As we cross into mid-2025, the narrative around artificial intelligence is no longer about sheer processing power or the latest model architecture. We are firmly in the era of AI trust. The new competitive advantage isn’t about how many parameters a model has or how quickly it can parse a billion tokens. It’s about how trustworthy, verifiable, and persistently intelligent it can be.
In my 2023 commentary on the rise of food delivery services, I noted that customers were abandoning platforms not because of a lack of options, but due to a breakdown in trust. Missed deliveries, cold food, and absent customer service eroded confidence in systems that once thrived on loyalty and convenience. The same trajectory now defines the AI landscape. It’s not enough for AI to be brilliant; it must be dependable.
This realization culminated in what I’ve coined as the “AI Trust Era Recognition,” where I made the analogy: “AI without memory is like hiring a world-class consultant who forgets every meeting.” That line, at its core, reflects the biggest barrier to AI adoption in the enterprise ephemerality. For AI to be a true collaborator, it must recall past context, learn from it, and continuously apply it.
This isn’t a new theme. Back in 2010, when discussing BroadSoft’s move to the cloud, I framed cloud computing not just as a technological shift, but as a trust transfer—from local servers to remote infrastructure. That same lens now applies to AI. Businesses are not just offloading tasks to AI; they’re entrusting judgment, autonomy, and the stewardship of their knowledge.
We’ve seen this before. When mobile apps started to replace traditional desktop software in the mid-2000s, there was initial skepticism. I wrote then about the Nokia N800, a Linux-powered internet tablet that predated the iPad and hinted at a future where portability and connectivity reigned supreme. What made it viable wasn’t the specs. It was the ecosystem and the experience. Trust in the product emerged because it remembered users, synced with their habits, and respected their expectations.
AI now faces a similar bar. Memory isn’t just a feature; it’s a prerequisite for trust. Without it, every interaction is groundhog day. That’s why companies like OpenAI, Anthropic, and Google DeepMind are investing in long-context memory and retrieval-augmented generation. They’re recognizing that for AI to serve as a true digital employee or advisor, it must remember the last meeting, the prior questions, the evolving goals of its human counterparts.
Back in 2018, I wrote that “VoiceAI will be as ubiquitous as collaboration,” in the context of Dialpad’s acquisition of TalkIQ. That wasn’t just about voice. It was about augmentation. AI that listens, learns, and improves. But here’s the evolution: augmentation now requires accountability. If AI advises your CFO, it must retain context like a seasoned analyst. If it supports your customers, it must remember prior issues—not merely guess anew each time.
The digital transformation accelerated by the pandemic in 2020 emphasized remote trust. Video calls, document collaboration, digital signatures. But trust was always mediated by humans. Now, in 2025, we’re entering a phase where trust must be mediated by code.
Historical parallels abound. In 2006, I reflected on Robert Scoble’s influence at Microsoft, highlighting how one person could reshape brand perception through authenticity and engagement. Today, AI agents carry similar power. They are becoming the new brand ambassadors, present in every support chat, embedded in enterprise workflows, and mediating billions of transactions. The difference? They don’t earn trust with personality. They earn it through memory, consistency, and verifiability.
Verifiability is the second leg of this trust stool. As the 2021-2022 surge in remote collaboration tools taught us, transparency is non-negotiable. Enterprises learned to distrust opaque systems that couldn’t offer audit trails or reproducible logic. Now, with AI, the demand is the same. Decisions made by AI must be traceable. “Why did it recommend this?” must have a concrete, inspectable answer, not a shrug from a neural net.
We saw this with Skype’s multi-platform expansion in the late 2000s. As I noted back in 2009, Skype wasn’t just racing to release clients for iPhone, Android, and BlackBerry, it was crafting a consistent user experience across devices and networks. That cross-platform reliability, combined with recognizable call quality and familiar interfaces, built the kind of user trust that fueled its massive global adoption. Today, AI must meet the same bar: it needs to work reliably wherever it’s deployed and deliver consistently explainable results—whether in healthcare, finance, or creative work.
Ultimately, trust in AI is not a luxury—it’s the foundation. As I noted years ago in writing about early VoIP rollouts, tech adoption only succeeds when reliability meets usability. That same bar is being applied to AI today. And those who ignore it—who chase only the next GPT model or benchmark—will find themselves replaced not by smarter AIs, but by those who earned trust first.
In the AI trust era, intelligence is expected. Memory is mandatory. Verifiability is vital.
That’s the new hierarchy—and it’s one we should embrace, not fear.