James Thomason

San Francisco-based CTGT, a startup focused on making AI more trustworthy through feature-level model customization, won the Best Presentation Style award at VB Transform 2025 in San Francisco. Founded by 23-year-old Cyril Gorlla, the company showcased how its technology helps enterprises overcome AI trust barriers by directly modifying model features instead of using traditional fine-tuning or prompt engineering methods.

Inside a new David's Bridal store in Delray Beach, Florida, a bride-to-be carefully taps images on a 65-inch touchscreen, curating a vision board for her wedding. Behind the scenes, an AI system automatically analyzes her selections, building a knowledge graph that will match her with vendors, recommend products and generate a personalized wedding plan.

This article is part of VentureBeat’s special issue, “The Real Cost of AI: Performance, Efficiency and ROI at Scale.” Read more from this special issue.

The signs are everywhere that edge computing is about to transform AI as we know it. As AI moves beyond centralized data centers, we're seeing smartphones run sophisticated language models locally, smart devices processing computer vision at the edge and autonomous vehicles making split-second decisions without cloud connectivity. 

In the race to harness the transformative power of generative AI, companies are betting big – but are they flying blind? As billions pour into gen AI initiatives, a stark reality emerges: enthusiasm outpaces understanding. A recent KPMG survey reveals a staggering 78% of C-suite leaders are confident in gen AI's ROI. However, confidence alone is hardly an investment thesis. Most companies are still struggling with what gen AI can even do, much less being able to quantify it. 

Drip Capital, a Silicon Valley-based fintech startup, is leveraging generative AI to achieve a remarkable 70% productivity boost in cross-border trade finance operations. The company, which has raised more than $500 million in debt and equity funding, is employing large language models (LLMs) to automate document processing, enhance risk assessment and dramatically increase operational efficiency. This AI-driven approach has enabled Drip Capital to process thousands of complex trade documents daily, significantly outpacing traditional manual methods.

Arcee AI launched SuperNova today, a 70 billion parameter language model designed for enterprise deployment, featuring advanced instruction-following capabilities and full customization options. The model aims to provide a powerful, ownable alternative to API-based services from OpenAI and Anthropic, addressing key concerns around data privacy, model stability and customization.

On the first floor of an industrial modern office building, we are among a select group of journalists invited into a secretive lab at Amazon to see the latest Just Walk Out (JWO) technology.

The trend toward small language models is accelerating as Arcee AI announced its $24M Series A funding only 6 months after announcing its $5.5M seed round in January 2024. The company also announced the launch of Arcee Cloud, a hosted SaaS version of their platform. This new offering complements their existing in-VPC deployment option, Arcee Enterprise. 

Palo Alto-based AI chip startup SambaNova Systems was awarded "Coolest Technology" at VentureBeat Transform 2024 in San Francisco. The company, founded in 2017, has raised $1.1 billion to date and is currently valued at $5 billion.

Meta AI researchers have unveiled MobileLLM, a new approach to creating efficient language models designed for smartphones and other resource-constrained devices. Published on June 27, 2024, this work challenges assumptions about the necessary size of effective AI models.