May 2026
Chris Williamson, Tania Reynolds, William Costello and Freya India argue that some young women’s pessimism is partly a status phenomenon: material advantage can coexist with incentives to present as injured, caring or constrained. In their account, gendered expectations make women easier to read as victims than agents, while beauty, higher-education status games and social media turn grievance, attractiveness and relationships into public signals.
Chris Williamson and Mark Manson argue that the most useful lessons from therapy and self-help are usually not hidden insights but basic principles people fail to keep in view under pressure. In their account, adulthood depends on repeated reminders about responsibility, boundaries, values and attention, because even familiar truths can disappear during success, stress or crisis. They distinguish between beginners, who may need years of immersion in personal development, and veterans, for whom the work becomes less about novelty than maintenance.
Uber CFO Balaji Krishnamurthy told Bloomberg Tech that the company’s latest forecast reflects sustained demand from riders and travelers despite a more uncertain macro and geopolitical backdrop. He argued that Uber is pairing product expansion, including hotel bookings through Expedia and a larger Uber One base, with tighter operating discipline and AI-driven efficiency. Krishnamurthy framed the quarter as evidence that Uber can keep growing by widening its consumer and enterprise use cases while controlling costs.
Comedian Mike Albo’s TEDNext 2025 performance turns an interrupted talk about smartphones into a staged fraud call from his bank. As a representative reviews Albo’s recent transactions, the routine argues that digital records no longer merely document purchases and locations; they can be made to narrate habits, desires and private embarrassments in the neutral language of customer service.
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.
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.
Bloomberg’s Mark Gurman reports that Apple has held early talks with Intel and Samsung about using new U.S. fabs to manufacture Apple-designed A-series and M-series processors. Gurman says the move is not a break with TSMC, Apple’s longtime chipmaking partner, but an effort to reduce dependence on one supplier and one geography for the components that determine whether Apple can ship its major devices.
Bloomberg’s Mark Gurman says Apple’s reported plan to let users choose outside AI models is a platform move driven partly by weakness in its own technology. Apple aims to make Siri and Apple Intelligence good enough as defaults while allowing services such as ChatGPT, Gemini and Claude to power some features on the iPhone, he argues. Gurman says that could help users in the short term, but it does not remove Apple’s need to build stronger AI of its own for future hardware.
Károly Zsolnai-Fehér of Two Minute Papers argues that DeepSeek V4 Preview is a consequential open-weight AI release because it pairs frontier-adjacent benchmark results with a reported one-million-token text context window and sharply lower long-context memory costs. His case rests less on outright benchmark dominance than on access economics: a freely self-hostable model appears close enough to recent closed frontier systems to change what developers can afford to use. He also stresses the limits: DeepSeek V4 is text-only, degrades near the edge of its context window, and still needs serious hardware at full scale.
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.
Speaking with Craig Smith on Eye on AI, IIT Madras electrical engineering professor Andrew Thangaraj argues that India’s AI talent problem begins with a higher-education system that filters too many students out too early and rewards exam knowledge over usable skills. He presents IIT Madras’s online undergraduate degree in data science — a low-cost, no-JEE program with a rigorous exit standard and project-heavy diploma stage — as an attempt to move the filter from admission to completion. Thangaraj says that model is necessary if India is to build AI capacity at national scale rather than through a handful of elite seats.
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.
At AI Ascent 2026, Flapping Airplanes co-founders Ben and Asher Spector argued that data scarcity, more than compute alone, will determine where AI can create value next. They said the biggest gains so far have come in unusually data-rich domains such as search and coding, while much of the economy — including robotics, trading, science and narrow industrial workflows — lacks comparable datasets. Their proposed answer is to make models far more data-efficient by developing new GPU-level primitives that current frameworks such as PyTorch make hard to express.
Philip Johnston, Starcloud’s co-founder and chief executive, argues that AI data centers could become cheaper in orbit than on Earth if launch costs fall to about $500 per kilogram. His case rests on continuous solar power in a dawn-dusk orbit, avoiding land and battery costs, and using constellations of optically linked satellites for inference workloads. Starcloud’s plan, he said, starts with an orbital GPU proof point and points toward an 88,000-satellite network delivering roughly 20 gigawatts of compute capacity.
At Sequoia’s AI Ascent 2026, ElevenLabs co-founder and CEO Mati Staniszewski argues that audio was an overlooked frontier in 2022 because the AI field was focused on text and images, leaving room for a smaller company to build quickly and monetize early. His broader case is that as AI intelligence becomes more capable, voice becomes the interface problem: the way people will use agents, robots, services, education and healthcare. Staniszewski says the next hard problems are emotional intelligence, timing, authentication and workflow, not merely making synthetic speech sound human.
At AI Ascent 2026, Ricursive Intelligence co-founders Anna Goldie and Azalia Mirhoseini argued that the next bottleneck in AI is the chip-design process itself, and that AI should be used to design the hardware that trains and serves it. Drawing on their AlphaChip work, which Goldie said has shipped in four generations of Google TPUs, they described Ricursive’s plan to rebuild chip-design tools for fast AI feedback loops and turn that tooling into a platform for custom silicon. Their larger claim is that workload-specific chips, and eventually co-designed chips and models, require moving chip design from yearlong expert workflows to automated optimization.
At AI Ascent 2026, Unconventional AI founder and CEO Naveen Rao argued that the current AI compute stack is approaching an energy wall because it is built on an 80-year-old digital computing model poorly suited to intelligence. Rao’s case is that GPUs and matrix math cannot close the efficiency gap with biological brains fast enough, and that AI hardware must instead be rebuilt around physical dynamics, time-domain computation, and architectures that blur memory and processing. He presented Unconventional AI’s coupled-oscillator chip prototype as an attempt to move compute closer to the thermodynamic limits of intelligence per watt.
Tony James, former president and COO of Blackstone, tells David Haber on the a16z Show that his career at DLJ, Costco and Blackstone was defined less by asset class than by a repeatable operating pattern: enter under-scaled franchises before the opportunity is priced, then use culture, disciplined decision-making and structure to let them compound. He argues Blackstone’s rise from roughly $16bn in assets to near $1tn depended on turning a collection of subscale businesses into a firm-level machine, with investment committees, distribution and succession treated as sources of advantage rather than administrative chores.
Oege de Moor, founder and CEO of XBOW, argues that autonomous AI hacking has moved from assistance to real exploitation. In an AI Ascent 2026 talk, he says XBOW’s system reached the top of HackerOne using only black-box access, found a remote code execution flaw in Bing Image Search from a URL alone, and would have been three times more effective with GPT-5. His warning is that defenders have six to nine months before comparable open-weight models make the same capabilities broadly available, including to attackers.
NASA Administrator Jared Isaacman uses an a16z Show interview with Morgan Brennan to cast the US return to the moon as a national-security test rather than an open-ended exploration program. He argues that NASA must compress Artemis from years between launches to months, insert a 2027 risk-reduction mission before 2028 landing attempts, and rebuild internal capabilities the agency has outsourced. Industry still has a central role, in his account, but NASA should set sharper demand signals for a lunar base while reserving its own effort for capabilities no market will fund, including nuclear power and propulsion for Mars.
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.
Laura Burkhauser, Descript’s chief executive, distinguishes generative AI tools for creators from the “slop” she defines as mass-produced content arbitrage. Her case is that Descript’s future depends less on adding AI everywhere than on making editing automation reliable, reversible and useful for recorded human media. That means choosing third-party models by fit and taste, building in-house systems where Descript has workflow data, and treating creator backlash as a product constraint rather than a branding problem.
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.
WorkOS engineers Nick Nisi and Zack Proser make the case that AI “skills” are a practical way to turn repeated agent instructions into portable, reusable workflows. They argue that small markdown-and-script packages can encode team context, constraints, evidence-gathering commands and output formats so agents stop producing generic answers and start following a team’s way of working. Their warning is that skills only help when they are focused, routed correctly, tested against a no-skill baseline and managed like shared software rather than treated as another giant context file.