I've been thinking about what AI could mean for the world, not just what it can do for technology companies, but what it could do for humanity. The conclusion I keep reaching is both simple and radical: AI has the potential to help us overcome many of the socioeconomic and geographic inequalities we face, and to usher the world into a new age of abundance.
But that outcome isn't automatic. It depends on choices we make now about how AI gets built, who participates in building it, and how the benefits get distributed. The default path, where a few companies extract value from the work of many, leads to concentration, not distribution. Extraction, not empowerment.
This is the Avala Master Plan: a blueprint for AI infrastructure that creates genuine abundance, where the people who power AI development share in its success, and where the technology becomes a tool for human flourishing rather than displacement.
The core principle
The guiding framework behind everything we do is to maximize usefulness to humanity with every innovation. This sounds abstract, so let me make it concrete. When we evaluate a decision, about technology, operations, or partnerships, we ask: does this improve lives? Does it expand opportunities for real people, especially those traditionally left behind? Does it move us closer to a world where the benefits of AI are widely shared?
This isn't charity layered on top of a business. It's the business itself. We believe AI built with people at the center produces better outcomes, technically, commercially, and socially, than AI built by treating humans as disposable resources.
The vision that inspires us is a future of economic abundance for all: advanced AI and robotics eventually handling most of the work no one wants to do, freeing humans to pursue what matters to them while everyone has access to the essentials of a good life. That's the horizon we're building toward.
People-First AI
Rather than treating humans as "data labelers" in a black box, anonymous workers completing tasks with no stake in the outcome, we treat them as partners in a shared endeavor.
We call our workforce "coworkers" rather than contractors, and it isn't just terminology. It means paying fairly, often 2-3x what typical gig platforms pay for similar work. It means flexible work that pays for accepted output, with training and continuity on the projects a person knows rather than a new anonymous batch every day. It means paths from basic annotation into validation, technical leadership, and specialized expertise. And it means listening to the people doing the work about the tools and processes that shape it.
The result isn't just a happier workforce, though it is that. It's a more capable one. Coworkers who stay for years accumulate expertise new workers don't have: they understand the intent behind guidelines, not just the literal words, catch subtle issues less experienced annotators miss, and produce data that's more consistent, more nuanced, and more aligned with what our partners actually need. People-First AI isn't people-first despite what's good for the technology. It's people-first because of what's good for the technology. The ethics and the quality point in the same direction.
There's one more dimension. Our coworkers aren't just contributors: they're co-designers, bringing ideas to the table about what their communities need and what tools would help them work better. Automation isn't something done to people. It's something built with them.
Universal Basic Infrastructure
There's been a lot of discussion about universal basic income: a baseline payment to cover essential needs, regardless of employment. It's an important conversation, but we think a complementary idea deserves more attention: universal basic infrastructure. Instead of a check to cover basic needs, a platform that provides work, training, and income to anyone who wants to participate. A safety net of work rather than welfare.
This is what we're building at Avala. Through our platform, anyone with a smartphone can access the AI economy: learn new skills, contribute to meaningful work, and earn income, regardless of where they live or what credentials they have. We think of AI as infrastructure that should be as accessible as roads or electricity, and by distributing participation in AI development globally, we're creating pathways for people everywhere to benefit from the AI economy, not as an afterthought, but as a core part of how the technology gets built.
The four phases
Phase 1: Foundations. We're currently here, establishing the core platform that connects people worldwide to AI development work. Our Mission Control platform and mobile coworker app form what we call a "programmatic virtual assembly line," infrastructure that can coordinate AI operations across a distributed global workforce. The goal is to prove that the people-first model works: that treating coworkers well produces better data, that distributed participation is viable at scale, and that the flywheel of community involvement leading to better AI leading to more work leading to more community benefit actually spins.
Phase 2: Expansion. Having proven the model in data annotation, we expand the scope of work: model validation, edge-case discovery, domain-specific consulting, simulation training. The platform evolves from a data labeling tool into a comprehensive AI operating system where humans and AI collaborate across the full development lifecycle. We'll also develop our Neural Digital Twin technology: high-fidelity virtual replicas of real-world environments using techniques like 4D Gaussian Splatting that capture both space and time, so AI can learn from realistic motion and temporal patterns. Critically, we'll decentralize, partnering with local organizations in developing regions to build regional hubs and coworker cohorts, so communities become co-owners of their AI infrastructure rather than a source of extracted labor.
Phase 3: Integration. This is where the digital meets the real world. Using the AI systems trained through our people-in-the-loop pipeline, we'll work with robotics partners to deploy autonomous systems against real needs: labor shortages, essential services, quality of life. The key principle is that robots work alongside humans, not instead of them: local coworkers, trained through earlier phases, operate, monitor, and maintain these systems. This is when Universal Basic Infrastructure becomes tangible, with AI and robotics delivering healthcare, energy, and education at dramatically lower cost, reaching communities that traditional infrastructure never could.
Phase 4: Fulfillment. The long-term vision: advanced AI and robotics drive the marginal cost of essential goods and services low enough that basic human needs are met abundantly for all. Work evolves rather than disappears: tedious labor becomes optional, and humans are free to engage in creative, strategic, and community roles, with anyone still able to contribute through the platform and be rewarded for it. The emphasis is universal basic opportunity: tools to build the life you want. And governance is as distributed as the benefits: the communities served have a genuine stake in how these systems are deployed. This isn't utopian fantasy; it's a goal we're working backward from, making choices today that move us toward it rather than away.
We're realistic about the timeline. Phases 3 and 4 are years away, dependent on technology still developing across the industry. But a clear long-term vision shapes the decisions we make now: every choice about how we treat coworkers, structure our platform, and engage with partners is evaluated against this horizon.
The moats that matter
Building toward abundance requires capabilities that can sustain the mission over time.
The first moat is our community. The global coworker network is self-reinforcing: more coworkers, better AI, more partners, more work, more community benefit. The trust behind it takes years to build and compounds.
The second is our technology stack: an end-to-end platform spanning data ingestion, labeling, simulation, and deployment, with Neural Digital Twin fidelity that fragmented toolchains can't match.
The third is our data and knowledge. Our datasets aren't just large; they're continuously refined by human context and insight, yielding AI that is culturally competent and robust in ways static "big data" approaches can't achieve.
The fourth is trust. In an era of increasing scrutiny on AI, a track record of transparency and positive impact reduces friction with partners and regulators. Trust is earned slowly and lost quickly; we're building it deliberately.
The fifth is global reach with local footing. Presence in communities worldwide lets us operate in emerging markets and incorporate local context in ways centralized approaches can't. We're not extracting labor from the Global South; we're building partnerships with communities that have a stake in our success.
The world we're building toward
I want to be honest about the scope of what we're attempting. AI-enabled abundance, a world without poverty, where technology amplifies human capability rather than replacing it, is ambitious to the point of audacity. Many will reasonably be skeptical.
But I've seen what happens when you aim for something meaningful and work backward from it. At Tesla, I watched a team attempt something most of the auto industry said was impossible, building autonomous driving capabilities at scale, and make remarkable progress through sheer determination and first-principles thinking. Ambitious goals, pursued with discipline, yield results that incremental thinking never achieves.
Avala is an attempt to apply that approach to a different problem: ensuring that as AI becomes more powerful, its benefits are widely shared rather than concentrated. The default trajectory of technology tends toward concentration: wealth, power, and capability accumulating in fewer hands. Bending that trajectory toward distribution requires deliberate effort, sustained over years, guided by a clear vision of where we're trying to go.
That's what the Avala Master Plan is about: not just building a successful company, but building the kind of company that makes the world we want to live in more likely to exist. We're early. Phase 1 is still underway, and we'll face challenges we can't anticipate. But the direction is set, the principles are clear, and the team is committed.
If you share this vision, whether as a potential partner, a potential coworker, or simply someone who believes AI can be a force for broad human benefit, reach out. The world we're building toward is too important to build alone.
