Another Crazy Day in AI: Your Company Needs Hybrid AI (And Doesn't Know It Yet)

Wowza Team
56 minutes ago
4 min read

Hello, AI Enthusiasts.

How’s the week unfolding on your side? The AI world’s busy connecting the dots.

The AI industry’s talking less about chips and more about deployment. “Hybrid AI” is the new buzz, mixing private systems with public cloud to create scalable, secure, and surprisingly flexible solutions.

Meanwhile, Gamma just turned its slide-making magic into a $2.1 billion success story.

And Higgsfield is giving creative teams a new space to co-build, edit, and ship... all without leaving the app.

Makes you wonder what the next few days will bring.

Here's another crazy day in AI:

Why Hybrid AI is gaining traction with companies
Gamma hits $2.1B valuation as AI slides surge in popularity
Higgsfield rolls out shared AI hub for teams and studios
Some AI tools to try out

TODAY'S FEATURED ITEM: Hybrid AI Is Reshaping Enterprise Strategy

A robotic scientist in a classic white coat with 'AI Scientist' on its back stands beside a human scientist with 'Human Scientist' on their coat, looking towards the AI Scientist.

Image Credit: Wowza (created with Ideogram, edited on Canva)

How can companies harness AI’s full power without giving up control of their own data?

While much of the conversation around artificial intelligence focuses on chip manufacturers and their latest hardware, something interesting is happening with how companies actually deploy AI. TECHnalysis Research president Bob O'Donnell recently spoke with Yahoo Finance's Market Catalysts about what he calls “hybrid AI”—essentially, how major enterprises are combining public cloud services with their own private infrastructure. With AMD's Analyst Day at Nasdaq and SoftBank's sale of its Nvidia stake setting the financial backdrop, O'Donnell discusses how companies like Coca-Cola and GM are building private AI “factories” and what that could mean for the next phase of innovation, infrastructure, and investment.

Some of the main insights from the discussion include:

SoftBank’s sale of its Nvidia shares reflects a portfolio reallocation, with capital moving toward ventures like ARM and OpenAI instead of signaling lost confidence.
Hybrid AI builds on the concept of hybrid cloud computing, allowing workloads to be divided between public platforms and private data centers for flexibility and security.
Large enterprises are developing in-house AI systems to handle sensitive data and proprietary processes more securely.
AMD is positioning itself as a strong competitor to Nvidia, expanding across CPUs, GPUs, and FPGAs to support growing AI workloads.
AI applications are moving closer to the edge, enabling automation through robotics, sensors, and industrial tools.
Experts anticipate that tangible returns and wider adoption of AI technologies may become more evident by 2026–2027.
Integrating AI into organizations remains a human challenge as much as a technical one, requiring changes in workflow and management.

O'Donnell talks about something that doesn't always get much attention in AI coverage: the practical choices companies face when they're deciding where their AI should actually run. When large companies like Coca-Cola or GM invest in their own infrastructure, they're thinking about things like data security, regulatory compliance, long-term costs, and keeping control over systems that might be central to their business. The hybrid approach gives them flexibility—they can keep sensitive information on their own servers while still using public cloud services for other tasks that don't require the same level of security.

O'Donnell acknowledges that companies are spending heavily on AI infrastructure right now, but many are still figuring out what works best for them. The returns will probably come in stages over several years as organizations learn through experience. His point about the human side being just as tough as the technical side seems particularly relevant—having sophisticated AI systems available doesn't automatically mean people will know how to use them effectively or that organizations will successfully integrate them into daily operations. For anyone following how AI is actually being implemented in businesses rather than just developed in labs, these kinds of details provide useful perspective. It's less about dramatic breakthroughs and more about the steady work of figuring out where systems should run, how to manage them, and how to make them genuinely useful for specific business problems.

Check it out here.

OTHER INTERESTING AI HIGHLIGHTS:

Gamma Hits $2.1B Valuation as AI slides Surge in Popularity

/Julie Bort, Startups & Venture Desk Editor, on TechCrunch

AI presentation startup Gamma has reached a $2.1 billion valuation after raising $68 million in its Series B led by Andreessen Horowitz. Co-founder and CEO Grant Lee shared that the company has hit $100 million in annual recurring revenue with 70 million users. Known for its AI-generated presentations, websites, and social content, Gamma has achieved profitability with a small team of about 50 employees. The new funding round also includes a $20 million secondary offering for early employees, underscoring investor confidence in Gamma’s steady, profit-driven growth.

Another Crazy Day in AI: Your Company Needs Hybrid AI (And Doesn't Know It Yet)

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