The future of AI lives in your pocket, not the cloud.
While everyone’s throwing billions at bigger AI models, I just figured out how to squeeze expertise from tiny open-source models that’ll run on your phone.
We are mesmerized by the AIs with the largest investment. OpenAI & Anthropic are billions in debt, burning through cash for the benefit of humankind(?) and we’re all impatiently waiting for the next big breakthrough, like Ghibli-fication.
These big money operations are indeed creating genuine genius that can design better systems, but that only goes so far. Here’s the reality: genius does not implement the designs. The architect does not build the building. Mechanical engineers do not mass produce the gadget. Genius doesn’t deliver invention to the people.
The gears were conceptualized by a genius, but it ain’t genius turning them day-in & day-out.
We don’t need a trillion parameter AI burning kilowatts per prompt to help me figure out what to have for lunch. We need that computing power to deduce medical breakthroughs; not to figure out the word on the tip of my tongue.
For many uses, you don’t need a cutting edge model running on some distant server bank. All you need is a tiny ‘AI’ running privately on your phone, that knows what you normally like to have for lunch and has access to a thesaurus.
I just witnessed the potential of a tech combo that was so compelling, I promptly shifted the scope of my project and started seeking a co-founder.
What I discovered is this: the biggest thing in AI, is NOT the biggest AI.
My Coach’s Peanut-Butter Cup
My high school soccer coach would claim his father invented the Reese’s Peanut Butter Cup in their basement in New York City when he was growing up. For a time, he said, he was the only kid on the planet with access to an ever improving supply of Reese’s Peanut Butter Cups. I’m starting to feel like that kid.
Two simple ingredients. Chocolate and peanut butter. Neither revolutionary on their own, but put them together in the right mix and you create something that’s miraculous. A combination so perfect it seems obvious in hindsight, yet took someone to actually do it first.
That’s exactly what I discovered about the AI stack that’s about to become the new app standard.
The ironically huge secret is this: I learned how to make a tiny language model smart. Not by making it bigger, but by giving it the right ingredient. Two elements that aren’t new, but served together just right, create something that feels like magic.
Like chocolate & peanut butter.
The AI Peanut Butter Cup
Here’s what the real stack of the future looks like:
The Chocolate: Small Language Models (SLM), distilled from the large genius versions (LLM), that can run on lithium.
The Peanut Butter: The boring embedded databases Google’s been using for decades for near instant searches.
The Recipe: Private, offline orchestration that makes 1+1=10
No cloud dependency. No privacy concerns. No internet required.
All it takes is a cleverly orchestrated RAG database and a pocket sized AI, to completely invert how we think about AI deployment. Instead of your phone being a terminal connecting to smart AI servers, your phone becomes the smart hub that occasionally syncs with the world.
Why does this matter?
Because this 3-layer architecture beats cloud AI in metrics that matters to real people: speed, privacy, reliability, and cost. Your phone becomes a personal AI ecosystem that knows you, adapts to you, and works for you whether you’re online or in a dead zone.
The combination makes small models smart enough to replace much of your daily cloud dependency.
The Genius Caveman Principle
A genius caveman could never build a plane. There’s too many supporting technologies to figure out for one lifetime. But any fool today can jet around the world.
Your capability depends more on the tools you have access to then your raw intelligence.
Genius often flounders, while the mediocre among us, who have solid resources, thrive. It’s not about raw computing power: it’s about having the right resources at the right moment.
This is exactly how small language models, with the right tools, can be more powerful than genius LLMs. Tools and access matter more than pure processing power. A 1 Billion parameter model with instant access to the right information can outperform a 1 Trillion parameter model that has to guess from its training data.
The genius caveman principle applies to AI just like it applies to people: being smart isn’t enough if you don’t have the right tools also.
The “I Don’t know” That Changed Everything
I’ve been building an AI-powered task management app. A side project that grew out of my strugglings with a digital mirror project; then I soured on hardware-dependency altogether and tripled my effort on the app.
I tested Google AI Edge Gallery (released in late May with a whisper) thinking, a little on-device logic could save a lot in API calls. This new open source Google product (that is free to branch & commercialize) is like LM-
Studio for the Android; can run small language models on-device.
If you’ve ever played with them, you’d know these small language models are not the brightest. They’re like those types who are friendly enough, but you’re not going to hire them for indoor-work.
Equipped with the latest in prompt engineering for consistent accuracy, I conducted some experiments. I paired a vector database project with Edge Gallery to pipe in a basic RAG system to the little language models, sideload it onto my extra Galaxy S, and took it for a spin.
I uploaded Oren Klaff’s “Flip the Script”. The book embedded in seconds. The response was as fast as GPT-4o.
Instead of the hallucinated-gibberish one can often expect from a cell phone sized AI with a complex prompt, it explains to me how norepinephrine & dopamine drive the tension & desire that keeps your audience engaged during a presentation.
No fancy orchestration even. A simple semantic search, feeding some context into a ‘dumb’ AI, and BAM! Accuracy in milliamps..!
Even more impressive, I asked “how do I fix my bike chain”, and it said, “I don’t know.”
Ever heard an AI plainly declare it’s ignorance, correctly? I’m so impressed!
That moment, I back-burnered the task manager to build something much bigger.
Instead of building one app fully dependent on AI via an API, I’ve decided to build the pipeline builder to help create families of apps, leveraging on-device AI. No more API fees, concerns of outages, or questionable privacy behaviors.
The Vision: Families of apps leveraging on-device SLM/RAG orchestrations, each focused on specific use cases.
The Apps: An offline AI-powered smorgasbord: auto-personalizing motivational taskmaster, a private journal that talks to you like a friend, the text auto-responder that always fools your friends, an offline expert in any field you need, etc, etc…
The Strategy: Leverage small language models & RAG databases with well orchestrated pipelines to create the future of app features. MVP the foundational apps, then niche different versions for different market segments.
Breaking the Solo Pursuit
The only reason I’m sharing this, is that I’m ready to admit, there’s too much here for me to handle alone.
I’m having successes with these cutting-edge tools I hear very few talking about. I should be focused on pushing the bar of on-device AI, not fretting about my social media account for launch day.
I might be early figuring out the future of AI, but the formula for social (media) success is still a mystery to me.
I’m looking for someone focused on the distribution side to help me turn this technical breakthrough into a business breakthrough. The co-founder I need is a marketing master who gets the vision: a world where your phone becomes your personal, off-line AI ecosystem.
I am looking to build a ‘small’ bootstrapped company around this stack. If you know how to get the latest tech in the customer’s hands, let’s connect.