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ZDNET’s key takeaways

• Designing healthy relationships between humans and AI will require formalizing business practices.
• The future of work is humans and AIs as colleagues, co-creating value. 
• Agentic AI governance must clarify shared responsibility and accountability.

First, Dr. Vint Cerf, one of the Internet’s co-creators, observes that with AI, “It feels like we’ve encountered a new life form and we’re trying to figure out how it thinks.” It’s worth noting that our work and business relationships with AI might differ from human relationships. 

Second, Dr. David Bray, a tech leader with success in challenging environments including the 9/11 response, Anthrax in 2001, and modernization of the FCC, recommends that “Maybe instead of calling it artificial intelligence, we should call it alien interactions. Because that way we will not try to anthropomorphize the machine.” 

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These two observations, taken together, suggest that how we approach AI will determine which companies succeed in the future. 

And third, Cheryl Strauss Einhorn, award-winning investigative journalist and founder and CEO of Decisive, a decision sciences company, emphasizes that “When the hammer falls, it falls on us. AI doesn’t care. We are going to all be the ones who have to explain, and we’ve got to bear the consequences.” It’s a clear, compelling call to recognize that how we approach relationships with AI is essential to determining business success, 

Cumulatively, these three experts emphasize that the nature of how we work with AI and the work we involve AI in will determine both individual and company success in the decade ahead. For CEOs, corporate boards, and senior policymakers alike, good human discernment in deciding whether and when to trust or not to trust the outputs of an AI is essential. 

Recently, R “Ray” Wang of Constellation Research and I had the opportunity to host Vint, David, and Cheryl on our weekly podcast, DisrupTV. The fast-paced, yet extremely nuanced conversations focused on why we need to avoid confusion between the directions given by humans to an AI (as well as AI to another AI), how we discern whether to trust the outputs of an AI, and whom to hold responsible if an AI does something incorrect or harmful. 

Avoiding confusion in AI instructions is essential

Vint provided the opening salvo of why we need to think proactively about avoiding confusion in the directions given by either a human or AI to another AI, noting: “The big problem I worry about is agents talking to each other using natural language. We don’t need agents to misunderstand each other and execute at the speed of light compared to human speed.” 

Having lived the origin of the internet from the original ARPANet to our modern global network, Vint underscored the risk that both deterministic computer programs and more generative AI can each do things not intended, specifically: “Programs, at least the deterministic ones, do what you tell them to do. The trouble is sometimes what you tell them to do isn’t what you wanted them to do. It’s called a bug.”

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Building on Vint’s comments, David noted, “I define governance as how we avoid anarchy. We’ve got to have anarchy protection, not just for humans, but for agents.” Having previously led positive change in turbulent and often chaotic environments, including work with the US intelligence community, David also provided an apt visual image of the state of affairs. “It’s sort of a repeat of 1910. We hadn’t invented stoplights yet. We hadn’t even figured out stop signs or right of way or sidewalks.” In the 1910s, both New York and Chicago had streets with trolleys running alongside personal automobiles, human pedestrians, and horses, akin to modern enterprises with different cloud-based AI models, local AI models, human users, and other analytic software tools all present together. 

Cheryl reminded us of the importance of knowing one’s default way of understanding the world. “Each of us has a special sauce. It is the way we make decisions. And most of us don’t really have awareness of what that is.” To avoid misunderstandings when prompting an AI, it is crucial to understand our own “special sauce.” As the author of “The Human Edge: Smarter Decisions in the Age of AI” Cheryl elaborated: “If you’re going to lead the machine, what you actually need to do is spend more time to investigate your special sauce… so that instead of giving you somebody else’s answers… it can actually work specifically for you.” 

How do we discern whether to trust AI’s outputs? 

Elaborating on his observation that “We’re trying to figure out how it thinks”, Vint noted: “I see these as a new set of workers that we can relate to. It looks a lot to me like a very smart research agent.” For Vint, who chaired the People Centered Internet coalition from 2017 to 2020, this statement demonstrates his recognition that AI can be considered more than a tool, potentially a digital colleague capable of augmenting the research capabilities of a project. This key observation highlights that for the future of work, incentivizing and guiding workers (including AI agents working with human workers) becomes important. 

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David borrowed from his time working with the intelligence community and FCC, noting that when it comes to discerning whether to trust the outputs of an AI: “A healthy response for societies in these times is increasingly don’t trust the first thing you see unless you triangulate it… That’s what the CIA does.” For David, this message is close to his own professional career since he once endured a large number of bot-generated comments flooding a public commenting system with the requirement to record all 23 million comments submitted, regardless of who, human or digital bot, was posting. For businesses facing the AI agentic future, knowing when agents add value to customer and client interactions will be crucial. David’s experience leading an IT team that kept the system running for human comments amid the flood is a good reminder that AI agents can both amplify and potentially disrupt and obscure human voices. 

Cheryl also emphasized the importance of stepping onto the metaphorical balcony and recognizing that AI is much more than a digital capability. “This is not just some new software. This is actually a cultural change… about problem solving.” Cheryl’s comments built on what Vint and David shared about the importance of sound human discernment and judgment in deciding whether to trust AI outputs for problem-solving in business, communities, and companies. Each of their expert observations underscores the importance of good human discernment and good chemistry between humans and AI in distinguishing productive AI relationships from those that are unhelpful. 

Who do we hold responsible if an AI does something incorrectly?

For the final trifecta of successful human-AI relationships, Vint, David, and Cheryl tackled the key question of accountability in the event of something going wrong. 

Cheryl shared a visual contrast in styles of using AI: “There are really two different ways that people use AI — the surgeon and the Lamborghini driver. When we go to AI, we want one specific answer… we’re using it like the surgeon.” There are risks that a surgeon might make an error, and there also are risks that a surgeon could do everything right and still have adverse outcomes for their patient.

For the other use of AI, Cheryl continued, “There are other times… you’ve got a really high-stakes decision, and you want to undergo a process. At that point, you’re the Lamborghini driver.” In this second case of using AI akin to driving a high-performance race car, skill at navigating tight curves and knowing the capabilities as well as limits of the AI engine are essential to avoid the equivalent of AI car wrecks. 

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In addition, David shared a lesson from flagged maritime vessels: “Whose flag is this AI agent flying when it is doing something? Whose organization is it flying the flag up?” In this example, the organization, if it employs an AI agent, also takes responsibility for the agent, assuming good instructions are provided. He also observed that discerning non-synthetically produced data will become increasingly challenging in the near future. “By 2030, more than 40% of the information on the planet will have been synthetically produced by an AI… that’s going to create massive questions for CEOs and boards.” 

And finally, Vint observed that individuals and organizations need recourse if something goes wrong. “Establishing a mode for recourse in a variety of circumstances might be a very high benefit and maybe even a necessity.” As Google’s chief evangelist, Vint has been encouraging such approaches to AI, and both Vint and David acknowledged that companies like Salesforce, Google, and others were taking similar benevolent approaches to AI in workplace and customer settings. 

Vint also built on a concern that both he and David shared on the increasing challenge in what should be labeled as AI-generated information vs. information from an actual human or human curated sources, observing that “losing access to digital information is a serious issue.” For Vint this includes the risk of losing software to interpret data as well as the origin of data. 

Key takeaways for CEOs and boards

Vint’s caution that “we don’t need agents to misunderstand each other and execute at the speed of light compared to human speed” highlights that the relationships we, as humans, have when engaging AI, as well as the relationships AI engage each other, will determine successful outcomes. Together, Vint, David, and Cheryl highlighted three major points: 

1. There are clear needs to ensure the instructions given to generative AI are clear, precise, and don’t have unintended consequences. The visual image of New York and Chicago streets in the 1910s, with trolleys on the same road as personal automobiles, human pedestrians, and horses, without stop signs, a clear right-of-way, or sidewalks, applies to our current era.
2. Good human judgment is increasingly essential in discerning whether to trust the outputs of an AI, and it is getting more difficult to distinguish between human-generated and AI-generated content. Good chemistry between humans and AI helps distinguish productive AI relationships versus those that are unhelpful.
3. Working to develop clarity, as well as individual and organizational recourse, should an AI do something incorrectly, will be important to improve human-AI relationships within and across companies. There will be different contexts where humans will interact with AI; some will be more focused on seeking a specific answer, while others will be tied to driving a high-performance race car around tight turns at speed. 

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All three experts also emphasized that successful companies, communities, and countries would employ “AI in the group” recognizing the networked interplay between humans and AI to include relationships based on intent and accountability. Both Vint and David also emphasized that we needed to move beyond the Turning Test, focusing on AI that amplifies individual and collective human abilities and helps us improve our strengths. 

This article was co-authored by Dr. David Bray, principal and CEO at LeadDoAdapt (LDA) Ventures, chair of the Accelerator, and distinguished fellow at the Stimson Center.