Venture capital firm 137 Ventures has successfully raised more than $700 million aimed at growth-stage startups. Among its notable investments are SpaceX, Anduril, and Hadrian, supporting innovative companies on the rise.
Salesforce is pioneering a collaborative approach by allowing its customers to influence the direction of its AI product roadmap. The philosophy behind this is straightforward: when one enterprise customer encounters a challenge, it's likely that others face the same issue. This approach ensures that Salesforce's innovations are directly aligned with the real-world needs of its user base.
Robhy Bustami, CEO of BioticsAI, shared with Isabelle Johannessen on Build Mode the challenges and strategies of operating in the tightly regulated healthcare industry. He highlighted how the company stays focused and motivated while overcoming regulatory hurdles and complex processes to advance their mission.
Two rapidly expanding competitors in the legal AI space have secured substantial funding, ventured into each other's markets, and escalated their competition with overlapping advertising efforts.
CMO Don McGuire shares insights into Qualcomm’s strategy to elevate its Snapdragon brand, highlighting the latest campaign developed with creative agency 72andSunny that positions the tech leader at the forefront of consumer technology in the AI era.
For decades, success in business meant having the best ideas—better ideas led to superior products, more customers, and increased profits. Consulting firms and innovation experts thrived on this belief. But with the advent of generative AI, idea generation has become trivial and accessible to anyone. GenAI can produce countless product concepts instantly, making ideas as commonplace as electricity, which is not a competitive differentiator. The real advantage now lies in how companies identify and frame the problems they aim to solve—what’s called “Question Zero.” The challenge is that most organizations fail to see the right problems because users can’t articulate the underlying frustrations they feel. Traditional research methods only capture surface-level issues, leading to incremental improvements and commodification. AI helps by revealing behavioral patterns at scale that humans can’t easily detect, enabling companies to uncover deeper insights for reframing problems. Examples from startups like Cursor, Speak, and Fireflies.ai demonstrate how reframing a problem rather than generating more ideas leads to breakthrough products and market leadership. Established companies such as Netflix, Duolingo, and Procter & Gamble leverage AI similarly to unearth hidden truths about customer behavior, prompting strategic shifts that redefine their businesses. To leverage this advantage, businesses should focus on identifying the gap between what customers say and do, regularly audit their current problem assumptions, and use AI not just for generating ideas but for exploring alternative problem framings. Ultimately, the scarcity in innovation today is not ideas but the insight that guides the right problem to solve, empowering companies to build products and services competitors haven’t imagined.
The vector database landscape is evolving as agentic AI demands more sophisticated approaches than the traditional retrieval-augmented generation (RAG) pipeline. The RAG model, built for human users, is insufficient for agents that perform complex tasks requiring contextual understanding across multiple data sources. Recognizing this, Pinecone introduces Nexus—positioned as a knowledge engine—that compiles raw enterprise data into reusable, task-specific knowledge artifacts. This pre-compilation transforms agent queries by providing structured, context-rich information rather than raw documents, dramatically reducing token usage and improving efficiency. Along with Nexus, Pinecone launches KnowQL, a declarative query language tailored for agents to specify output needs and confidence thresholds. Analysts note that while the concept of upstream reasoning isn't new, Pinecone's ability to operationalize it at scale without bespoke engineering marks a significant advance. Industry leaders like Microsoft and Google are pursuing similar innovations, but experts advise enterprises to prioritize governance, cost control, and deterministic grounding over feature arms races. The shift underscores a fundamental architectural change necessary to support agentic AI workloads effectively, aiming to turn agentic AI from experimental projects into reliable, audited solutions for enterprises.
American Express (AMEX) is developing an innovative system enabling AI agents to shop and make payments on behalf of customers, uniquely leveraging its own closed payment network. Their Agentic Commerce Experiences (ACE) developer kit addresses a major gap in agent-driven commerce by offering full control at the payment layer — a feature uncommon among current protocols.
Unlike other financial networks, AMEX acts as both card issuer and payment network, allowing seamless validation of agent-led transactions within its system. While the kit builds trust through agent identity verification and intent contracts, some aspects of its validation process remain a black box, raising concerns around transparency.
The ACE kit features agent registration, account enablement, intent intelligence including creation of Intent IDs and Proof of Intent Tokens, and usage of single-use payment tokens bound to spending constraints. This structure ensures that AI agents execute transactions strictly within user-defined parameters, enhancing security and accountability.
This approach highlights the critical balance of innovation and risk management in agentic commerce, underlining the importance of trusted systems to support AI-driven financial interactions and safeguard consumers, merchants, and institutions alike.
Jai Das, cofounder and president of Sapphire Ventures, shared his insights at the AI Agent Conference in New York City. He discussed how AI agents are set to disrupt the workforce, highlighting which profession will experience changes first, and expressed that not everyone may adapt successfully in this rapidly evolving landscape.
In this in-depth conversation, Uber CEO Dara Khosrowshahi discusses the company’s strategic expansion beyond ridesharing — including hotel bookings through a new Expedia partnership, personal shopping services, and even snacks in vehicles. He explains Uber’s vision to evolve into an "everything app" by enhancing the travel experience with technology and AI innovations, while navigating risks and trade-offs inherent in a growing platform. Dara shares insights on the current state and future of AI integration at Uber, from coding assistance to customer service automation, as well as the impact of AI token usage on hiring strategies.
The discussion also covers Uber's significant investments in autonomous vehicle technologies through partnerships and equity stakes with companies like Rivian, highlighting the milestones and challenges in bringing robotaxis to market. Importantly, Dara addresses what autonomy means for Uber's drivers, anticipating new complex roles in a mixed human-and-robot driving future. Lastly, he reflects candidly on the limits of AI, the current slow adoption of ride bookings via AI chatbots, and even the emergence of an AI version of himself at Uber for internal use, emphasizing that the best future leadership combines human and AI teaming.
This interview offers an unusually transparent look at how one of the world’s leading tech companies balances innovation, risk, AI advancements, and human impact as it moves into a new era of mobility and travel.
