The Data Exchange with Ben Lorica

The AI Boom Is Impressive. Can It Last?

When AI Writes the Code, Who Proves It Works?

Formal verification is transitioning from a niche, labor-intensive practice to a scalable standard, driven by the emergence of auto-formalization. AI models now bridge the gap between natural language requirements and machine-checked proofs, allowing developers to verify software properties without manually coding ever...

Let Domain Experts Build the Agents

Lyft’s agent platform democratizes AI development by enabling non-technical domain experts to build customer-facing agents, significantly reducing development time. By abstracting complex orchestration and providing guardrails like prompt linters and malicious intent detectors, the platform allows experts to focus on d...

AI Is Coming for Tasks Before It Comes for Jobs

AI progress is shifting from a "crashing waves" model of sudden, disruptive automation—characteristic of software engineering—to a "rising tides" phenomenon where capabilities gradually improve across diverse economic tasks. While inference costs are plummeting, fixed costs associated with data gathering, validation, a...

Why Build Your Own AI Model When Frontier Models Already Exist?

Thomson Reuters has developed a domain-specific frontier AI model, "Thomson," to enhance its legal, tax, and compliance software offerings. By utilizing open-weight checkpoints like Qwen 3.5, the company avoids the limitations of basic fine-tuning APIs, instead employing a rigorous pipeline of continuous pre-training a...

Why AI Makes Games Worse—and How to Make Them Better

AI integration in gaming requires moving beyond cost-reduction gimmicks toward foundational mechanics that enable new player experiences. While the industry has historically adopted new technologies quickly, current AI applications often prioritize automated asset production, which risks cheapening the artistic quality...

What Comes After Transformers?

AI development is shifting toward post-transformer architectures that prioritize latent reasoning and continual learning over massive, static parameter scaling. Zuzanna Stamirovska, CEO of Pathway, argues that current transformer-based models are inefficient for reasoning and prone to catastrophic forgetting. By utiliz...

Inside OpenAI's First Chip

OpenAI’s new "Jalapeno" chip aims to reduce inference costs and provide greater control over the AI infrastructure stack. By prioritizing high affinity between memory and compute, the architecture achieves both high throughput and low latency, allowing for flexible deployment across varying model requirements. The deve...

Navier-Stokes and AI generated work: If You Can't Explain It, Did You Really Do It?

The rapid advancement of AI technology is fundamentally reshaping knowledge work, shifting the primary challenge from content generation to validation and verification. While AI tools enable high-speed output, human expertise remains critical for peer-reviewed validation and effective communication. This shift mirrors ...

The Missing Data for Spatial Intelligence

Spatial data, specifically stereo imagery, serves as a critical foundation for training advanced AI world models by providing accurate geometric representations that monocular video lacks. While current foundation models rely heavily on flat 2D data, integrating stereo information allows AI to better understand depth, ...

Your AI Agent Is Costing You More Than You Think

LLMOps represents a fundamental shift from traditional MLOps, moving beyond simple containerization to managing complex, distributed inference systems that require behavioral performance monitoring and rigorous cost control. As generative models scale, the focus has transitioned toward building reliable agentic workflo...

Reasoning Doesn't Start With Language

Visual reasoning remains a significant hurdle for current frontier models, which often fail at basic tasks like counting objects or navigating spatial constraints despite their massive scale. Andrew Dai, CEO of Elorian AI, argues that reasoning originates from the visual world rather than language, necessitating specia...

An Agent Is Just an LLM in a For-Loop

Topic modeling remains a robust tool for structuring large datasets, even as large language models (LLMs) dominate the AI landscape. Developers must master foundational concepts—specifically tokens, embeddings, and attention mechanisms—to build reliable, high-performance applications rather than relying solely on promp...

The Bloomberg Terminal for AI Compute

The AI compute market remains opaque and supply-constrained, characterized by rising rental costs for both legacy A100 and modern H100/B200 GPUs. Silicon Data brings transparency to this sector by tracking GPU rental indices, token expenditure, and forward curves, revealing that demand consistently outpaces supply. Whi...

Can China Do to Robotaxis What It Did to Solar?

AI agents causing security incidents highlight critical failures in governance, identity management, and network access control rather than inherent model danger. Enterprises must prioritize rigorous security guardrails over mere scaling, especially as autonomous agents move toward complex, non-human workflows. Meanwhi...

Why Video Is AI's Next Great Frontier

Video search and understanding require moving beyond simple metadata or transcript-based approaches to natively process the rich, spatial-temporal information inherent in video. Twelve Labs addresses this by utilizing specialized foundation models: Marengo, which creates vector embeddings for efficient search, and Pega...

Your AI Safety Tests Are Lying to You

Effective AI risk evaluation requires a holistic, high-dimensional approach rather than relying on simplistic, low-dimensional tests. Current industry practices often fail because they utilize single-model "LLM as a judge" frameworks, which produce inaccurate results and hinder deployment speed. Organizations must impl...

Enterprise AI Is Moving Slower Than You Think

Why Observability May Be AI’s Next Frontier

Time series foundation models are transforming predictive analytics by moving beyond classical statistical approaches to leverage massive, self-supervised datasets. Ameet Talwalkar, Professor at Carnegie Mellon and Chief Scientist at Datadog, explains that these models have reached a "BERT moment," where scaling parame...

AI Is Producing More Code, but Is It Producing More Value?