Canifi Issue #036

Hello, Canifi community! This week, we're tracking major moves in the AI landscape, from Wall Street's internal adoption of AI assistants to new platforms simplifying edge computing. We're also seeing significant developments in AI for specialized industries and a major debate unfolding in the regulatory space. Here’s how these innovations are shaping the future and what they mean for you.

June 24, 2025

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📰 This Week’s AI Highlights

Goldman Sachs Deploys Firmwide Generative AI Assistant In a major move to boost productivity, Goldman Sachs has rolled out an in-house generative AI assistant to its entire workforce. The tool, "GS AI Assistant," is designed to help employees with tasks ranging from summarizing complex documents and drafting content to performing data analysis, tailored to the specific needs of different divisions like investment banking and asset management.
Source: Goldman Sachs Announces Firmwide Launch of AI Assistant
Use Case: Large enterprises can look to this as a model for developing bespoke, secure AI tools to enhance employee productivity and streamline complex internal workflows across various departments.

Proposed 10-Year Freeze on State-Level AI Laws Advances in U.S. Senate A controversial proposal to place a 10-year moratorium on states creating their own AI laws has cleared a key procedural hurdle in the U.S. Senate. If passed, the measure would centralize AI regulation at the federal level, aiming to create a uniform business environment but raising concerns about limiting states' ability to address specific local and ethical issues.
Source: 10-Year Freeze on State AI Laws Clears Senate Hurdle
Use Case: Tech companies and startups developing AI would face a more predictable, unified regulatory landscape across the U.S., but they must also prepare for the possibility of a single, comprehensive federal framework governing their operations.

Latent AI Launches "Agentic" Platform to Automate Edge AI Latent AI has released "Latent Agent," a new platform designed to automate and simplify the deployment of AI models on edge devices (like cameras, sensors, and local hardware). The platform aims to drastically reduce the complexity and time required for MLOps at the edge, cutting deployment cycles from weeks to just hours without needing specialized hardware expertise.
Source: Latent AI Debuts Agentic Platform to Automate Edge AI
Use Case: Developers and businesses working on IoT and edge computing projects can now deploy and manage AI models much more efficiently, accelerating the rollout of smart devices and real-time AI applications.

AI-Powered Ingredient Discovery Firm Shiru Named a WEF Tech Pioneer Shiru, a company that uses an AI platform to discover novel proteins and other ingredients for the food industry, has been named a 2025 Technology Pioneer by the World Economic Forum. The recognition highlights the growing impact of AI in biotechnology and its potential to create healthier, more sustainable food products.
Source: Shiru Named 2025 Technology Pioneer by World Economic Forum
Use Case: Food and beverage companies can partner with AI-driven platforms like Shiru to rapidly innovate and develop new ingredients, meeting consumer demand for healthier and more sustainable options.

Mastercard Deploys Advanced AI to Combat Card Fraud in EEMEA Mastercard has introduced a new, advanced AI solution in the Eastern Europe, Middle East, and Africa (EEMEA) region to tackle credit card fraud. The "Account Intelligence Reissuance" service uses AI to more accurately predict and identify compromised cards, streamlining the process of reissuing them before fraudulent activity can occur.
Source: Mastercard Unveils Advanced AI Solution to Tackle Card Fraud
Use Case: Financial institutions can leverage such AI tools to significantly enhance fraud prevention, reduce losses, and protect customers by proactively identifying and neutralizing threats in real-time.

New Report Highlights AI Adoption and Challenges in Logistics A new "State of AI in Logistics" report from Pando and JBF Consulting reveals that while 91% of logistics companies have increased AI investments recently, 83% cite poor data quality as the biggest barrier to successful implementation. The report underscores the strong enthusiasm for AI to cut costs and improve decision-making in the complex supply chain ecosystem.
Source: Pando and JBF Consulting Release 2025 Report on the State of AI in Logistics
Use Case: Logistics managers should prioritize improving their data infrastructure and quality to unlock the full potential of AI for optimizing operations, navigating disruptions, and increasing overall efficiency.

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