Muse: A Tool Built to Convince the People Around You That AI Is Actually Useful

When AI Needs a "Convincing" Use Case

In an era of breakneck advances in AI technology, there's a somewhat awkward reality: many ordinary users remain skeptical of AI. Whether it's the explosion of ChatGPT or the endless parade of new large language models, for a significant number of non-technical people, AI remains stuck in the "sounds impressive, but what does it have to do with me?" stage.

This skepticism isn't irrational — it has deep social and psychological roots. A 2024 Gallup survey found that over 52% of American adults feel "more worried than excited" about AI. This skepticism stems from multiple factors: the "black box" nature of AI systems makes their decision-making logic hard to understand; media coverage of AI replacing human jobs triggers existential anxiety; and the actual experience of early voice assistants like Siri and Alexa fell far short of marketing promises, creating a lasting trust deficit. In psychology, this phenomenon is known as "technophobia" — it's not merely resistance to new things, but a primal defensive reaction when humans feel their capabilities are being encroached upon by technology.

It's against this backdrop that an AI tool called Muse has appeared with a positioning that's both intriguing and refreshingly blunt — its tagline is enough to make you smile: "Built to convince your family and friends that AI is actually useful."

This slightly tongue-in-cheek slogan actually highlights a pain point that has long been overlooked in AI adoption: no matter how advanced the technology, if ordinary people can't feel its practical value in their daily lives, its widespread adoption will always be separated by a pane of glass.

What Problem Is Muse Trying to Solve?

From "Showing Off" to "Being Useful"

Over the past two years, the AI field has produced a flood of jaw-dropping demos: writing poetry, creating artwork, coding, holding conversations. Yet for the average consumer, these capabilities often feel "interesting but not essential." Sure, you can use AI to generate a beautiful image, but that doesn't necessarily change your way of life.

In fact, there's a widely discussed "demo curse" in the AI world: a system performs brilliantly in carefully choreographed demonstration scenarios but stumbles repeatedly in real-world use. The technical root of this phenomenon lies in the fact that large language models are fundamentally probability-based text generation systems whose performance on open-domain tasks is inherently unpredictable — the so-called "hallucination" problem, where models confidently produce output that seems plausible but is actually wrong. OpenAI co-founder Andrej Karpathy once described this dilemma as the "80/20 trap": getting AI to handle the first 80% of a task is easy, but the remaining 20% — ensuring reliability, handling edge cases, adapting to real user needs — often requires many times more engineering effort.

Muse's core approach is to package AI capabilities into a practical tool that delivers immediate results in real-life scenarios. It doesn't aim to showcase how smart the model is. Instead, it aims to make users' family members and friends — the people who normally couldn't care less about AI — exclaim, "I had no idea AI could help me like this."

This product philosophy, designed around "persuasiveness" as its goal, represents a noteworthy direction in the evolution of AI applications.

Product Design for the "Non-Believers"

You might not have noticed, but Muse's target users aren't tech enthusiasts who already embrace AI — they're ordinary people who are on the fence or actively skeptical. This is a remarkably smart positioning, because the true mass adoption of AI technology has never depended on the geek community. It depends on whether AI can cross the "chasm" and enter people's everyday lives.

The "chasm" here refers to Geoffrey Moore's technology diffusion theory from his classic book Crossing the Chasm. The theory divides the technology adoption curve into five stages: Innovators (2.5%), Early Adopters (13.5%), Early Majority (34%), Late Majority (34%), and Laggards (16%). The "chasm" specifically refers to the massive gap between Early Adopters and the Early Majority — many seemingly promising technology products fail precisely at this stage. While ChatGPT set the record for the fastest product to reach 100 million users, its monthly active user retention rate and depth of engagement are far lower than that number might suggest. The real challenge isn't getting people to "try" AI, but getting them to incorporate it into their daily habits.

When a tool's success metric is defined as "can it convince your parents, partner, or friends," it inevitably demands that the product delivers on three dimensions:

• Ease of use: The barrier to entry must be low enough that no technical background is needed
• Immediate value: Users must see results right away
• Emotional resonance: The problems it solves must be ones users genuinely care about in their lives

And these are precisely what many technology-driven AI products lack.

What This Means for the Industry

AI Applications Enter the "Everyday" Competition Phase

Muse's emergence is not an isolated case. It reflects a fundamental shift in priorities across the entire AI industry:

• Phase One: The model capability race — who has more parameters, who scores higher on benchmarks
• Phase Two: The application-layer implementation race — who can translate capabilities into real user value

This shift has deep commercial logic behind it. Between 2023 and 2024, the cost of training foundation models skyrocketed — GPT-4's training cost is estimated at over $100 million, and next-generation models could reach hundreds of millions or even billions of dollars. Meanwhile, the marginal improvement in model capabilities is diminishing, a phenomenon the industry calls "the slowdown of Scaling Laws." This is forcing industry players to shift attention from "training bigger models" to "building better applications." In a widely circulated analysis, Sequoia Capital pointed out that the AI industry faces a "revenue gap": the infrastructure layer (GPUs, cloud computing) has captured substantial investment returns, but application-layer monetization has yet to deliver on its promises. In this context, application-layer products that can genuinely create user value have become the scarcest assets.

Positioning a product around "convincing the people around you" is essentially emphasizing the last mile of user experience. The competitive moat in technology is gradually shifting from "how powerful the model is" to "how smooth the experience is and how obvious the value is." Whoever can make the most ordinary user feel the benefits of AI in the shortest time holds the key to the mass market.

The Viral Advantage of Emotionally-Driven Marketing

From a communications perspective, an expression like "convince your loved ones" has natural viral potential. It transforms a technology product into a topic connected to family, relationships, and everyday life.

The strategy Muse employs has classic theoretical support in the Growth Hacking playbook. This mechanism is known as "inherent virality" — the product's core use case inherently involves showing it to and sharing it with others. The most successful historical examples include: Hotmail adding "Get your free email at Hotmail" to the bottom of every email, PayPal's transfer function naturally requiring the recipient to also register, and Dropbox's "invite a friend to earn storage space" mechanism. The key metric for this growth model is the viral coefficient (K-factor) — the average number of new users each existing user brings in. When K>1, the product enters exponential growth.

When users engage with Muse, they're not just experiencing an AI tool — they're completing a social act of "demonstrating AI's value to the people around them." This design cleverly embeds the product's growth path into users' social relationships — every successful "conversion" is an organic act of word-of-mouth marketing.

A Reality Check: Real Challenges Beyond the Gimmick

Of course, we need to stay rational. A catchy slogan alone isn't enough to judge a product's true value. An AI tool that markets itself on "persuasion" faces its biggest test in precisely this question: Does it actually have the functionality to convince skeptics?

There are several risks that cannot be ignored:

• Expectation gap risk: If the product experience is all style and no substance, the "persuasion" positioning could backfire — users bring high expectations to their demonstrations, only to end up disappointed, and negative word-of-mouth spreads just as fast.
• Insufficient scenario validation: True utility must be repeatedly validated in concrete, real-life scenarios — it can't stay at the marketing level.
• Higher tolerance for error: Targeting "non-believers" means that once these users have a bad experience, they're often nearly impossible to win back. Their first impression of AI may become permanently fixed.

Regarding that last point, the "primacy effect" in cognitive psychology offers a precise explanation. A Microsoft Research study found that users form their basic judgment of an AI tool's capabilities within the first 3-5 minutes of use, and this judgment is remarkably sticky — even if subsequent versions improve dramatically, the probability of a previously disappointed user trying again is less than 15%. This means Muse gets only one chance to prove AI's value to skeptics, and the window for that chance may be just a few minutes. This places extraordinarily demanding requirements on the first-use experience design.

Therefore, whether Muse can deliver on its promise ultimately comes down to the product's actual capabilities.

Conclusion

The emergence of Muse is less about the launch of a specific product and more a microcosm of a broader shift in AI application thinking. As the industry moves from "showing off how amazing AI is" to "making AI useful for everyone," the starting point of product design must inevitably shift from technological showmanship to user value.

"Convince the people you love that AI is useful" — this seemingly playful positioning actually articulates the hurdle that AI must clear to achieve true mass adoption. The ultimate value of technology lies not in how advanced it is, but in whether the most ordinary person, in the most ordinary moments of life, can genuinely feel helped by it.

Whoever can achieve that wins the mass market of the AI era.