AI is often framed as either a world-changing revolution or an overhyped bubble. But at this year’s HumanX Conference in Las Vegas, leaders across government, startups, and innovation teams agreed: Finding success with the technology is about creating real impact — not being distracted by the hype about its eventual potential.
Yet, defining what progress looks like remains a challenge. Are we measuring AI’s success by funding rounds, enterprise pilots, or actual transformation? From US policymakers warning about global competition to AI founders struggling with adoption, the discussions at HumanX revealed that AI’s biggest challenge isn’t innovation; it’s trust.
The Challenge: What Does AI Progress Look Like?
AI has been called the single greatest tool for productivity in human history. But as US Representative Jay Obernolte, Chair of the House’s Task Force on AI, pointed out, the US government hasn’t done enough to share AI’s benefits. While AI could drive exponential workforce productivity, many policymakers still struggle to grasp the upside and the road ahead.
Meanwhile, the global AI race is intensifying. China is graduating four times more STEM PhDs and AI-focused undergraduates as the US. Obernolte warns that if AI is truly going to supercharge economic growth, America needs to invest in talent and better communicate AI’s benefits and role in long-term prosperity.
Former Vice President Kamala Harris offered another perspective: AI’s evolution is a lot like the electrification of the US in the early 20th century. At first, access was limited, but the country made a concerted effort through policy changes and infrastructure investment to ensure that electricity reached all Americans. The question now is: How do we ensure AI’s benefits extend to everyone, not just big tech and early adopters? Harris emphasized the importance of always asking, “Who is not in the room being represented?”
Harris also called for a more human-centered approach to AI development, one that accounts for equity, access, and responsible governance.
The Vendor Landscape
Not surprisingly, many AI vendors are mimicking human form and voice, with solutions ranging from customer support and interview coaching to smarter, more natural-sounding AI voices for connected home devices. The line between real and synthetic interaction is getting blurrier, but there’s still something about connecting with a real human that AI just doesn’t replicate—at least not yet.
Beyond that, data management, integration, and problem identification were major themes across vendors. Many AI tools are not seeking to supplant humans, but to identify the problems that need a human to solve.
We’re also seeing AI tools become increasingly specialized, which can be overwhelming. If general AI models like ChatGPT and Copilot are like cable TV, then today’s agentic AI and niche tools are the streaming apps and YouTube channels disrupting that model. Many enterprises have signed up for cable — so simple! — but that approach doesn’t give them access to other valuable tools and platforms being developed outside the world of the big vendors. Keeping up with the ever-evolving AI landscape is critical for any innovator. If you have a challenge, chances are there’s already an AI tool for it, or one being built.
It really does feel like a tech revolution happening in real time. Imagine living in an era when electricity was first introduced. Suddenly, you no longer had to rely on candles. That’s where we are with AI. The question is, which companies and organizations will take full advantage of it?
The Reality Check: AI Adoption is Still Lagging
- Open AI development could fuel innovation, but it also creates opportunities for bad actors to exploit the technology.
- Closed AI systems could be safer and more controlled, but might limit access and progress.
- So, who gets to decide? Leaders at HumanX argued that AI governance and policy needs to evolve alongside the technology and platforms.
The Path Forward: What AI Needs to Get Right
1. Define What “AI Success” Actually Means
- Is AI’s goal automation or augmentation? The consensus? Augmentation is the right approach. AI should enhance human capabilities, not just replace them.
- AI’s biggest success metric isn’t funding for fast-growing tech companies — it’s trust.
2. AI Policy Needs to Catch Up
- Obernolte warned that the US is falling behind China’s rapid AI talent development.
- Without stronger AI literacy among government leaders, AI’s impact will be bogged down — either by slow regulation or overregulation.
- Governments also need better AI education for the public. Without it, AI fear and misinformation will slow adoption.
3. AI Needs to Be More Inclusive
- Who is making AI decisions? Harris challenged leaders to ask who is missing from the conversation.
- Without diverse perspectives, AI risks being built for a limited audience — not for the broader public good.
Final Thought: Bubble or Revolution?
So, is AI just another bubble, or is it a true revolution?
The answer: It’s both. AI is undeniably overhyped, but many of the speakers at HumanX made the case that its potential is real for those who implement it effectively.
The next phase of AI progress won’t come from bigger funding rounds or bigger AI models. It will come from:
- Better enterprise adoption strategies
- Smarter, more agile policy decisions
- Technologies that can be trusted
- A clearer, more inclusive vision of AI’s future
At this early juncture, the question isn’t whether AI can change the world. It’s which corporate and elected leaders will take the right steps to make it happen.
