We Are Mobile First
We stand at the threshold of a profound transformation in how we build technology. For decades, we've been writing code—explicit instructions telling computers exactly what to do. But the future of engineering isn't about writing better code. It's about growing better brains.
This isn't a metaphor. The next generation of engineers won't primarily write software; they'll cultivate neural networks—literal artificial brains—that learn to solve problems through experience rather than instruction. This shift from programming to training represents the most significant change in engineering since we moved from mechanical to digital systems.
Consider the most complex software you've ever encountered. No matter how sophisticated, it's fundamentally brittle—it only handles scenarios its creators explicitly anticipated. But a brain, whether biological or artificial, possesses something magical: the ability to generalize from experience, to handle situations it's never seen before, to surprise even its creators with elegant solutions.
We're not talking about chatbots or image recognizers. We're envisioning specialized neural networks etched into silicon, designed for specific purposes—brains that control factories, manage supply chains, diagnose diseases, or pilot aircraft. Each one trained, not programmed. Each one learning, not just executing.
The process of creating these brains will be radically different from traditional software development:
1. Specification Through Demonstration
Instead of writing requirements documents, we'll build interactive prototypes. Engineers and domain experts will work together in "experience labs," demonstrating desired behaviors rather than describing them. Want a brain that manages traffic flow? Don't write specifications—build a simulation and show it what good traffic management looks like.
2. Growing, Not Building
Traditional software is constructed; brains are grown. We'll create environments where proto-brains can learn through trial and error, guided by human expertise. This is iterative, experimental, and fundamentally creative. Engineers become gardeners, cultivating intelligence rather than constructing it.
3. Testing Through Simulation
Before any brain touches the real world, it will live thousands of lifetimes in simulation. We'll throw every conceivable scenario at it, and many inconceivable ones. These simulations won't just test functionality—they'll generate the experiences that train the brain to handle reality's infinite complexity.
4. Production Through Distillation
The final step transforms our laboratory brain into something that can run efficiently in the real world. Like distilling whiskey, we'll concentrate the essence of what the brain has learned into a compact, efficient form that can be etched into silicon or run on specialized hardware.
Here's the profound insight: we already have a working model for everything we need to do. Human civilization is built on biological brains that we've learned to train, certify, and coordinate without fully understanding how they work.
Certification Without Comprehension
We certify pilots without understanding the exact neural patterns in their brains. We trust surgeons based on their training and track record, not because we can inspect their neural connections. We'll certify AI brains the same way—through demonstrated competence, progressive responsibility, and continuous monitoring.
Reproducibility Through Standards
No two human brains are identical, yet we achieve reproducible results through tools, protocols, and standards. A pilot in Tokyo and one in New York can fly the same aircraft because they follow the same procedures. AI brains will achieve reproducibility not through identical implementations but through shared behavioral contracts and external constraints.
Evolution, Not Patches
Perhaps most importantly, human brains don't update—they evolve. Knowledge advances through generational change, not individual modification. Our AI systems will follow this pattern: populations of brains competing, reproducing, and evolving. When we need new capabilities, we won't patch old brains; we'll breed new ones.
This future isn't about replacing human intelligence—it's about amplifying it. Humans will play several critical roles:
The Architects
Humans will design the environments where brains learn, crafting experiences that teach not just skills but values, judgment, and wisdom.
The Teachers
During the training phase, human experts will guide learning, providing examples, corrections, and most importantly, the nuanced understanding that comes from experience.
The Judges
Humans will evaluate whether brains are ready for deployment, using our intuition and judgment to assess readiness in ways that go beyond metrics.
The Partners
In production, humans and brains will work together, each contributing their unique strengths. Humans provide creativity, ethical judgment, and adaptability. Brains provide consistency, scale, and superhuman performance in specialized domains.
This approach—growing specialized brains for specific purposes—is how we'll eventually create AGI. Not through some moonshot project to build a universal intelligence, but through the gradual combination and coordination of specialized intelligences.
Imagine thousands of specialized brains—ones that understand physics, others that grasp economics, some that excel at creativity, others at logic. As these brains learn to work together, guided by human wisdom and values, they'll form collective intelligences that exceed any individual capability.
The key to making this work is embracing evolution over engineering. We'll create ecosystems where:
This isn't just a metaphor—it's a fundamental recognition that complex intelligence emerges from evolutionary processes, not top-down design.
For Engineers:
Your role transforms from writing code to crafting experiences. You'll need to think like educators, creating curricula for artificial minds. The most valuable engineers will be those who can bridge domains—who understand both the technical aspects of neural networks and the subtle realities of the problems they're solving.
For Project Managers:
Project management becomes experiment management. Instead of tracking features and deadlines, you'll orchestrate learning experiences, manage brain populations, and coordinate the dance between human expertise and artificial learning. Success metrics shift from "on time and on budget" to "evolved effective solutions."
For CEOs:
This transformation requires patience and vision. Growing brains takes time, and the results aren't always predictable. But the payoff is enormous: instead of software that merely executes your vision, you'll have intelligence that extends it, finding solutions you never imagined. The companies that master this transition will have an insurmountable advantage.
As we create these artificial brains, we bear a profound responsibility. We're not just building tools; we're nurturing new forms of intelligence. This demands:
The goal isn't to create servants but partners in the grand project of understanding and improving our world.
This future is closer than you might think. Here's how it will unfold:
Phase 1: Specialized Brains (Now - 2030)
We'll see the first production deployments of specialized brains—neural networks designed for specific tasks, trained through simulation, and certified through rigorous testing. These will start in low-risk domains and gradually expand.
Phase 2: Brain Ecosystems (2028 - 2035)
Multiple specialized brains will begin working together, coordinated by human-designed frameworks. We'll see the emergence of "brain teams" that combine different capabilities to solve complex problems.
Phase 3: Evolutionary Platforms (2032 - 2040)
Full evolutionary ecosystems will emerge, where brains reproduce, mutate, and evolve with minimal human intervention. Human role shifts to setting goals and fitness functions rather than direct training.
Phase 4: Collective Intelligence (2035+)
The boundaries between individual brains blur as they form vast, coordinated intelligences. Humans remain essential as guides, judges, and partners, but the intelligence landscape is transformed.
This transformation won't happen automatically. It requires deliberate choices:
We're not just changing how we build software. We're changing how intelligence itself is created, deployed, and evolved. This future—where engineers cultivate rather than construct, where solutions evolve rather than execute, where humans and artificial brains work as partners—isn't just possible. It's inevitable.
The question isn't whether this transformation will happen, but who will lead it. Will it be companies that cling to traditional software development, or those bold enough to embrace the future of engineered evolution? Will it be engineers who perfect their coding skills, or those who learn to nurture artificial minds?
The age of programming is ending. The age of brain engineering has begun. And in this new age, human creativity, wisdom, and values aren't replaced—they're more essential than ever. We're not building tools to serve us. We're growing partners to join us in the eternal human project of understanding and shaping our world.
The future of engineering is brains. The question is: are you ready to help grow them?