ep. 99. This is AI’s dial-up moment. What kind of internet do you want to build?
A Conversation with Elena Yunusov
Elena Yunusov is on a mission to move the AI industry from marveling at raw technical “fire” to making meaningful “soup” that nourishes human flourishing.
She’s the founder and executive director of Human Feedback Foundation (HFF), a Toronto-based nonprofit that prototypes more open, human-centered futures for AI.
✨ A few highlights from HFF’s work include running AI Tinkerers, Canada’s largest AI/ML builders community with 5,300+ members; supporting 200+ students building open-source AI projects with experienced mentor-practitioners; and helping 500+ nonprofits responsibly and effectively adopt AI.
Before founding HFF, Elena’s career journey included leading the responsible AI strategy at RBC, Canada’s largest bank; building communities through initiatives like Crypto Camp and Maker Festival; and working as a journalist focused on technology and art. Her roles are connected by a throughline of steering bleeding-edge technology toward public benefit and preserving human agency.
The Cathedral & The Bazaar
Elena has had a front-row seat to several disruptive technologies, from mobile, to social media, to crypto. When I asked where she situates AI, she named one comparable moment: the rise of the internet, matched in both promise and risk.
“I remember what life was like before: the chunky dial-up connection, the open web, the dreams of the cathedral and the bazaar. I remember the kind of optimism I had about technology literally improving my life and bringing people closer. Looking at the reality now, this is a watershed moment in the exact same way. We have so much more experience to learn from, but I worry we’ll repeat the same mistakes, only now at a new scale.”
Elena is concerned that AI will repeat the internet’s path by hardening into—in the words of HFF Chief Technologist Eric Boyd—a “corporate singularity” where power and your personal data are concentrated in the hands of a few elite organizations. One of the reasons she founded HFF was to intentionally prototype a different future for AI.
The Case for Open-Source AI
To Elena, a key path to preventing the corporate singularity is open-source AI, decentralizing control of generative models.
“Open source was the bad guy in the news, where criminals and cybersecurity threats come from. Meanwhile, closed models—as capable and magical as they were—remained concentrated in very few hands. You can talk about alignment all day, but the sheer ability to manipulate people by virtue of knowing more than their search history about everybody, all the metadata, the geography, financial information—it’s Know Your Customer-level. Goodbye privacy.”
Because of that, HFF advocates open source, community solutions, and human-centered AI.
She notes how, despite years of human-centered design work starting with IDEO, it feels like we have lost sight of those lessons. Elena wants to bring that wisdom back into the fold.
“If technology—AI or no AI—isn’t contributing to human flourishing, then what are we doing?”
On Geopolitical Sovereignty
Prime Minister Mark Carney recently unveiled Canada’s National Artificial Intelligence Strategy: “AI for All”, which promotes “advancing open-source AI for resilience and choice”. Because of that, I was curious to hear Elena’s perspective on the new strategy.
To her, “AI for All” is one example of how a country is addressing urgent global questions regarding technological sovereignty for human benefit. For any country to maintain that technological sovereignty, investing in open source is a critical geopolitical and economic imperative.
Participating in the creation of new open-source protocols and governance mechanisms ensures that a society has a say about how good its technology will be, rather than just being a passive consumer of what the “corporate cathedrals” offer. Relatedly, open source frees you from reliance on a small number of organizations.
There’s another practical reason why Elena thinks open source will win out over closed models: compute is a finite resource.
“People in the AI Tinkerers community have thought this through: what’s the maximum compute achievable if we turned planet Earth into a data center? Then build a few data centers on Mars and the Moon? It’s still a finite number. That leads to the natural conclusion that we won’t use frontier models for everything; we’ll need to diversify. We can use the frontier models now to bank the window and build the open-source alternatives faster.”
Shifting from Compliance to Benefit
For Elena, open-source AI also provides the decentralized agency required for human-centered design to function as a meaningful industry anchor.
She believes that by anchoring technology in the human experience, the industry can restore “Responsible AI” to its ethical roots, rather than the corporate compliance checklist it has often become in the race to commercialize.
She argues that for AI to remain “socially allowed” to exist, it must earn a “social license” by being provably beneficial to human flourishing rather than just safe.
“Normal people don’t use the words “responsible AI”. Instead, they evaluate technology based on a simpler binary: is it good AI or bad AI?”
By prioritizing utility over a “build fast, break things” mentality, practitioners can create products that solve real problems instead of adding to daily frustrations.
“Good AI has to be human-centered AI or else it’s tobacco all over again. The social license will evaporate. It’s not enough for things to be safe, they also need to be beneficial.”
The Evolution of the AI Builder
Elena is already seeing signs of the shift toward the prioritization of building useful AI experiences in the builder community.
Through her frontline view of the 5,300-person AI Tinkerers community, she has seen what was once novelty-driven fascination with raw capability turn into disciplined focus on creating real-world value:
“It’s not ‘look at this cool thing, what can I make it do?’ It’s ‘how do I make it do the thing I want?’ How do I make soup with the fire, not just look at the properties of the fire.”
This evolution has triggered a complex identity crisis within the industry:
Some veteran builders mourn the erosion of craft and loss of authorship as traditional coding becomes commoditized. These are often the people building the infrastructure where reliability is non-optional. They see vibe coding as a source of significant technical debt and potential system failure.
Other builders are pivoting toward the challenges of architecture and orchestration. These are usually developers pushing the boundaries of AI, willing to accept high risks for the sake of progress.
Ultimately, the industry will need developers from a range of backgrounds to build robust, useful AI systems that move the field forward. That said, Elena feels that people from the reliability camp deserve more recognition, as they play a critical role in the technology’s future:
“If AI is how we build technology, we need to find ways to design critical infrastructure with AI. That takes time. The stakes are higher.”
The AI Hype Check
I ask every practitioner I interview the same question: is AI overhyped or underhyped?
“Easy—it’s underhyped, for sure.”
That’s because the average person hasn’t yet felt a significant shift in daily existence comparable to the printing press or the internet.
People may mistakenly view AI as just another technological wave like social media or mobile, failing to realize that the ground underneath them is fundamentally shifting.
For Elena, this creates a critical window of urgency:
“The foundational patterns of the AI era won’t emerge organically; they must be intentionally prototyped now to ensure we don’t end up in a future we might not like living in. The current state of AI is like a bunch of atoms that need to become solid matter. Now is the time to shape them.”
Given the sheer number of opportunities to shape AI, a relatively small organization like HFF needs prioritization criteria. Elena describes the one question that guides her team’s decision-making: What’s the no-regrets bet?
“I will not have regrets by helping nonprofits adopt AI. Even if my success rate is 55%, no regrets, guaranteed. I will not have regrets by exposing computer science students to open source and human-centered ways of building. I’ll have no regrets by supporting artists exploring what AI means for society.”
What if we all started taking more no-regrets bets?
This interview has been edited for length and clarity.
Three Things to Try This Week
Here are three takeaways from Elena’s insights that you can action on this week:
Test each AI feature against the social license bar. Do the people you’re building for call your product “good AI” or “bad AI” based on their day-to-day experience with it? If your team cannot answer confidently, you have a research or design gap to close before shipping.
Lead your specs with the “soup”, not the “fire”. Rewrite your next AI feature spec so it opens with the human outcome, not the technology capability.
Prototype one optimistic future your team would defend. Write down one future you want your product to make possible, then work backwards to name the step your team could take toward it this sprint. These incremental decisions are what will give shape to the “AI atoms” still taking form.
👋 Know a practitioner navigating AI in their work who should be featured here? Reach out to hello@sendfull.com
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Can AI Make Scientific Breakthroughs? by Iulia Georgescu
TLDR: While AI excels at processing formalized scientific literature, it remains unable to produce major breakthroughs. That’s because it lacks access to tacit knowledge, the unrecorded, experience-based deep craft and social understanding gained through trial and error that are essential to the creative process of discovery.
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Across my book, Substack, and recent QRCA keynote, I argue: Tacit knowledge, embodiment, and situated action comprise the durable human core that resists automation. Because of that, we’ll always need to be in or on the loop, more than the AI hype suggests. The goal continues to be joint optimization between humans and machines, not replacement.
Shout out to Michael Anton Dila for putting this one on my radar.
That’s a wrap 🌯 . More on UX, automation, and strategy from Sendfull in two weeks!
Hey,
thanks so much for this article.
Really interesting read. I love the things to try section, I'd love AI to help us save the planet, feels somehow too much of a goal to break it down into smaller tasks but I will think about it more!