AI Is the Shiny Object. But What’s Now and Next in Tech?

By Zack Huhn, Enterprise Technology Association

If AI is the shiny object in the room, the real story is unfolding just behind it.

Every boardroom is buzzing about artificial intelligence, and rightly so. But while ChatGPT headlines dominate LinkedIn feeds, the deeper transformation in tech is evolving fast—and most leaders aren’t tracking the full picture. Behind the buzzwords, the world’s most powerful tech labs are retooling their missions, investing billions, and laying the groundwork for the next wave of innovation: superintelligent systems, AI agents, and multi-modal infrastructure.

So what’s now? What’s next? And what really matters for enterprise leaders, strategists, and builders?

🔥 What’s Now: The Rise of Superintelligence Labs

Meta’s launch of its Superintelligence Lab this summer made a clear statement: the race to artificial general intelligence (AGI) is not just theoretical. It’s operational—and moving fast.

Led by Alexandr Wang (Scale AI) and Nat Friedman (ex-GitHub), Meta’s new division is focused on building AGI-capable systems that are:

• Safe: Built with red-teaming, interpretability, and community alignment baked in.

• Scalable: Running on Meta’s custom MTIA chips alongside NVIDIA’s GPU backbone.

• Open: Committed to releasing open-source tools like Llama 3 and SAM to empower broad use.

And Meta isn’t alone. A wave of tech labs is reshaping the innovation landscape, and their strategies are surprisingly diverse.

Key Labs Defining the Future of AI

Here’s a look at the major players reshaping AI—and what sets each one apart:

1. Meta

• Mission: Build safe and open superintelligence for the public good.

• Stack: Custom MTIA chips + massive NVIDIA deployments.

• Approach: Open-source leadership (Llama, SAM, AudioCraft) and practical safety tools.

2. OpenAI

• Mission: Ensure AGI benefits all of humanity.

• Stack: Deep partnership with Microsoft and Azure compute.

• Approach: Closed-source for frontier models (GPT-4), with tight safety alignment and API access.

3. Anthropic

• Mission: Build steerable and interpretable AI systems.

• Stack: Backed by Amazon and Google Cloud.

• Approach: Constitutional AI, public benefit corporation status, and clarity-first principles.

4. DeepMind (Google DeepMind)

• Mission: Solve intelligence and use it to advance science and humanity.

• Stack: Google Cloud with proprietary TPU infrastructure.

• Approach: Strong research culture, mostly closed deployments, science-first orientation.

5. xAI

• Mission: Build truth-seeking AI aligned with human values.

• Stack: Taps into Tesla, X, and Dojo compute infrastructure.

• Approach: Closed-source models like Grok; focused on integrating AI into Musk’s ecosystem.

6. Mistral

• Mission: Democratize access to powerful, efficient LLMs.

• Stack: Lean, EU-based model with smaller, high-performing architectures.

• Approach: Full open weights with community-centered development.

🔭 What’s Next: Emerging Tech Shifts to Watch

1. AI Agents & Automation

Tools like Devin (AI software engineer), OpenAI’s function-calling layers, and Meta’s task orchestration platforms are ushering in a future where AI doesn’t just suggest or generate—it acts across platforms.

2. Multimodal Intelligence

Systems that combine text, image, audio, and video input/output are maturing fast. LLMs like GPT-5, Gemini 2.5, and Llama 4.x will fundamentally shift how we interact with machines—beyond keyboards and prompts.

3. Extended Context Windows

Next-gen models are now operating with over 1 million tokens of memory, allowing them to analyze full documents, books, codebases, or strategic datasets in a single interaction.

4. Open vs. Closed Innovation Paths

The divide between open-source (Meta, Mistral) and closed (OpenAI, DeepMind) continues to shape what’s available to builders, startups, and enterprises—and how fast ecosystems evolve.

5. Safety, Governance & Global Standards

The future of innovation will be built on trust. International safety coalitions, national AI frameworks, and public-private alliances will define how AI gets regulated—and how it gets adopted.

💡 What It Means for Business and Tech Leaders

The real takeaway here isn’t just about keeping up with AI—it’s about understanding the speed, scope, and consequences of what’s unfolding.

If you’re a business or technology leader, this shift will impact:

• Your stack: Systems and infrastructure will require more orchestration and real-time learning layers.

• Your people: Talent strategies must evolve—your teams need AI fluency, not just technical skills.

• Your roadmap: Innovation, data governance, and customer trust are no longer separate conversations.

• Your partnerships: You’ll need allies across startups, research, and advisory domains to keep pace.

🚀 Join the ETA Brain Trust

At the Enterprise Technology Association, we’re not just watching the future unfold—we’re actively building the support structure to help leaders thrive in it.

From AI Week events to peer advisory circles and insight-packed publications, ETA is the national brain trust for business and technology leaders navigating AI, cybersecurity, and emerging tech.

👥 Join the Brain Trust at joineta.org/sign-up

Let’s stay curious, stay grounded—and stay in front. Join us at the Future Tech Forum and AI Week events to do just that!