Problem Aware, Solution Lost: Navigating Bittensor's Awareness Stages
Why 95% of Bittensor Beginners Never Make It to Stage 5
I'm back from a week off and about as fresh as a man can be after a week minding small children. I don't want to go down another padded dinosaur slide until at least the next daycare birthday party. So back to it...
In a previous issue, I compared Bittensor's UX to handing my toddler a Toy Story coloring book without showing him the movie.
This time, I'm using a proven marketing framework, Eugene Schwartz's 5 stages of awareness, to diagnose exactly where beginners get stuck in the Bittensor ecosystem.
Schwartz's framework, originally designed for marketing, perfectly describes the psychological journey from "What is this?" to "I can contribute value here."
By mapping this to Bittensor, let's identify precise pain points and create targeted solutions.
The 5-Stage Beginner Journey Map
Stage 1: Problem Unaware
"I love Chat GPT! Why would I want anything different?"
Let's take an example of AI privacy concerns, something close to my heart. I've already listened to friends tell me how they casually share sensitive family health information with an AI chatbot in the hope it will turn up something useful.
They are completely unaware this data is being stored, analyzed and likely used to train proprietary models.
"It doesn't care about me, it's just an AI" they'd say. But this data has now been captured and can be used as training data that might be sold to insurance companies in future.
The risks of centralized AI such as privacy concerns and corporate control have not been exposed to the masses and most never even reach Bittensor because they don't perceive a problem with the status quo.
Most subnet landing pages assume users already understand why decentralized AI matters, skipping this critical awareness stage entirely.
This might be the hardest stage to crack, as people don't see a problem to solve.
The best way to get through this is to give them exposure to the problem and offer resources to understand it themselves, like my issue on Bittensor's critics.
Stage 2: Problem Aware
"I see the risks of centralized AI, but what's the alternative?"
Continuing with the AI privacy theme, some more savvy individuals now try to avoid sharing sensitive details with AI chatbots after finding out about data collection practices.
Like my Mom who wasn't far off uploading her hospital scans to Chat GPT for more explanations.
She's since switched to incognito mode and uses less personal info but still feels trapped as the tools are too helpful to give up completely.
What is needed here is a clear explanation of alternatives and how they address their specific concerns: decentralized AI lets you keep control of your data while still getting helpful responses.
You can now sign up for Chutes and use their new Chat feature (with Auto model selection) and use Open Source models that are very close in performance levels to many of their closed alternatives.
Subnets often dive straight into technical details without connecting to the user's pain points. This can be addressed better as the Bittensor ecosystem grows and evolves.
To make it more friendly to non-developers they can also build user-friendly UIs (as I have previously discussed), hiding complex token mechanics, and letting people pay with fiat or through Apple/Google Pay.
Stage 3: Solution Aware
"Decentralized AI sounds good, but how does Bittensor specifically help?"
Lets say your friend attends a tech meetup recently where someone mentioned Bittensor as a privacy solution. Now he asks things like, is this like running my own AI model locally? Or is it a marketplace? How does it actually keep my data private compared to other options?
Bittensor isn't just open source models- it's a network where AI models compete to serve you while keeping your data private through distributed validation.
Understanding decentralized AI as a concept is different to grasping Bittensor's specific value proposition or how it differs from other projects.
A clear explanation is needed here of Bittensor's market-based approach to AI development and how it creates value.
Most subnet sites assume you already understand Bittensor's core concepts like tensors, validators, and miners.
A practical tip for beginners here is to read the Bittensor whitepaper summary I created in Issue 2 and follow some of the growing band of thought leaders on X trying to TAO-pill newcomers daily.
Stage 4: Product Aware
"I get Bittensor, but how do I actually participate?"
The user understands Bittensor creates a decentralized network where no single entity controls the AI. But how do they actually interact with it? What's their role as a non-technical user? Where's the 'private mode' button?
The issue here is the beginner sees the technology but not their place in it.
The valley of despair between understanding the concept and taking first action needs a path to contribution that matches their skills.
Here a good idea is to identify your existing skills (e.g. writing, basic Python) and search for "Bittensor for [your skill]".
Most subnets present a single onboarding path (usually technical) rather than multiple entry points for different skill levels.
I've referenced a 4-layer funnel in a past issue that applies here as mining is not the default or only path to contribution here.
Stage 5: Most Aware
"I know how to make my first contribution—let's do this!"
"I'll use Subnet 42 with privacy parameters enabled for everyday queries, contribute to data cleaning to improve privacy standards, and track how my participation helps strengthen the network's privacy guarantees."
If you're at this stage, you're well past my level and I salute you.
The participant here understands exactly how to make their first contribution and what to expect from the experience.
Clear expectations are needed here, safety nets, and immediate positive reinforcement for first actions.
A practical tip here is to make your first contribution in a low-stakes environment (like documentation fixes) before attempting anything with financial implications.
Then continue on the path to success, you absolute legend.
Why This Framework Matters
This isn't just academic, it explains exactly why so many beginners bounce from Bittensor.
Most subnet landing pages speak to people at Stage 4 or 5, but 95% of visitors are at Stage 1, 2, or 3.
I've fallen into this trap too.
When I tried to explain Bittensor to colleagues, I made the classic mistake of starting at Stage 4: (crypto-heavy, high level AI chat).
I got skeptical looks from the crypto references and glazed eyes talking anything bigger than Chat GPT and AI Agents.
I needed to start more basic and assess the stage of awareness of my audience.
Practical Applications
For Beginners:
Diagnose your current stage using this framework (if you're already interested, you're likely a 3-4).
Seek resources specifically designed for your awareness stage.
Don't waste time on advanced materials until you've addressed your current stage's needs.
For Subnet Teams:
Create content specifically for each awareness stage (even if they're not your target audience. We all have a responsibility to promote the ecosystem to newcomers).
Map your landing page content to address Stage 2 and 3 concerns first.
Develop clear pathways from awareness to contribution.
For the Community:
When helping beginners, diagnose their awareness stage first.
Adjust your explanations to match their current understanding (usually advice for beginners is just- start learning Python. But this does not always apply).
Celebrate small wins at each stage of the journey.
Check Your Level
Here’s a simple 10 second check for your stage of awareness:
Which of these sounds most like you?
'AI is great!' (Stage 1)
'I'm worried but stuck' (Stage 2)
'I know decentralized AI exists but...' (Stage 3)
'I get it but how do I participate?' (Stage 4)
'I'm contributing right now!' (Stage 5)
Your answer determines exactly where to focus your Bittensor journey.
Meeting People Where They Are
The most successful technology adoptions don't force everyone to climb the same mountain. They build multiple paths to the summit.
Bittensor's future depends on recognizing that beginners aren't stupid for not understanding tensor weights, they're simply at a different stage of awareness.
By applying this framework, we can create subnet experiences that guide users through each stage rather than expecting them to leap from "What is AI?" to "Let me deploy a miner" in one bound.
Let me know what you think of this framework, and if I should expand on how to self-assess what stage we are at in this journey.
Until next week.
Cheers,
Brian