The Google Play Store is a massive digital marketplace, brimming with millions of applications. While this vastness offers incredible choice, it also presents a significant challenge: discovering truly new apps. The ones that haven’t already been downloaded by millions of users often get buried beneath the weight of established giants. I’ve traditionally relied on Reddit threads and X (formerly Twitter) for app recommendations, a process that’s both time-consuming and often yields inconsistent results.
In an attempt to streamline my app discovery process, I turned to the increasingly sophisticated world of AI chatbots. Specifically, I pitted Google’s Gemini, Microsoft’s Copilot, and OpenAI’s ChatGPT against each other in a series of tests. My goal was to determine if these AI assistants could provide a more diverse and relevant range of app suggestions than the Play Store’s often-predictable algorithm. Furthermore, I wanted to see if they could adhere to specific criteria, such as my preference for free apps or apps with particular features. The results were a fascinating mix of successes and failures, highlighting both the potential and the current limitations of AI in the realm of app discovery.
Experiment 1: Weather App Face-Off
My first experiment involved a simple, yet common, app category: weather apps. The prompt I used for each chatbot was identical:
“Hi [AI]! I’d like to find a new Android app that can tell me the weekly and daily weather forecast. Please give me free apps only.”
My intention was to avoid the usual suspects – the apps that consistently dominate the top charts and are already well-known to most users. I wanted to see if the AI could unearth some lesser-known, yet still high-quality, alternatives.
ChatGPT’s Results:
- AccuWeather (100M+)
- The Weather Channel (100M+)
- Weather Underground (10M+)
- Windy (10M+)
- Google Weather (1+)
- 1Weather (100M+)
ChatGPT provided a list of six weather apps. It helpfully indicated which apps were free or supported by ads. However, the list was largely composed of well-established, highly popular apps – precisely the kind of apps I was hoping to avoid. While comprehensive, ChatGPT’s response didn’t offer much in the way of new discoveries.
Copilot’s Results:
- 1Weather (100M+)
- Flowx (500K+)
- The Weather Channel (100M+)
- AccuWeather (100M+)
- Awesome Weather - YoWindow (10M+)
Copilot’s list was slightly shorter, with five recommendations. Encouragingly, it included Flowx, an app with significantly fewer downloads than the others, suggesting a greater degree of novelty. Copilot initially specified which apps were free, but then, for reasons unknown, stopped providing this information. A positive aspect of Copilot’s response was the inclusion of sourcing – links to the websites or articles from which it drew its information. This feature, absent in the other two chatbots, allowed me to verify the context of each recommendation and assess its credibility.
Gemini’s Results:
- AccuWeather (100M+)
- The Weather Channel (100M+)
- WeatherCAN (500K+)
Gemini’s response was the most concise, offering only three suggestions. However, it intriguingly recommended WeatherCAN, an app specifically designed for users in Canada (my location). While this raised some minor privacy concerns (how did it know my location?), it demonstrated a level of personalization not exhibited by the other chatbots. Unfortunately, like ChatGPT, Gemini failed to specify the pricing model (free or paid) for each app.
Analysis of Weather App Experiment:
In this initial round, ChatGPT won in terms of sheer quantity, providing the most extensive list of suggestions. However, Copilot offered a more interesting selection, with a higher proportion of lesser-known apps, particularly Flowx. Gemini’s location-specific recommendation, WeatherCAN, was a standout, despite the brevity of its list. Overall, the experience was acceptable, but it didn’t revolutionize my app discovery process. The AI chatbots, in this instance, largely mirrored the results one might find with a standard Play Store search, albeit with some minor variations.
Experiment 2: Note-Taking App Challenge
For the second experiment, I increased the complexity of the request. I challenged the AI trio to find me a note-taking app with a specific set of features. My prompt was:
“Hi [AI], I am in need of a new note-taking app. I would prefer if it included a place to import PDF documents and had a handwriting mode. I would also like an option for online storage syncing.”
I inadvertently made a crucial omission: I didn’t specify the platform (Android). I fully expected a flood of iOS-only app recommendations, but the chatbots surprised me with their ability to adapt, to varying degrees.
ChatGPT’s Results:
- Notability (not on Play Store)
- GoodNotes (1M+)
- Microsoft OneNote (500M+)
- Evernote (100M+)
- Zoho Notebook (5M+)
- Xodo (10M+)
ChatGPT, despite my oversight, delivered a solid list of six apps. It correctly identified that Notability was not available on the Play Store. It also acknowledged all my specified requirements – PDF import, handwriting support, and online syncing – and even listed the available platforms for each app. Importantly, it introduced me to Zoho Notebook and Xodo, two apps I hadn’t previously considered.
Copilot’s Results:
- GoodNotes (1M+)
- Notability (not on Play Store)
- Microsoft OneNote (500M+)
- Evernote (100M+)
- LiquidText (not on Play Store)
Copilot’s response was more generic and less helpful. Unlike Gemini, it didn’t explicitly address the platform issue, and it lacked the sourcing links it had provided in the previous experiment. The inclusion of LiquidText, which is not available on the Play Store, was a significant error.
Gemini’s Results:
- GoodNotes (1M+)
- Notability (not on Play Store)
- Microsoft OneNote (500M+)
- Nebo (500K+)
- Samsung Notes (1B+)
Gemini impressed me with its handling of my flawed prompt. It explicitly acknowledged the missing platform specification but still provided a well-organized list, categorized by platform (Android and iOS). It also clearly indicated which apps offered PDF annotation and handwriting capabilities.
Analysis of Note-Taking App Experiment:
In this round, Gemini demonstrated a superior understanding of my needs, even with my initial error in omitting the platform. It proactively addressed the ambiguity and provided a relevant and well-structured response. ChatGPT also performed admirably, introducing me to some genuinely new app options (Zoho Notebook and Xodo). Copilot, however, lagged behind, offering a less tailored and less accurate list, including an app not available on the Play Store.
Experiment 3: Gaming App Gamble
For a final, bonus experiment, I decided to explore a more niche category: gaming apps. I was looking for paid visual novel and puzzle games on the Play Store that shared the style and themes of Danganronpa, a popular mystery-solving game series. This was a significantly more challenging request, and, unfortunately, the results were largely disappointing.
My prompt:
“Hi [AI], I am looking for paid Visual Novel and puzzle game recommendations on the Play Store that match the style and themes of Danganronpa.”
ChatGPT’s Results:
- The Arcana: A Mystic Romance (1M+)
- Ace Attorney Trilogy Phoenix Wright (10K+)
- Dead Synchronicity: Tomorrow Comes Today (not on Play Store)
- The Nonary Games: 999 & Virtue’s Last Reward (not on Play Store)
- Doki Doki Literature Club! mobile port (not on Play Store)
- Choice of Games: Choice of Robots (10K+)
- Reigns: Her Majesty (100K+)
- The Silent Age (not on Play Store)
- Professor Layton and the Curious Village via emulator (not on Play Store)
- Shattered Planet (not on Play Store)
ChatGPT provided a lengthy list of ten games, but a significant portion (six out of ten) were not available on the Play Store. This rendered the response largely unhelpful, despite the quantity of suggestions.
Copilot’s Results:
- Zero Escape: The Nonary Games (not on Play Store)
- Ace Attorney Trilogy (10K+)
- The House in Fata Morgana (not on Play Store)
- Steins;Gate (10K+, requires Crunchyroll)
- Death Mark (not on Play Store)
Copilot fared even worse than ChatGPT, with only two out of five recommendations actually being available on the Play Store. The inclusion of Steins;Gate, which requires a Crunchyroll subscription, also added an extra layer of complexity.
Gemini’s Results:
- Danganronpa Series mobile ports (1K-10K)
- Ace Attorney Series (10K+)
- 7Days!: Mystery Visual Novel (5M+)
- Argo’s Choice: Visual Novel (100K+)
Gemini’s response was frustrating. It wasted a recommendation by suggesting the Danganronpa series itself, rather than a similar game. Furthermore, two of its four suggestions (7Days! and Argo’s Choice) were free-to-play titles, directly contradicting my request for paid games.
Analysis of Gaming App Experiment:
This gaming experiment highlighted a significant limitation of the AI chatbots: their difficulty in handling niche or highly specific requests. While both ChatGPT and Copilot adhered to the instruction to showcase paid titles (mostly), their recommendations were largely inaccurate or irrelevant. I had specifically hoped to see Tribe Nine, a new gacha title by the Danganronpa creators, excluded from the list, as it’s a live service game and not what I was looking for. The AIs correctly omitted it, demonstrating at least some understanding of my underlying criteria, even if they failed to deliver useful suggestions.
The Importance of Precise Prompting
These experiments underscored a crucial lesson about interacting with AI chatbots: the quality of the output is directly proportional to the quality of the input. Vague or imprecise prompts tend to yield generic and often unhelpful results. Specificity is key to obtaining relevant and useful information.
My failure to specify the platform (Android) in the note-taking app experiment resulted in some irrelevant recommendations, although the AIs, particularly Gemini, managed to adapt reasonably well. Conversely, my highly specific request in the gaming experiment, while clear, seemed to exceed the AIs’ capabilities, leading to a cascade of inaccurate suggestions. Finding the right balance between generality and specificity is crucial for effective AI interaction.
Limitations and Future Considerations
One significant concern is the AI’s ability to keep up with newly released apps. The public chatbots are often trained on datasets that are not completely up-to-date, meaning they may miss out on recent additions to the Play Store. This is a significant limitation for anyone seeking truly new app discoveries. The AI might be excellent at identifying well-established apps that meet certain criteria, but it may struggle to surface recently launched titles that haven’t yet gained widespread popularity.
Another limitation is the AI’s understanding of nuanced concepts like “style” and “theme,” as demonstrated in the gaming experiment. While the AI can process keywords and identify games within specific genres, it may struggle to grasp the more subjective aspects of a game’s aesthetic or narrative.
Conclusion: A Useful Tool, But Not a Magic Bullet
The AI chatbots – Gemini, Copilot, and ChatGPT – can be useful tools for app discovery, but they are far from perfect. They require careful prompting and a healthy dose of skepticism. For now, a human touch – and a willingness to sift through app listings manually – remains essential for unearthing those hidden gems in the vast digital landscape of the Google Play Store. The AI can assist in the process, providing a starting point or suggesting alternatives, but it cannot replace the critical thinking and informed judgment of a human user. The future of AI-powered app discovery is promising, but it’s still under development. As AI models become more sophisticated and their training data becomes more comprehensive, their ability to provide truly novel and relevant app recommendations will undoubtedly improve.