Why I Think Cloud-Based AI Will Actually Make Autonomous Machines Mainstream

I came across this article about physical world AI and autonomous machines, and honestly, it made me rethink some assumptions I had about how self-driving technology would evolve. The author, Mo Sarwat from Wherobots, makes a pretty compelling case that the current approach of putting all the AI brains inside the vehicle itself might not be the path to widespread adoption.

His main argument? Most companies aren’t Waymo with billions to spend on onboard computing systems. That’s a fair point that I hadn’t really considered before.

The Waymo Problem (And Why It Matters)

Waymo has been the poster child for autonomous vehicles in the US, and they’ve invested massive amounts in sophisticated onboard navigation tech – lidar, AI models, the works. But here’s the thing that struck me: this approach is incredibly expensive and resource-intensive. According to the article, the vast majority of companies simply don’t have the billions needed to replicate this model.

This reminds me of the early days of computing when companies tried to put all processing power in individual terminals versus leveraging centralized mainframes. Sometimes the centralized approach wins out for economic reasons, even if it seems less elegant technically.

What “Spatial Intelligence Cloud” Actually Means

The concept Sarwat describes is pretty interesting – instead of each vehicle being a completely independent decision-maker, they’d rely heavily on cloud-based systems that maintain “ultra high-precision representations of the planet.” Think of it as Google Maps on steroids, but specifically designed for AI consumption.

The technical challenge here is significant though. As the article mentions, citing Gartner research, physical-world data typically requires heavy engineering to be usable by AI systems. We’re talking about processing satellite imagery, drone data, and converting abstract shapes like vectors representing hills and roads into something AI models can understand.

In my view, this is where companies like Wherobots (Sarwat’s company) are trying to carve out a niche in what could become a massive market. The “spatial intelligence cloud” space seems ripe for innovation, especially as more industries beyond automotive start deploying autonomous systems.

The Real-World Scenarios That Make This Compelling

What really sold me on this concept were the specific examples in the article. Picture this: autonomous delivery vehicles in rural areas failing to recognize that long driveways lead to customers’ homes, or self-driving cars getting lost in large apartment complexes in urban areas.

These aren’t edge cases – they’re exactly the kind of everyday scenarios that would make or break consumer adoption. I’ve seen this firsthand with food delivery apps that struggle with apartment building navigation even with human drivers.

The cloud-based approach could theoretically solve these problems by maintaining constantly updated, detailed maps that go far beyond what current GPS systems provide. Fleet companies could use AI to create “finely detailed and ever-evolving maps” that get smarter over time.

My Take on the Hybrid Approach

What I find most realistic about Sarwat’s vision is that he’s not suggesting we completely eliminate onboard systems. Real-time decisions still need high-definition sensors like lidar for immediate hazard detection. But the cloud component could handle route optimization and identify potential problems before the vehicle even starts its journey.

This hybrid model makes economic sense too. Instead of every manufacturer having to solve the same mapping and spatial intelligence problems independently, they could leverage shared cloud infrastructure while focusing their R&D on vehicle-specific innovations.

The market dynamics here are interesting. In the US, we’re seeing companies like Tesla taking a more vision-based approach, while others like Cruise (before their recent troubles) were following Waymo’s sensor-heavy model. A cloud-centric approach could level the playing field for smaller players.

Challenges I See Ahead

That said, there are some significant hurdles. Connectivity is a big one – what happens when vehicles lose cloud connection in remote areas or during network outages? The article doesn’t really address this reliability concern.

There’s also the data privacy and security angle. Having vehicles constantly communicating with cloud systems about their precise locations and routes creates new attack vectors that bad actors could exploit.

From a regulatory standpoint, especially in markets like the US and EU, there will likely be questions about data sovereignty and cross-border data flows for these spatial intelligence systems.

The Bigger Picture for Autonomous Systems

What excites me most about this approach is how it could accelerate adoption across different types of autonomous machines – not just cars, but agricultural equipment, construction vehicles, and delivery drones. The article mentions tractors and drones as examples, and I think that’s where we might see faster adoption than in consumer vehicles.

The agricultural sector, in particular, seems like a natural fit. Farmers are already comfortable with GPS-guided equipment, and the controlled environments of farms could be perfect testing grounds for cloud-enhanced autonomous systems.

Looking at the competitive landscape, companies that can build robust spatial intelligence platforms might become the “AWS of autonomous machines” – providing critical infrastructure that everyone else builds on top of. That’s potentially a massive market opportunity.

This post was written after reading Is physical world AI the future of autonomous machines? . I’ve added my own analysis and perspective.

Disclaimer: This blog is not a news outlet. The content represents the author’s personal views. Investment decisions are the sole responsibility of the investor, and we assume no liability for any losses incurred based on this content.