Jake Loosararian: Robotics must prioritize data collection for efficiency, the impact of Nvidia’s dominance on hardware diversity, and the crucial role of determinism in future advancements

Key takeaways

Robotics should prioritize data collection to optimize performance and decision-making.
Industries like energy and defense are increasingly leveraging robotics for operational efficiency.
The future of robotics is promising, but safety and reliability through determinism are crucial.
Consolidation around Nvidia limits hardware diversity, impacting AI development.
Robotics can enhance efficiency in industries with high energy costs and frequent shutdowns.
GPUs have become vital for scaling AI applications, especially in chat-based models.
Fragmentation in hardware compatibility is due to proprietary software systems.
CUDA is outdated for modern systems, indicating a need for updated GPU software.
Heterogeneous systems enhance computing flexibility and scalability.
Enterprises seek hardware flexibility to avoid vendor lock-in.
The pragmatic impact of AI and robotics is a focus for sectors like energy and defense.
Determinism in robotics ensures safety and reliability in AI applications.
The rise of chat-based models has driven GPU importance in AI.

Guest intro

Jake Loosararian is the CEO and co-founder of Gecko Robotics, a company deploying purpose-built robots and AI for mission-critical infrastructure inspection across energy, defense, and manufacturing. In 2012 as a student at Grove City College, he built his first wall-climbing robot in a dorm room to solve persistent downtime at a local power plant, launching the company in 2013. Gecko now manages over 500,000 critical assets for Fortune 100 partners and the US Air Force and Navy, reaching unicorn status with a $1.25 billion valuation in June 2025.

The role of data in robotics

The idea of gathering information and data using robotics to help drive better outcomes

— Jake Loosararian
Robots should not be built just for the sake of building; they must serve a purpose in data collection.
Data-driven robotics can prevent a commoditized future in the industry.
If you’re building robots just to build robots… it leads to a commoditized future

— Jake Loosararian
Understanding the role of data is crucial for optimizing infrastructure performance.
Robotics in infrastructure is about improving decision-making through data.
The pragmatic impact of artificial intelligence… can potentially drive better decisions

— Jake Loosararian
Data collection is essential for enhancing operational efficiency in critical sectors.

Robotics in energy and defense

Energy, oil, gas, and defense sectors focus on the pragmatic impact of robotics.
The energy, oil and gas companies… are completely looking at how impactful can robotics be

— Jake Loosararian
Robotics and AI integration is enhancing operational efficiency in these industries.
The defense sector is exploring robotics for improved decision-making.
The department of war are completely looking at how impactful can robotics be

— Jake Loosararian
Robotics helps address challenges in industries with high energy costs.
Robotics can significantly improve operational efficiency in industries facing high energy costs

— Jake Loosararian
The focus is on how robotics can drive better outcomes in energy and defense.

Future of robotics and determinism

The future of robotics is optimistic but requires a focus on determinism.
I am very excited and optimistic about… what the future will be with robotics

— Jake Loosararian
Determinism ensures safety and reliability in robotics applications.
The key is being deterministic… that’s maybe where we’re lacking a bit

— Jake Loosararian
Safety and reliability are critical in the rapidly evolving field of robotics.
Determinism balances innovation and safety in robotics.
The focus on determinism addresses potential safety concerns in AI.
Ensuring reliability in robotics is crucial for future advancements.

Hardware diversity and Nvidia’s dominance

The consolidation around Nvidia limits hardware diversity in AI development.
A lot of the world is really consolidated around the Nvidia platform

— Jake Loosararian
There is a need for more hardware vendors to foster innovation in AI.
We want more hardware vendors in the space

— Jake Loosararian
Nvidia’s dominance impacts the diversity of AI hardware options.
Hardware diversity is crucial for fostering innovation in AI.
The current landscape of AI hardware needs more competition.
Consolidation limits the potential for diverse AI hardware solutions.

The importance of GPUs in AI

GPUs have become essential for scaling AI applications.
GPUs have captured the world… the inference side of it is huge

— Jake Loosararian
The rise of chat-based models has driven the importance of GPUs.
GPUs enhance computational capabilities in AI technologies.
The role of GPUs is critical for inference tasks in AI.
The evolution of AI technologies has increased the demand for GPUs.
GPUs are vital for enhancing AI computational power.
The importance of GPUs in AI continues to grow with technological advancements.

Fragmentation in hardware compatibility

Fragmentation arises from the lack of a unifying software layer.
Hardware companies don’t get along… they build software for their chips

— Jake Loosararian
Proprietary systems contribute to hardware compatibility issues.
The competitive dynamics between hardware companies lead to fragmentation.
Proprietary software solutions impact industry fragmentation.
Compatibility issues arise from the lack of a unified approach.
The impact of proprietary software on hardware systems is significant.
Fragmentation affects the overall efficiency of hardware systems.

The need for updated GPU software

CUDA is outdated for modern systems and generative AI.
CUDA… is the shining star of system software for GPUs but it’s 20 years old

— Jake Loosararian
There is a need for innovation in GPU software for current technology trends.
Existing GPU software may not meet the requirements of modern advancements.
The relevance of CUDA is questioned in the context of new technologies.
Modern systems require updated GPU software solutions.
The evolution of technology demands innovation in GPU software.
The need for updated software is critical for advancing AI capabilities.

Heterogeneous systems in computing

Heterogeneous systems enhance flexibility and scalability in computing.
You get these heterogeneous systems where you have different architectures

— Jake Loosararian
Different hardware architectures communicating enhances computing capabilities.
Heterogeneous systems are vital for modern computing architecture.
The impact of heterogeneous systems on enterprise flexibility is significant.
Enterprises benefit from the flexibility offered by heterogeneous systems.
The shift in computing architecture influences technology investments.
Heterogeneous systems play a key role in future computing developments.

Avoiding vendor lock-in with hardware choices

Enterprises desire the ability to choose between different hardware systems.
It gives enterprises choice… they want choice to be able to adopt other systems

— Jake Loosararian
Avoiding vendor lock-in is a critical concern for enterprises.
Flexibility in technology choices is essential for enterprises.
Enterprises seek to avoid dependency on a single hardware vendor.
The ability to choose different systems enhances enterprise flexibility.
Vendor lock-in poses challenges for technology adoption.
Enterprises prioritize flexibility in hardware choices to enhance innovation.