Today in AI

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• News roundup

• Today’s big story

• Research spotlight

• This week on the podcast

News roundup

The top stories in AI today.

NEW LAUNCHES

The latest features, products & partnerships in AI

• Hitachi Energy’s new AI tool enhances renewable energy forecasting

• Google Home introduces new Gemini AI features this week

• YouTube is testing a new feature to let you remix popular songs with AI

• Alibaba just released a free, open-source AI coding assistant and it’s very good

• Zerocam’s new mobile app completely rejects AI-tampered photos

• Box’s new AI will turn your files into automated workflows

HARDWARE

Computers, phones, chips, robots & AI powered devices

• The top 4 smartphones with AI features

• AMD launches Versal Premium Gen 2 for data centers

• Amazon pours $110M into AI Trainium chip research

• Samsung’s AI-powered One UI 7 beta is improving, but don’t get too excited

• How Dell is empowering enterprises to unlock the value of edge data

• MIT researchers train robot dog to navigate new environments with AI

• NVIDIA’s new app is an AI-powered GPU control center

AI MODELS

Deployment, research, training & infrastructure

• MIT researcher develops system to find hidden connections between science and art

• Microsoft-backed startup unveils specialized AI models that run on CPUs

• Generative AI models in healthcare require a reassessment of their reliability

• Stanford researchers probe LLMs for consistency and bias

• DeepMind open sources its groundbreaking AlphaFold3 AI protein predictor

IMPLEMENTATION

Announcements, strategies & programs

• These specialists are digitizing legal teams for the AI era

• Why this ICU nurse is cautioning against over-reliance on AI tech

• AI in medicine: 3 questions to separate reality from hype

• How to put AI to use for a sustainable and ethical future

• DataRobot unveils enterprise AI suite to turn AI development into business value

• How Pernod Ricard is leveraging AI to transform its global workforce

• Forrester survey correlates AI adoption trends with marketing team performance

What’s happening in AI right now

The AI chip race intensifies

Nvidia may have reclaimed its crown as the world's most valuable company this week, but the real story lies in how desperately its competitors are working to reduce their dependence on its chips. A flurry of major announcements showcases tech giants' determination to chart their own courses in AI hardware.

The great decoupling

Amazon's reported multi-billion dollar investment in Anthropic comes with strings attached - the AI startup would need to transition from Nvidia's chips to Amazon's own silicon. Meanwhile, the retail giant is pouring $110 million into university research centered on its Trainium chips, providing access to clusters of up to 40,000 chips for complex AI computations.

AMD isn't sitting idle either. Its new Versal Premium Series Gen 2 platform introduces cutting-edge features like CXL 3.1 support and PCIe Gen6 capability, positioning the company to compete more aggressively in data center and AI workloads.

Strategic implications

This wave of investment signals a fundamental shift in how tech companies view AI infrastructure. Rather than remaining dependent on a single dominant supplier, they're building proprietary alternatives - even if that means billions in R&D spending.

The trend mirrors Clayton Christensen's insights about vertical integration in emerging technologies. When product performance isn't yet good enough, companies often need tight control over the entire stack to push the boundaries of what's possible. We're seeing this play out in real-time as tech giants race to optimize their AI systems.

Risks and challenges

The path ahead isn't without obstacles. Transitioning massive AI models to new chip architectures presents significant technical challenges. There's also the risk of market fragmentation as different companies optimize their AI models for specific hardware platforms.

For enterprise customers, this could mean navigating an increasingly complex landscape of AI infrastructure options, each with its own trade-offs in terms of performance, cost, and compatibility.

Looking ahead

This competition might ultimately benefit the entire industry - driving innovation, reducing costs, and creating more options for deploying AI at scale. The question isn't whether the AI chip landscape will change, but how quickly and dramatically that change will come.

We publish daily research, playbooks, and deep industry data breakdowns. Learn More Here

Our partners at Bagel (the AI & cryptography research lab) just released some new research on two complementary methods for advancing AI reasoning: training-time and inference-time techniques.

The findings suggest combining both approaches: training-time builds foundational reasoning, while inference-time optimizes its application. Bagel Network supports this advancement through open-source infrastructure, empowering a community-driven AI evolution through monetizable open source AI.

Read More Here 🥯 

This week on the podcast

Can’t get enough of our newsletter? Check out our podcast Future-Proof.

In this episode, the hosts Anthony Batt and Shane Robinson with guest Joe Veroneau from Conveyor discuss outsmarting paperwork. Conveyor is a company that helps automate security reviews and document sharing between companies. They use AI technology, specifically language models, to automate the process of filling out security questionnaires. This saves customers a significant amount of time and improves the quality of their responses.