What would you do if you were told the problem you are working on will NEVER make money?
"Data portability doesn't make money."
I heard this for years - from market leaders, from VCs, from people I respected.
"This is a regulatory problem, not a technical one." "This is a feature, not a product." "People don't pay for idealistic things."
<<Back story>>
In 2019, my team and I went deep into Self-Sovereign Identity: wrote research papers, ran experiments, and found ourselves at the intersection of data, identity, and web3. Right at that intersection lay data portability and sovereignty: the ability to own your data and take it anywhere on the internet.
This idea wouldn't let us go. No matter how hard we tried to move on, we kept coming back.
We pivoted through doubt, market pressure, and plenty of moments where the easier path was to drop the idea entirely. We thought about leaving this vision and building something else multiple times, but just couldn't. It felt like this problem mattered.
We went through multiple iterations: data plugs, smart profiles, open context layer. But the core conviction never changed: your digital data should be like a USB stick, pluggable into whichever platform you connect to, sharing only what you allow.
Because we believed the infrastructure question — who controls your context, and can you move it freely? — would eventually become impossible to ignore.
That moment is now. AI systems are eating every workflow, and context lock-in is the new data lock-in.
So we built AI Context Flow: the open context layer for AI systems.
Move from Claude to Gemini to Codex to OpenClaw to LMStudio to TypingMind to literally any website on the internet. Your context stays with you, reusable on every platform, and grows with you as you browse through the internet or from within AI Chats.
Proud of the path. Even prouder of staying on it. With a slightly deranged and obsessed team.
What's a conviction you held onto that the market eventually caught up to?
Replies
Really interesting story. A lot of people give up on ideas when they hear "there's no market for it," but sometimes timing is the real issue. Glad you stuck with it long enough to see the shift happen.
AI Context Flow
@yahya_rogers yeah, wasn't really sure if the market would shift but I'm not complaining that it did :D We just thought we'd build something and open source it if nobody pays for it. But we wanted to put this alternative out there. Data silos and broad permissioning have done nothing but damage to the internet.
@yahya_rogers Totally agree. Many of the successful business founders of today pushed through the negativity because the saw the vision. On timing, founders are more often or not EARLY meaning others might not agree with the approach or product usefulness on the first pitch
AI Context Flow
That you don't need a traditional CS degree to build robust infrastructure. Spent years being told you can't build 'real' software without writing raw syntax line-by-line, but the shift toward natural language logic and autonomous backends proved that clear thinking matters way more than fighting semicolons.
AI Context Flow
@bellamynina698 times sure are changing. I think we will see a similar shift with "you can't vibe code production level apps" in a couple years.
Serand
Have you considered letting users visualize where their context is being shared and reused? A simple map or timeline could make the concept even easier for new users to understand.
AI Context Flow
@maali_baali this is an interesting one... we did talk about letting users know how many times they have used their context in a week and how much time they have saved..
what do you have in mind? a graph like visualization or something else? super curious to know
This resonates.
I don't know yet whether the market will fully catch up, but one conviction I've held onto is that interview preparation should feel like a real conversation, not a questionnaire.
For a long time, most tools focused on generating questions and answers.
I've always felt the harder problem was simulating recruiter behavior: interruptions, skepticism, follow-ups, and the emotional side of evaluation.
Maybe I'm early. Maybe I'm wrong.
But some ideas are difficult to let go of once you've seen the problem clearly.
AI Context Flow
One conviction I've held onto is that learners and professionals should own their development records rather than having them trapped inside institutional systems. It wasn't always viewed as a business opportunity, but portability and ownership seem increasingly important in an AI-driven world.
AI Context Flow
@hira_siddiqui1 That's great to hear. I've been exploring similar questions around learner assessment and professional development records. It feels like we're moving toward a future where people expect to carry their evidence, competencies, and context with them rather than leaving them behind when they change institutions or employers.
Honestly, I'd probably keep going but get more strategic about the packaging. "Data portability doesn't make money" is almost always a timing problem disguised as a product problem. The conviction matters, but so does surviving long enough to be right. What I find interesting about your story is the pivot from SSI to AI context portability, that's not a pivot away from the vision, it's the same idea just finally landing in the right moment.
Context lock-in in AI systems is the exact same fight as data lock-in, just with a much bigger addressable problem now. What does the commercial model look like at this point? Still figuring it out or is there a path that's starting to work?
AI Context Flow
@hira_siddiqui1 It's great to hear that, as long as the model is doing good then you can make money gradually
I'd keep building anyway — but I'd change what I was optimizing for. The honest answer is that 'will never make money' is almost always a distribution problem, not a product problem. Nav-vera operates in a space where the problem is very real — retail operators lose sales every day because customers can't find products. If that were declared 'never profitable' I'd find a different model: licensing the tech, white-labeling it, or going open source for adoption. The problem worth solving usually finds a business model eventually.
Same conviction, different vertical. We heard “broker import is a feature” for years—now context lock-in in wealth-tech means warehousing itemized ledgers just to run AI. We stayed on portable, bounded context: parse at the edge (MIT OSS importer), compress to a fixed aggregate, stateless inference—your USB stick, but for portfolio signal, not chat logs. Proud you kept going on context portability; we’re doing the regulated slice. 🙌
AI Context Flow
I’d keep building, but I’d change what I was selling. The problem is rarely the problem — it’s the packaging. ‘Relationship intelligence’ might not make money. But ‘the tool couples use before therapy’ might.
AI Context Flow
@dani_mashael exactly, like for us it was not "data portability", it was "stop repeating yourself to AI in every new conversation and save 5+ hours a week".
I'm more interested to read the replies here rather than to add anything further.