Twenty minutes, one familiar grid #
Matthew Berman’s recent video showcased a striking demonstration: Gemini 4 Argon Argon, Google DeepMind’s flagship reasoning model, generated a browser-based spreadsheet application in approximately 20 minutes. This unlooped, continuous run highlighted the agent’s autonomous capabilities.
Argon’s workflow began by opening Microsoft Microsoft Excel on Berman’s computer, using OS-level multimodality and computer vision to inspect the native application’s interface. This direct observation served as a reference before Argon proceeded to synthesize its own web-based counterpart.
The resulting spreadsheet, built in a single execution pass, demonstrated core functionality. It accurately processed basic arithmetic like =1+2, exhibited expected drag-to-fill behavior for cell replication, and successfully executed a =SUM(...) operation across highlighted rows.
While impressive, the demo focused on these specific features, not a complete Microsoft Excel replacement. Berman did note a minor parsing hiccup where an equals sign was initially misplaced during formula generation, requiring a quick manual correction. This feat, achieved within 20 minutes, underscores the accelerating trend towards zero marginal cost for software synthesis, challenging the traditional "moat" of proprietary codebases.
A convincing demo still has rough edges #
Berman’s demonstration of Gemini 4 Argon Argon truly impressed, showcasing an agent combining visual inspection, interface generation, and working spreadsheet logic in one concise task. Argon opened Microsoft Microsoft Excel on Berman’s machine, analyzing its layout and functionality before synthesizing a browser-based clone. This multimodal capability, coupled with rapid full-stack development, points to a future where software creation becomes dramatically more accessible.
One minor hiccup emerged during formula entry. Berman observed the equals sign was misplaced when he attempted to enter a SUM formula, requiring a quick manual correction before the calculation executed properly. This brief highlights the current boundary between a powerful prototype and a robust product.
While compelling, a few successful operations do not yet establish the comprehensive accuracy, resilience, or feature set of a mature application. A true Microsoft Microsoft Excel competitor needs to handle:
- Complex formulas
- Large files
- Edge cases
- Persistence
- Accessibility
- Security
Gemini 4 Argon Argon’s ability to generate a million output tokens in a single pass, a first for frontier models, is a significant leap. But the road from a 20-minute clone to a production-grade spreadsheet with deep functionality and zero defects remains a long one. The demonstration underscores AI’s incredible potential, while also clarifying the distinction between rapid synthesis and enterprise-grade deployment.
The real shock is how cheap imitation gets #
Berman's demo carries a stark warning: code alone may no longer protect a software business. If an AI agent like Gemini 4 Argon Argon can reproduce familiar features quickly, the traditional "moat" of proprietary source code erodes. This shifts the focus from what code you write to what value your software delivers beyond its basic functionality.
Cloning visible functionality is one thing; matching a mature product’s reliability, integrations, support, compliance, and years of user trust is another entirely. Microsoft Microsoft Excel, for instance, represents decades of refinement and an ecosystem of connectors, security protocols, and user habits that a 20-minute clone cannot replicate.
This demo signals a future where prototyping costs plummet. Argon, with its 1,000,000 output token generation limit and advanced multimodal reasoning, makes it incredibly cheap to spin up functional interfaces. However, transforming a prototype into a production-grade system still demands rigorous engineering, ongoing operation, and human oversight.
The real shock isn't that software will cost nothing, but that the apparent cost of building initial versions approaches zero. This dramatically lowers the barrier to entry for new ideas, while the demands for long-term maintenance, scalability, and user-centric design remain paramount. For more on Argon's capabilities, see Gemini 4 Argon Argon: Built to solve your hardest problems - Google Blog.
Enjoying this? Get one like it in your inbox each morning.
one email a day · unsubscribe in two clicks · no third-party tracking
What software companies should defend next #
Software companies need stronger defenses than code alone. Moats like trusted distribution, proprietary data, network effects, deep integrations, and dependable customer support offer more resilience. When an agent can rapidly reproduce features, the value shifts to what’s truly difficult to replicate.
To evaluate an AI-built competitor, ask what it can do beyond the demo. Who maintains it? What makes customers stay? A polished demo of Microsoft Microsoft Excel features is one thing; building a robust, enterprise-grade spreadsheet with complex integrations and a responsive support team is another entirely.
Matthew Berman’s video shows the impressive speed of Gemini 4 Argon Argon in generating functional code, completing its unlooped run in about 20 minutes. This capability will undoubtedly pressure commodity features. However, it does not show that established products or software teams are obsolete. Instead, it highlights the need for innovation beyond basic feature sets, focusing on defensible value and enduring customer relationships.
Frequently Asked Questions #
What did Gemini 4 Argon build in the video?
It produced a browser-based spreadsheet clone after inspecting Microsoft Excel, with a grid, basic arithmetic, drag-to-fill behavior, and a SUM formula.
Could the clone replace Microsoft Excel?
No. The demonstration shows a small set of spreadsheet interactions, not Excel’s broader features, reliability, compatibility, or enterprise controls.
How long did the AI build take?
Matthew Berman said the unlooped run lasted about 20 minutes.
What does the demo suggest about software companies?
When basic features are easier to reproduce, companies may need to compete through distribution, trusted service, data, integrations, and network effects.