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On April 16, ThinkingAI is launching at the Computer History Museum in Silicon Valley.

The event brings together leaders from Google DeepMind, MiniMax, and leading AI startups and investors to discuss the future of agentic enterprise systems.

Hosted by Brandon Nader (VP, Marketing, ThinkingAI), with panels led by Dasha Shunina (Forbes Contributor). If you’ll be in the Bay Area, don’t miss this awesome event next week.

A new Fortune investigation breaks down what's actually happening inside the enterprise. 54% of workers bypassed their company's AI tools in the past 30 days to finish tasks manually. Another 33% haven't touched AI at all. The combined picture is a workforce that sat through the training, nodded in the all-hands, and then closed the tab.

The trust gap between the people buying AI and the people using it is wider than any productivity number the vendors are quoting. 61% of executives say they trust AI for complex decisions. Only 9% of workers agree. 88% of executives believe their teams have adequate tools. Only 21% of workers say the same. That is not a training problem. That is two groups of people looking at the same software and seeing different jobs.

The cost is showing up on the clock. Workers are losing 51 working days a year to technology friction, up 42% from 2025. WalkMe's CEO put it plainly: fewer than 10% of employees are doing meaningful AI work today. The rebellion is quiet, but the math is loud.

In a piece published this week, Richard Beck of QA argues that organizations succeeding with agentic AI are the ones who stopped thinking about agents as clever tools and started treating them as digital employees. That reframe carries weight. Employees get onboarding, access scopes, accountability, and a manager. Tools get a login and a prayer.

The security problem is structural. Agents operate with real user credentials, which means traditional endpoint detection and DLP systems read their activity as legitimate behavior. Indirect prompt injection lets an attacker hide instructions inside an email or a document, and the agent executes them without knowing it crossed a line. Community skill registries add a supply chain surface that most teams have never threat-modeled.

The builders getting this right are moving controls down to the execution layer. Beck points to policy-based frameworks that enforce restrictions at runtime instead of trusting the agent to behave. The principle worth borrowing: when oversight costs more thinking than the work an agent replaces, the trust model is wrong and it's time to redesign it.

The AI Collective convened a panel at HumanX this week around the question every early AI startup eventually runs into: what happens after your first ten customers love the product and nobody else has heard of it? Vik Ghai (G2C Ventures), Rie Yano (Coral Capital), Murray Newlands (IA Seed Ventures), and Vaibhav from ODDBIRD VC spent an hour unpacking the gap between initial traction and scaled distribution.

The conversation kept returning to founder-led sales as a ceiling rather than a foundation. The panel agreed that the builders who compound are the ones who build channel partnerships and distribution infrastructure while they still have the bandwidth, not after the first growth stall. User engagement as a pivot signal, communicating AI value to skeptical management layers, and knowing when to trust the product over the pitch were all live threads.

On April 3rd, The AI Collective hosted its first Student and Early Careers pitch night in the NVIDIA Auditorium at Stanford's Huang Engineering Center. Eight startups, drawn from a pool of 30+ applicants, presented across semiconductors, healthcare compliance, medical imaging, satellite data analytics, and software engineering infrastructure. Judges Rio Hodges (Antler), Anisha Ladsaria (Hustle Fund), and Sunil Grover (G2C Ventures) did the scoring.

PrecXIMed took the best pitch award. Visibl Semiconductors closed $1.07M from the crowd during the event itself. But the thing worth noting is who filled the room: student founders and early-career builders, people who, a year ago, would have been updating their resumes. That shift in who shows up to a pitch night is worth paying attention to.

As LLMs evolve from chatbots into autonomous agents, the stakes for accuracy have never been higher. However, a significant blind spot remains: most benchmarks used to measure agentic success are stubbornly English-centric. Our latest audit reveals a "Functional Alignment Gap," where machine-translated tasks often break the underlying logic of the agent's tools. To build reliable global AI, we must move beyond literal translation and develop evaluation frameworks that respect cultural nuances and linguistic complexity.

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