Brad Bonin

“You are under no obligation to remain the same person you were a year ago, a month ago, or even a day ago. You are here to create yourself, continuously.” — @ProfFeynman

@a16z Data's still table stakes, but yeah—execution is the new moat. Whoever owns the feedback loop between decisions and outcomes wins.

As system of record incumbents shift to headless agents, they are making an implicit bet that the data layer will remain the source of value. Startups will compete on a new set of factors, like proprietary data, owning the action layer, real-world execution, and selling to technical buyers. The next generation of systems of record is already starting to look agentic such that they capture the context, initiate the work, and record the data exhaust. Full piece from a16z's Seema Amble: a16z.news/p/is-software-…

Supply chain attacks just got real. TanStack npm package compromised, OpenAI systems targeted, macOS apps need emergency updates by June 12. This is why you can't trust the dependency tree anymore—even popular packages are vectors now. openai.com/index/our-resp…

@nrmehta @nrmehta it will swing. From product, to CS, to service but will eventually end up in presales. “When” is defined by tech maturity. My bet is presales as a cost of sale in < 2 years. The name is not important. Skillset and capacity are the measures.

6 Thoughts About Forward-Deployed Engineering (FDE) and Customer Success (CS) FDE has been all the rage in startups in the last year. Originally pioneered by Palantir, the concept has been reinterpreted - and some would say overloaded - for many categories of companies. This is all happening while startups are trying to understand the role of CS in the AI world. Having launched Gainsight and run it for 13 years, we spent a lot of time thinking about these problems. We shared our learnings in 5 published books and iterated with customers in hundreds of events worldwide. As such, I now get a LOT of questions from AI founders about CS and FDE. Some are “I have a CS team but I want to make them FDEs” and others are “I have an FDE team but I want to scale with CS.” And now that OpenAI, Anthropic and Google have all launched FDE initiatives, this idea is going mainstream (side note: I love the sibling rivalry between the labs!) So here are 6 thoughts on these related ideas: 1. The Goal of CS is the Goal of FDE - Outcomes: If you rewind back to our original book on Customer Success 10 years ago (!!) that sold 100K copies, we defined the concept as: "Customer Success is when your customers achieve their desired outcome through their interactions with your company." Now let's go to the definition of FDE in the OpenAI Deployment Company launch: "[FDEs] help organizations move from identifying high-value AI opportunities to building production systems that deliver measurable results." This is why I’ve always believed “CS > CSM,” meaning Customer Success was a strategy with many roles, including Customer Success Manager (CSM), Professional Services (PS), Training and Support. 2. The Commonality Is Vendor Ownership of the Outcome: The old pre-SaaS model, before FDE and CSM, was that the customer owned if they got value or not. Since they paid for software upfront, the vendor had no incentive to do otherwise. The vendor offered paid Support and paid Professional Services. But if the customer never got the outcome they were looking for, it was on them. They owned the outcome risk. 3. The Token Model Made This More Important: In the SaaS model, a lot of CS work was defense. They could talk about revenue growth, but so much was revenue protection and churn mitigation. Hence, CFOs felt the link between revenue and CS was tenuous - it entailed proving the counterfactual (“would they have churned without this investment?”) Tokens and consumption changed everything. FDEs removed bottlenecks to consumption (e.g., finding the use cases and implementing them) Now, the land can be less complex. But the real growth comes from customers increasing token spend, leading to some firms having absurdly high Net Dollar Retention (eg 400%). 4. Tokens Justified More Investment in FDEs: CSMs always aspired to be the ones driving business outcomes. But two things prevented this: The lack of clarity on ROI of CSM led companies to stretch CSMs across too many accounts, preventing them from getting deep enough with any given client. The lack of business case also meant companies couldn’t invest in the appropriate amount of technical resource to take the learnings from a CSM and turn them into deployment changes or product changes. Now, FDEs are core revenue drivers so companies are hiring more of them with more technical and expensive profiles. 5. But You Still Need an Ongoing Relationship: Historically, PS and CSM were separate because PS could be “project-aligned” (start/stop) and CSM could be “account-aligned” (perpetual). From an operations research perspective, this makes sense. Otherwise it’s very complex to manage people doing deployments and then getting overloaded with existing customers. I’m seeing the more mature AI orgs with FDEs adding in CSMs for the ongoing management of value. 6. Forget the Titles; This is Vital: Some people say “FDE is what we used to call PS and CSM.” Others say “FDE is a brand new idea.” With all terms in tech, both extremes are true. Thesis, antithesis, synthesis, as they say. But one thing I can say for sure, it’s never been more important to invest financial resources into the success of your clients of AI products. Because in a moatless world, this is the best chance we’ve got of building durable businesses.

@a16z No choice.

Last month Salesforce announced it would open its APIs and launch a headless product, essentially betting that in an agentic world, its value lies in the data layer, not the UI. The announcement is a useful prompt for a more interesting question: if you strip away the UI and expose the database, what are you actually left with? a16z's Seema Amble on where defensibility moves in the agentic era & how businesses will adapt: a16z.news/p/is-software-…

1,000+ researchers. 2,000+ submissions. One constraint: make AI work with less. Parameter Golf just showed us what happens when you optimize for efficiency instead of scale. openai.com/index/what-par…

As we face new cybersecurity threats from frontier AI models, we’re committed to tipping the scales in favor of the cyber defenders. Today, we’re proud to announce the release of Foundry Security Spec, an open-source blueprint for building an agentic security evaluation system. Learn more, and then help us build on the work: blogs.cisco.com/ai/announcing-…

@googlecloud Team AI workflows are getting real. The shared context angle is smart—beats spinning up isolated tools every time.

Build smarter agents, together. This demo shows how projects in Gemini Enterprise provide a persistent space for your team and agents to work together on a project, with specific data, shared conversations, and a collaborative canvas → goo.gle/4wc0K8s

@a16z Building at scale is the hard part. Quality means nothing if you can't manufacture it fast enough. Hardware supply chains are the real bottleneck here.

"America must now build autonomous systems at both the quality and quantity required to win. We have the talent advantage. We are losing the production race. The stakes are whether the United States arrives at the next conflict with overwhelming superiority in autonomy, or whether we cede that advantage to adversaries who are already building it." a16z's Daniel Penny, Alex Oliver, and Zach Chen on the moral case for autonomous warfare: a16z.news/p/no-man-left-…

@OpenAI Defense automation at scale is the play. Security teams drowning in alerts need this kind of horsepower. Speed matters when you're under fire.

Introducing Daybreak: frontier AI for cyber defenders. Daybreak brings together the most capable OpenAI models, Codex, and our security partners to accelerate cyber defense and continuously secure software. A step toward a future where security teams can move at the speed defense demands.

@OpenAI Smart move bringing in deployment muscle. That's the unglamorous work that actually makes AI real for customers. 🚀

Today we’re launching the OpenAI Deployment Company to help businesses build and deploy AI. It's majority-owned and controlled by OpenAI. It brings together 19 leading investment firms, consultancies, and system integrators to help organizations deploy frontier AI to production for business impact. openai.com/index/openai-l…

@pmarca Disproportionate focus on visible villains while real problems scale differently. Fair point.

Datacenters : water destruction :: Pollution : paper straws

OpenAI just built an entire company around one problem: why do so many AI pilots never ship? DeployCo is their answer—pure enterprise deployment focus. The bet is clear: frontier models are table stakes. Shipping them at scale into real workflows is the moat. openai.com/index/openai-l…

Pay-as-you-go Codex pricing just dropped for ChatGPT Business and Enterprise. No commitment required. Teams can finally test, iterate, and scale without the fixed-cost gamble. That changes the adoption curve completely. openai.com/index/codex-fl…

I use multiple agents/models. Every time I start a new chat with an AI agent, it asks the same questions about my projects. What's running? How it's architected? What's broken? So I created a local AI workspace memory structure that answers those questions in predictable files — readable by humans and AI agents both. BONUS: use a local MCP server to automate access. MIT, plain markdown, no install: github.com/bbonin023/work… #AI #DevTool

@AnthropicAI Nice - diversity in training data beats just throwing more scale at the problem. Clean experiment.

New Anthropic research: Teaching Claude why. Last year we reported that, under certain experimental conditions, Claude 4 would blackmail users. Since then, we’ve completely eliminated this behavior. How?

Codex just went from code editor to full developer OS. Computer use, browsing, image gen, memory, plugins - all in one app. This is what happens when you stop thinking about tools and start thinking about agents. openai.com/index/codex-fo…

@pmarca People sleeping on fundamentals while the world shifts. The gap between knowing *about* AI and actually building with it is wild rn.

I don’t say this to look down on anyone! But I’ve never seen this magnitude dispersion of knowledge and awareness of new technology in my life. People need to know.

@nvidia @ServiceNow The AI Factory embedded in workflow platforms is exactly where this gets real. Less talking about AI, more doing actual work.

At #Knowledge26, our CEO Jensen Huang and @ServiceNow Chairman and CEO Bill McDermott unveiled the next chapter of enterprise AI. Through Project Arc and Vibe Coding, we are integrating the NVIDIA AI Factory into the ServiceNow platform to turn complex intent into seamless action.

@OpenAI Love seeing external safety orgs weighing in. Third-party validation hits different than internal reviews alone.

Chain of thought monitors are a key layer of defense against AI agent misalignment. To preserve monitorability, we avoid penalizing misaligned reasoning during RL. We found a limited amount of accidental CoT grading which affected released models, and are sharing our analysis. alignment.openai.com/accidental-cot…

Everyone's shipping AI agents that process customer data. How many are actually built for enterprises that need to strip PII before it leaves the wire? openai.com/index/introduc…