problems.
7. One Practical Lesson: Powerful AI Needs Careful Management
Argon is impressive, but frontier models come with usage limits and computing costs.
Complex coding tasks can consume considerable tokens—the units of information processed by AI—particularly when the model must repeatedly examine long programs.
During this project, I learned to preserve working code and request specific improvements rather than continually rebuilding everything.
This is an increasingly important principle in AI-assisted development.
Use expensive frontier models where sophisticated reasoning is genuinely needed. Use smaller or lower-cost models for routine tasks.
The best AI workflow may involve several different models, not just one.
8. The Bigger Picture
I started this experiment hoping to find a way to hit my golf ball farther.
Instead, I discovered something more interesting.
A short video had become a functioning interactive application combining visual reasoning, geometry, programming, and hypothetical simulation.
The application is far from perfect. Its biomechanical explanations need verification, and its performance predictions are illustrative rather than scientifically validated.
Yet the implications are significant.
For years, developing specialized analytical software required considerable programming expertise, time, and resources.
Today, frontier AI models are increasingly able to help individuals turn ideas into working tools.
This could fundamentally change how engineers, scientists, educators, entrepreneurs, and business leaders approach problems.
The future of AI is not just about getting better answers. It is about building better tools to explore the questions.
And as for my golf swing?
Unfortunately, even Google's most advanced AI cannot yet rotate my hips for me.
References: Google DeepMind, Gemini 4 Argon announcement and technical benchmarks (October 2026); Alibaba, Qwen3-VL Technical Report (2025); ERQA-Plus research (2026).
Note: This was an exploratory AI experiment. The biomechanics analysis and simulated golf performance have not been independently validated.
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