What are the top minds in AI actually talking about behind closed doors?

At the HumanX conference—arguably the flagship event in the AI ecosystem—hundreds of speakers (from CEOs to policymakers to Kamala Harris) shared their unfiltered thoughts on the state and future of artificial intelligence. But with so much happening at once, even attendees couldn’t absorb it all.

So HumanX did something novel: they partnered with Read.AI to record and synthesize every single session. The result? A real-time AI copilot for the conference and a post-event report that reveals the key themes, trends, and tensions shaping the industry.

In this episode, we speak with HumanX CEO Stefan Weitz and Read.AI CEO David Shim to unpack the insights from that report—what they signal for 2025, what business leaders should pay attention to, and what’s probably just noise.

We talk about the rise of agentic AI, the shift from AGI ambition to ROI expectations, and the practical realities of implementing AI inside large organizations. We also dig into issues of trust, open source, industry-specific adoption, and how AI is starting to reshape roles from customer service to legal to healthcare.

Whether you’re in strategy, ops, tech, or just trying to keep up, this conversation offers a data-driven pulse check on where enterprise AI is headed.

Highlights & Timestamps:

• [1:00] – How Read AI became the official AI copilot of the HumanX conference
• [3:10] – “You can’t be everywhere at once”—the problem this tech solves at events
• [6:15] – The most talked-about concept at HumanX: agentic AI
• [7:45] – Why AGI hype is shifting toward practical use cases with agents
• [8:58] – The fast hype-decay cycle of AI and the emerging focus on outcomes
• [12:26] – Open source, cost savings, and why business leaders care about transparency
• [14:19] – Trust as the “anchoring tenet” of enterprise AI adoption
• [16:45] – Real ROI: how Read AI identified $10M in sales pipeline in 30 days
• [20:03] – Why companies are hiding their AI wins from competitors
• [22:43] – Cross-industry learnings: how healthcare patterns may apply to other sectors
• [25:47] – The “put up or shut up” moment: 2025 as the year AI must deliver
• [29:06] – What business leaders should do before launching AI agent initiatives
• [35:03] – The #1 mistake orgs make with AI: failing to assign ownership
• [37:09] – Predictions: personalization, interoperability, and privacy friction ahead
• [42:28] – How Stefan and David personally use AI—for work, fun, and creative hacking

Links & Mentions:

• HumanX – Flagship AI conference co-founded by Stefan Weitz
• Read AI – Productivity-focused AI platform by David Shim
• Suno – AI music generation tool mentioned by Stefan
• Replit – AI coding sandbox used by Stefan for strategy visualization
• Veo by Google DeepMind – AI video generation tool referenced by David

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What if we’re all chasing the wrong kind of AI? Dr. Ruchir Puri, Chief Scientist of IBM, argues that Artificial General Intelligence (AGI) is overrated—and that we should be focusing instead on AUI: Artificial Useful Intelligence. This is a pragmatic, business-focused approach to AI that emphasizes real-world value, measurable outcomes, and implementable solutions.

In this episode of AI-Curious, we explore what AUI actually looks like in practice. We discuss how to bring AI into your organization (even if you’re just getting started), why IBM is betting big on small language models (SLMs), and how companies can move beyond hype toward real, trustworthy AI agents that do actual work.

You’ll also hear:

• Why AI usefulness is a function of both quality and cost [00:11:00]
• The “crawl, walk, run” strategy IBM recommends for business adoption [00:14:00]
• Internal IBM examples: HR systems and coding assistants [00:16:00]
• Why SLMs may be a smarter bet than LLMs for many enterprises [00:37:00]
• A breakdown of how agentic systems are evolving to reflect, act, and self-correct [00:41:00]

Whether you’re leading a startup or an enterprise, this conversation will help you reframe how you think about deploying AI—starting not with hype, but with value.

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What exactly is AGI—Artificial General Intelligence—and how close are we to achieving it? Will it transform the world for better or worse? And how can we even tell when true AGI has arrived?

In this episode of AI Curious, we sit down with Dr. Ben Goertzel, the iconic computer scientist who coined the term AGI more than 20 years ago. As the founder of SingularityNET and the Artificial Superintelligence Alliance, Ben has spent decades thinking about the architecture, risks, and potential of general intelligence.

We explore why today’s large language models (LLMs), while powerful, still fall short of true AGI—and what will be needed to bridge that gap. We dive into Ben’s prediction that AGI could arrive within just 1 to 3 years, and why he believes it will likely be decentralized. Along the way, we unpack some of the key ideas from his recent “10 Reckonings of AGI”—a candid look at the social, economic, and existential questions we must face as AGI reshapes human life.

Topics include:

• [00:04:00] What AGI really means vs. current LLMs
• [00:10:00] Are we reaching the limits of current AI architectures?
• [00:13:00] How will we know when AGI has truly arrived?
• [00:17:00] The “PhD test” for human-level AGI
• [00:19:00] AGI timeline predictions (1–3 years? 2029?)
• [00:29:00] The 10 Reckonings of AGI: key societal impacts
• [00:36:00] The gap between AGI and superintelligence
• [00:44:00] Why a decentralized AGI might be safer
• [00:51:00] Surprising upsides of a post-AGI world

If you’re curious about the future of artificial intelligence, this conversation offers a rare and unfiltered perspective from one of the field’s most original thinkers.

SingularityNet

https://singularitynet.io/

Ben Goertzel on X

https://x.com/bengoertzel

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