May 2026

Eric Ries, author of The Lean Startup, argues in his new book Incorruptible that companies often lose the qualities that made them valuable because standard governance treats them as instruments for shareholder returns rather than institutions with a purpose. In a conversation with Garry Tan, Ries says founder control, aligned investors and dual-class shares are too fragile to protect a mission once a company becomes valuable enough to attack. His answer is legal and governance design—public benefit corporations, mission-controlled boards, trusts or industrial foundations—that gives a company’s purpose authority beyond any founder, investor or executive.

Cisco’s DJ Sampath says Codex became central to building AI Defense, Cisco’s security product for monitoring and validating AI systems, rather than serving as a peripheral coding aid. According to Sampath, Codex wrote the majority of AI Defense, is writing every new feature for it, and helped move delivery timelines for some features from several quarters to weeks.

Former Coach chief executive Lew Frankfort argues that the handbag maker’s rise from a $6mn New York manufacturer to a global brand was built on controlled distribution, direct customer knowledge and disciplined evidence, not fashion intuition alone. In his account to Masters of Scale, Coach’s “accessible luxury” position emerged from early proof points — catalog data, the Madison Avenue store and measured demand — before becoming an investor story and global expansion strategy. Frankfort frames the company’s durability as a balance of “magic and logic”: brand belief and instinct constrained by metrics, operational control and a willingness to refuse damaging growth.

IBM quantum systems chief Oliver Dial argues that the field is moving from open-ended promise to testable milestones: IBM says it reached quantum utility in 2023, is aiming for verifiable quantum advantage in 2026, and believes error-corrected client systems are plausible by 2029. In a conversation with Craig Smith, Dial says the shift rests on error-correction work that has sharply reduced the overhead needed to build useful logical qubits, while cautioning that advantage must be proved against classical systems rather than asserted from headline qubit counts.

Google chief executive Sundar Pichai told Hard Fork’s Kevin Roose and Casey Newton that Google is at the frontier in some areas of AI and behind in others, particularly long-horizon coding tasks. He argued that the race is moving fast enough for public judgments of leadership to change within months, while defending Google’s broader platform strategy in search, agents, cloud infrastructure and chips. Pichai also treated public anxiety about AI as rational, saying the technology is advancing toward AGI quickly enough that companies and governments need to prepare without either dismissing disruption or slowing progress excessively.

Nvidia’s quarter, SpaceX’s IPO pitch, startup compute shortages, token economics, agent runtimes, and YC’s operating model all pointed to a broader bottleneck around useful AI work. Gil Luria, Joe Kaiser, Sarah Guo, Shruti Koparkar, Ivan Burazin, Liam Hampton, and Tom Blomfield each located that constraint in different parts of the stack, from packaging and GPUs to execution environments and organizational memory.

DA Davidson’s Gil Luria and Switchyard Partners’ Joe Kaiser argue that Nvidia’s latest earnings reinforce a broader market bet on companies controlling scarce AI and space infrastructure. Luria says Jensen Huang used the quarter to show Nvidia’s competitors still lack meaningful traction, while Kaiser says the company’s moat lies as much in TSMC advanced packaging capacity and networking scale as in chips. They extend the same framework to SpaceX, OpenAI and Anthropic: valuations depend on whether these companies can secure the physical capacity needed to turn demand into revenue.

Ivan Feinseth, chief investment officer at Tigress Financial, argued on Bloomberg Technology that the AI investment case is already broader than the hyperscale capex cycle and the next wave of AI IPOs. He pointed to Microsoft’s Azure and Copilot revenue, Adobe’s underrecognized AI content tools, Garmin’s health-and-wellness devices and SpaceX’s long-duration space story, while cautioning that AI-native IPOs may draw strong initial demand but will still have to prove themselves as public companies.

Ross Gerber, co-founder and CEO of Gerber Kawasaki, told Bloomberg that Nvidia’s rivals may be misreading the competitive threat in AI chips. His argument was that Nvidia is not merely defending its data-center GPU franchise, but moving into adjacent markets such as CPUs, edge computing and AI infrastructure for sovereign, enterprise and robotics customers, making competitors more vulnerable to Nvidia than Nvidia is to them.

Bloomberg’s Neil Campling said Nvidia’s latest quarter showed both the strength and the constraint of the AI trade: revenue beat estimates sharply, but expectations and index positioning left limited room for a larger stock reaction. His main point was that Nvidia is trying to shift investor attention from competition in hyperscaler chips to a broader AI infrastructure market spanning agentic AI, physical AI, sovereign AI and fast-growing AI companies. In Campling’s account, Jensen Huang framed that opportunity as potentially reaching $3 trillion to $4 trillion in annual infrastructure spending by the end of the decade.

Bloomberg’s Ed Ludlow reports that SpaceX has filed to go public on Nasdaq under the ticker SPCX, targeting as much as $75 billion at a valuation above $2 trillion, according to people familiar with the matter. Ludlow says the filing presents SpaceX not just as a launch company but as a vertically integrated business built around Starlink, reusable rockets and a proposed network of space-based data centers for AI inference. The pitch, as he describes it, is that IPO proceeds would help fund the capital-intensive infrastructure needed to turn that model into a business.

Bloomberg Originals argues that Keir Starmer’s rapid loss of political authority reflects a deeper British problem: voters want fast improvement, while weak growth, high debt costs, persistent inflation and limited fiscal room leave little capacity to deliver it. Alex Wickham, Lizzy Burden and Alice Gledhill frame the churn of UK prime ministers as a response to that gap, but say changing leaders does not remove the constraints. The bond market, in their account, is now central to the politics, narrowing Labour’s choices and making any challenge to Starmer economically consequential.

Károly Zsolnai-Fehér presents DeepSeek’s “Thinking with Visual Primitives” paper as a meaningful shift in visual AI: not a model that merely sees images, but one that can reason by marking them with points, boxes and paths. He argues that this makes tasks such as counting and maze tracing cheaper, more accurate and easier to inspect, with the paper reporting strong benchmark results while using about 90% fewer visual tokens than many frontier systems. He also cautions that the work is a blueprint rather than a released model, and still depends on triggers and may struggle with fine visual detail or unfamiliar topology problems.

TBPN’s John Coogan, Jordi Hays and guests read SpaceX’s filing as more than a rocket-company IPO: its valuation case increasingly rests on Starlink, defense and especially AI infrastructure, including a large Anthropic compute partnership. They argue that Anthropic’s reported revenue acceleration and OpenAI’s claimed breakthrough on an Erdős math problem strengthen the case that frontier AI is becoming both economically material and technically more capable. The discussion frames the day’s market news as a shift from AI adoption stories to capital-intensive infrastructure, public-market valuation and measurable frontier-model results.

Daytona chief executive Ivan Burazin argues that AI agents need more than disposable code-execution sandboxes: they need fast, stateful, programmable computers that can be configured with different operating systems, resources, tools and persistence. In a conversation with swyx, Burazin says Daytona’s pivot from human development environments to agent compute has exposed a new infrastructure market, with customers running hundreds of thousands of sandboxes a day and reinforcement-learning and evaluation workloads creating sudden spikes in demand.

AI, in TBPN’s latest discussion, is no longer treated mainly as a product demo but as a question of infrastructure, financing and institutional adoption. The strongest evidence came from SpaceX’s AI-heavy IPO framing, Anthropic’s reported move toward operating profit, and OpenAI’s claimed Erdős breakthrough, which the speakers used to challenge the “AI is a scam” critique. The unresolved issue is not whether the technology matters, but how quickly compute capacity, rights regimes, regulation and existing institutions can absorb it.

OpenAI says Codex’s goal mode is now a persistent workflow for assigning the agent a concrete software milestone and letting it work until the stated completion criteria are met, even over hours or days. The feature, available in the Codex app, IDE extension and CLI, turns a `/goal` prompt into the task definition Codex uses to judge when it is done. OpenAI argues the mode is best suited to work with observable endpoints, while still allowing users to steer, inspect, pause, resume or revise the goal as the run progresses.

Kevin Roose and Casey Newton read Google’s I/O announcements as evidence of a company that has regained operational confidence in AI without yet proving frontier leadership. Roose argues Google is leaning on speed, cost, distribution and infrastructure — putting capable models across search, coding, video and cloud tools at enormous scale. Newton is more skeptical: fast and cheap, he says, is not the same as best, and many of Google’s most important product claims remain untested until users can rely on them in real workflows.

OpenAI says Codex plugins can now be shared across a workspace rather than remaining local to one user’s machine. The update lets creators distribute custom plugins to invited users or anyone in the workspace with a link, gives recipients a “Shared with you” area in the plugin directory, and adds direct share URLs for curated plugin pages. The company’s case is that recurring team workflows such as onboarding, pull-request preparation, and Slack triage can be packaged as Codex plugins and reused by teammates from inside the app.

Bloomberg Technology’s Ed Ludlow framed SpaceX’s Nasdaq IPO filing as a test of whether public investors will underwrite Elon Musk’s farthest-reaching claims: a company seeking a valuation above $2 trillion, as much as $75 billion in proceeds and a $28.5 trillion addressable market built largely on AI, Starlink and Mars. Bloomberg reporters and guests said the filing asks investors to look past large losses, debt and Musk’s continuing control, while treating Starship and space-based infrastructure as central to the valuation case rather than speculative side projects. The program placed that pitch alongside Nvidia’s effort to prove AI demand is broadening beyond hyperscalers and possible OpenAI and Anthropic filings that could bring similar public-market scrutiny to frontier AI.

Microsoft’s Liam Hampton argues that coding agents should be chosen by the amount of control a developer wants to keep, not treated as a single all-purpose assistant. In a VS Code demo using one repository, he assigns tests to a local Claude agent for hands-on iteration, a front-end build to a background agent isolated in a Git worktree, and open-source documentation to a cloud agent running through GitHub Actions. His case is that VS Code can act as the control plane for these modes, including Copilot, Claude, and third-party agents.

T. Rowe Price portfolio manager Tony Wang told Bloomberg Tech that Nvidia’s selloff after earnings reflects investors applying an old semiconductor-cycle framework to a company whose AI demand may be more durable. Wang argued that agentic AI, inference, enterprise and sovereign customers, and Nvidia’s ecosystem investments widen the company’s market beyond hyperscale data-center spending. He said that makes Nvidia’s strategy “smart” and its valuation attractive if growth proves less cyclical than the market assumes.

Conviction founder Sarah Guo told Bloomberg’s Ed Ludlow that Nvidia’s compute shortage is showing up directly in startup behavior: young AI companies want current-generation chips first because that is where they discover new capabilities, and only later optimize for cost. Guo said demand stress now spans small on-demand users and buyers seeking $100 million commitments, reinforcing Jensen Huang’s argument that supply remains far behind AI compute demand. She also framed the larger enterprise-AI opportunity as an automation bet whose value may accrue across infrastructure, models and applications.

Former Deputy Secretary of State Kurt Campbell argues in a conversation with Elizabeth Economy that the Iran crisis has weakened the U.S. position in Asia just as President Trump meets Xi Jinping in Beijing. Campbell says the conflict has diverted military capacity and political attention from the Indo-Pacific, exposed limits in allied support, and given China an opening to test U.S. commitments, especially on Taiwan. Economy shares his emphasis on allies but is less convinced that Beijing can convert U.S. disorder into lasting strategic advantage.