[Thesis name] Venture Capital

DevOps has been refined over the past 30 years and certain parts will need to adapt for the robotics ecosystem. Observability, CI/CD, deployment, and infrastructure will all likely need to evolve to cater to robotics.

We have seen a broad shift to open source technologies as large enterprises attempt to avoid vendor lock-in, are focused on improving security, and are building more custom solutions. We are also excited about the benefits that these technologies gain from community contribution.

Version control systems have been pivotal in managing code, but as the digital landscape pivots towards more complexity, traditional version control platforms show their limitations. While solutions like Perforce have made strides in adapting to these needs, there remains room for improvement and innovation. As we look to the future, integrating features such as improved storage and cloud-native functionality could significantly enhance efficiency and collaboration.

The race is on for innovators and established players alike to craft a service that not only meets the current demands of large file asset management but also sets the stage for future advancements, potentially transforming how we store, manage, and analyze digital assets in a rapidly evolving digital world.

Formal Proof / Solver Based Simulation: These are tools that are embedding domain-specific logic and mathematical solvers to formally validate AI outcomes. Especially relevant for critical industries that can't settle for non-deterministic outputs.

Physics-based Simulations: These AI tools embed the laws of physics governing a specific domain and implement that solution within an enterprise

Digital Twins: Tools creating replicas of unique assets and simulating the impact of stimulus.

Agent-Based Simulations: These are tools that use agents to emulate real people or entities to help understand how they will react to stimulus. This category can be thought of as decision co-pilots.

The current wave of digital instability highlights a critical paradox: we have traded independence for speed, security, and efficiency of global cloud consolidation. While hyperscalers have democratized high-end computing, the resulting architecture means single errors now trigger massive, cascading blackouts across our essential infrastructure. As a result, 2026 is becoming the year when both governments and enterprises aggressively adopt multi-cloud solutions, edge computing, and on-premise repatriation to ensure that a single cloud failure does not lead to total operational paralysis.

Local AI is gaining traction thanks to advances in specialized hardware and more efficient inference, making models deployable directly on devices.

This is resulting in stronger privacy, lower latency, and offline capability for everyday applications. Local processing enables users to customize AI for sensitive, real-time scenarios, while avoiding ongoing cloud fees; however, it remains limited by hardware constraints and higher initial setup costs.

Meanwhile, cloud AI centralizes inference on powerful remote servers, lowering barriers for experimentation and scaling with pay-as-you-go pricing, which is ideal for large datasets and collaborative teams.