Enterprise AI Adoption
How companies buy, deploy, govern, and scale AI across teams, including pilots, procurement, change management, and internal enablement.
Rezolve Frames Hostile Commerce.com Bid Around Stagnant Growth and Merchant Scale
Rezolve AI chief executive Dan Wagner used a Bloomberg Technology interview to defend his hostile bid for Commerce.com as an effort to accelerate Rezolve’s push for leadership in commerce and retail AI. Wagner argued that Commerce.com’s 60,000 merchants are an underused asset held back by weak growth and limited innovation, while Rezolve’s own revenue momentum and anti-hallucination technology could make that customer base more valuable under its control.
Rising Productivity Has Not Settled AI’s Role in the Labor Market
Bloomberg’s Stacey Vanek Smith describes a $400 wager between Stanford’s Erik Brynjolfsson and Northwestern’s Robert Gordon over whether US productivity growth will average 1.8% from 2020 to 2030. Smith says recent data, including 2.9% year-over-year growth in early 2026, suggest productivity is improving, but she cautions that the figures do not show how much is due to AI. The central dispute is whether AI is making workers more productive, or whether layoffs are raising output per hour by reducing labor hours.
Endava Treats Codex as a Lifecycle Agent, Not a Coding Assistant
Endava executives Joe Dunleavy and Mike Krolnik argue that Codex is changing software delivery less by speeding up individual coding than by shifting teams toward supervising generated work across the lifecycle. Dunleavy says small teams can deliver more value in compressed time as their role moves from producing code to overseeing Codex’s output. Krolnik says the tool also helps senior architects turn intent into usable artifacts and enables junior staff to produce more mature work, extending Codex’s role into planning, documentation, diagrams, and client-facing explanation.
Investing Behavior Looks More Like Temperament Than Strategy
Sam Parr and Shaan Puri use a discussion of genetics, investing and startup ideas to argue that outcomes often depend less on information than on fit between temperament and the game being played. Parr reads a Swedish twin study on investing behavior as evidence that biases are partly hard-wired and says the practical answer is to design systems around one’s weaknesses; Puri is more skeptical of genetic fatalism, preferring beliefs that preserve agency. Their exchange returns to Parr’s decision to put most of his post-exit money in the S&P 500 despite Howard Marks’s warning, which Parr defends as a long-horizon plan matched to his own disposition.
Long Lake’s $6.3 Billion Amex GBT Deal Tests AI-Led Buyouts
Long Lake Management co-founder and CEO Alexander Taubman argues that AI can change the economics of services businesses when the buyer owns the workflow, not just the software layer. In a conversation with Elad Gil about Long Lake’s announced $6.3bn take-private of American Express Global Business Travel, Taubman presents the firm’s model as acquiring trusted services companies, embedding its Nexus AI platform into day-to-day operations, and using productivity gains to drive growth, customer service and employee retention rather than short-term cost cuts.
Most AI Startups Should Consider Selling Within 18 Months
Elad Gil, the investor and former operating executive, argues that many AI companies should consider selling within the next 12 to 18 months, not because AI is overhyped but because most companies formed in major technology cycles do not survive them. In a conversation with Tim Ferriss, Gil says the exceptions are the few durable winners — likely including leading foundation-model labs and deeply embedded application companies — while many others may be nearing their best exit window before growth slows, models commoditize their products, or larger competitors move in.
SpaceX-Anthropic Deal Highlights Compute as AI’s Revenue Bottleneck
The All-In panel used SpaceX’s compute deal with Anthropic to argue that frontier AI is now being constrained less by demand than by access to power, GPUs and data-center capacity. David Sacks warned that Anthropic’s reported revenue trajectory could make it a historic monopoly if sustained, while Brad Gerstner pushed back that the market is still too early and competitive for pre-emptive regulation. The discussion turned on whether AI safety concerns justify coordination with government or risk becoming an “FDA for AI,” and whether the AI boom will ultimately show up as measurable productivity and profit for customers buying tokens.
Travel AI Needs Visual Agents, Not Chatbot Booking Flows
Airbnb chief executive Brian Chesky argues that today’s AI chatbots are the wrong interface for travel and e-commerce, even as AI becomes central to how Airbnb operates. In a live TBPN conversation, Chesky said consumer AI’s next wave will depend on richer, more visual and collaborative agentic products, not text-first chat boxes or another round of enterprise software. He also tied Airbnb’s recent growth reacceleration to more hands-on “founder mode” management, saying AI makes operating intensity more important rather than less.
America’s AI Race Requires Silicon Valley to Build for National Interest
Ben Horowitz argues that a16z’s scale gives it responsibilities that now extend into national strategy. In a conversation with David Ulevitch following the firm’s largest-ever fundraise, Horowitz says Silicon Valley should treat U.S. technological leadership in AI, defense, manufacturing and allied supply chains as a national-interest obligation, not a side concern. His case is that if the next technological revolution determines global influence, venture capital and startups have to help America build, adopt and remain optimistic about the technologies that will shape it.
Raising Cane’s Built a Billion-Dollar Chain by Refusing Menu Expansion
Raising Cane’s founder and CEO Todd Graves tells Masters of Scale that the chain’s growth to nearly 1,000 restaurants came from refusing much of the conventional quick-service playbook. He argues that a narrow menu built around chicken fingers, company ownership rather than franchising, resistance to private-equity-style cost cutting, and continued reliance on human service are not constraints on the brand but the operating choices that made it scalable.
Agentic AI Is Making Enterprise Software a Control Layer
ServiceNow president, COO and chief product officer Amit Zavery argues that agentic AI will change enterprise software, but not by letting unconstrained agents replace the platforms that run corporate workflows. In a ServiceNow-sponsored interview, Zavery says the hard problem is turning probabilistic AI into reliable action across regulated, multi-system businesses, with the context, permissions, auditability and governance that enterprises require. His case is that companies such as ServiceNow retain leverage if they make AI production-ready, while software vendors that fail to adapt remain exposed.
AI Is Splitting Product Management Into Builders and Information Movers
In a Stanford CS153 guest lecture, Mike Abbott and Nikhyl Singhal argue that AI is changing product management by eroding the value of roles built around coordination, reporting, and internal information flow. Singhal, founder of Skip and a former product executive at Meta, Google, and Credit Karma, says companies still need product judgment, but increasingly favor hands-on builders who can understand customers, work with technical systems, and make decisions. His broader case is that the product role now depends less on title and process than on company stage, iteration speed, and the ability to build directly.
Compute Supply, Power, and Capital Are Defining the AI Buildout
Arm’s warning on smartphone weakness sat alongside a stronger claim from chief executive Rene Haas: handset softness is concentrated in lower-end devices, while data-center demand is accelerating because agentic AI workloads need CPU orchestration. Bloomberg Technology’s May 7 program used that contrast to trace a broader AI-infrastructure market in which demand is less in question than the ability to secure compute capacity, power, supply chains and capital. Anthropic’s lease of SpaceX compute and CoreWeave’s financing questions pointed to the same constraint: available infrastructure, not appetite for AI, is becoming the limiting factor.
Perplexity Frames AI Agents as Metered Digital Labor
Perplexity chief business officer Dmitry Shevelenko argues that AI agents should be judged less as software features than as metered digital labor: tools users will pay for when they perform economically useful work. In a Big Technology Podcast interview, he makes the case that Perplexity’s computer-use agents, workflow packaging, broad permissions and multi-model orchestration are all part of that shift. The unresolved question is whether users and companies will accept the access, trust and usage-based pricing required to make those agents a real business rather than another AI novelty cycle.
Replit Agent Turned AI Coding Into a $250 Million Run-Rate Business
Replit founder Amjad Masad told Sam Parr and Shaan Puri that Replit’s jump from roughly $2.5 million to $250 million in revenue run-rate was not a smooth growth curve but the result of a market-creation moment. In his account, Replit Agent turned years of stalled platform ambition into a product non-engineers could use to build, deploy and run software, producing about $1 million of ARR on its first day and changing the company’s problem from finding demand to keeping up with it.
Apple Explores Intel and Samsung for U.S. Chip Production
Mark Gurman said Apple has held early talks with Intel and Samsung about using new U.S. fabs to make future A-series and M-series processors, an exploratory move he framed as a supply-chain redundancy question rather than only a political one. Apple still relies heavily on TSMC, primarily in Taiwan, and Gurman described that geographic and supplier concentration as one of the company’s biggest risks. Across the rest of the broadcast, executives and analysts described a similar shift from exposure to execution: AI companies are giving Washington early model access for review, while enterprise adoption is being tested by security, deployment cost and proprietary data advantages.
Thoma Bravo Keeps AI Strategy Model Agnostic as Cyber Risks Accelerate
Thoma Bravo managing partner Seth Boro told Bloomberg’s Dani Burger that enterprise AI is creating parallel problems for companies: faster cyber threats and uncertain deployment economics. Boro said the firm is “model agnostic,” maintaining relationships with OpenAI, Anthropic and Google while using its cybersecurity portfolio to monitor emerging threats. He argued that enterprises will need layered defenses, tighter governance of AI agents and more specific, efficient models rather than assuming general-purpose systems fit every workflow.
AI Panic Gives Way to Company-by-Company Software Stock Sorting
Lauren Webster of Piper Sandler argues that the software market is moving from broad AI panic to a more selective test of execution, durability and exposure to disruption. In a Bloomberg Technology discussion, she said layoffs at PayPal and Coinbase should be read as both a response to investor pressure for profitability and, in some cases, evidence of AI-driven labor displacement. Her framework puts more value on software that is deeply embedded in enterprise workflows and harder to replace.
Airbnb Is Rebuilding Around Identity, Not Homes, for AI
Airbnb’s challenge in the AI era is less a feature rollout than a company reinvention, chief executive Brian Chesky argues in a conversation with Patrick O’Shaughnessy. Chesky says the company has to move beyond a business still identified mainly with homes, rebuild around identity and personal preferences, and do so without damaging a large public platform that hosts and investors depend on. His answer is a more hands-on operating model: fewer abstraction layers, smaller elite teams closer to users, continuous recruiting, and a CEO directly engaged with the work.
Razorpay Turned India’s Payments Friction Into a $180 Billion Platform
In a Startup School India fireside with YC’s Jon Xu, Razorpay co-founder and CEO Harshil Mathur argues that the company’s rise in Indian payments came less from an initial fintech thesis than from staying with a painful customer problem through regulation, bank failures and market skepticism. Mathur says Razorpay turned delays into a moat, customer trust into an operating principle, and early bets such as UPI into openings incumbents missed. His broader case is that founders must keep direct ownership of the decisions that define the company, especially as AI lowers the cost of building and raises the cost of slow judgment.
Luma Is Rebuilding Video AI Around a Unified Multimodal Transformer
In a Stanford CS153 guest lecture, Luma AI co-founder and chief executive Amit Jain argues that generative video is only a staging point toward “unified intelligence”: models that understand and generate across text, images, video, audio, code and tools in a single work loop. Jain traces Luma’s path from Apple-era LiDAR and 3D capture to internet-scale video, saying the company followed the data but now sees prettier clips as insufficient. The destination, he says, is a multimodal AI factory for professional creative and physical work, where human skills, tool use, feedback and unified transformer architectures produce full campaigns, schematics, productions and eventually robotics workflows.
Agent Failure Should Drive Enterprise AI Knowledge Base Curation
Raj Navakoti argues that enterprise AI agents fail less because of model limits or retrieval plumbing than because companies have not made institutional knowledge legible. In his Demand-Driven Context workshop, he proposes building agent-ready knowledge bases from the bottom up: give agents real tickets or incidents, observe where they fail, and turn those failures into structured, validated context blocks. The method, shown through smaller-scope examples and prototypes including work from IKEA Digital, is presented as an incremental curation loop rather than a proven enterprise-scale system.
Enterprise AI Agents Need Harnesses, Traces, and Controlled Runtimes
LangChain co-founder and CEO Harrison Chase argues that enterprise AI agents are becoming an architectural problem rather than a question of adding autonomy wherever possible. In an NVIDIA AI Podcast interview, he says systems such as Claude Code, Manus and Deep Research share a common “deep agent” pattern: an LLM in a tool-calling loop, supported by a reusable harness, workspace, subagents and planning. For enterprises, Chase says trust depends on choosing the right level of autonomy and surrounding agents with observability, evaluation, secure runtimes and continued iteration.