Over the last few weeks on Chain of Thought, I've had three conversations that, taken together, changed how I think about why enterprise AI agents fail.

Michel Tricot, co-founder of Airbyte, showed me a live demo where a single Gong query burned 30,000 extra tokens because the agent had no structured context layer. Richmond Alake, Director of AI Developer Experience at Oracle, walked me through how human memory maps onto agent architecture and argued that memory engineering deserves to be its own discipline. Sudhir Hasbe, President and CPO of Neo4j (episode coming soon), made the case that hallucinations are a data architecture problem, not a model problem, and that knowledge graphs are the fix most enterprises are missing.

Three different guests. Three different companies. The same conclusion: the model is almost never the bottleneck. The way we give it information is.

I want to pull these threads together because I think they form a single argument that most teams building agents are getting wrong.

A quiet failure mode

When I sat down with Michel, he was straight forward - we are poisoning our agents with too much context. Context poisoning: the idea is both straightforward and a bit unintuitive. Aren’t context windows improving? Don’t we want our agents to have more information?

The problems come in once you dig into how that data is managed. When an agent connects directly to APIs without an intermediary data layer, it drowns in irrelevant information. Rate limits force pagination. Pagination forces the agent to process everything sequentially. Token budgets explode. And the agent's reasoning degrades because it's spending capacity managing data access instead of actually thinking about your question.

Think about studying for an exam by reading every textbook in the library, or trying to cram last minute, versus having organized notes for the specific course. You get through the material slower, retain less, and half your mental energy would go to figuring out which books to pick up next, or how to structure the information. You’ve consumed all the information, you just can’t think clearly when you’re drowning in it.

Michel demoed this problem live for me. Same query, two approaches. With Airbyte's context store: 45 Gong calls retrieved in about a minute, reasonable tokens. Without it: the agent had to paginate through every call in the system because Gong's API doesn't support filtering by user. Three minutes. 30,000 extra tokens. And that was a simple lookup.

The scary part: unless they’re paying attention, teams often don't know this is happening. They see an agent that's slow or occasionally wrong and assume they need a better model. They don't. They need better plumbing.

Memory is architecture

Richmond's expertise this week sharpened this idea even further. He argues that we've been through a progression, from prompt engineering (writing magic words) to context engineering (systematically selecting what goes into the context window) to what he calls memory engineering: designing how AI systems remember, forget, and adapt over time.

The distinction matters more than you might think. Context engineering is about what's in the window right now. Memory engineering is about what gets stored externally, how it's retrieved, and when it decays.

Richmond drew a direct parallel to neuroscience. Humans don't have one undifferentiated memory system. We have working memory (your context window), semantic memory (your knowledge base), episodic memory (timestamped interactions), and procedural memory (skills, routines). He built an agent, AFSA, that segments its context window to mirror these categories, and each memory type has its own retrieval logic and decay rules.

Two principles stood out. First: "think memory first." If you want an agent to perform well over long interactions or multi-session tasks, memory needs to be a first-class primitive in your architecture, not an afterthought bolted on when the context window fills up. Second, and this one stuck: "don't delete, forget." Information in a memory system should decay through relevance scoring, recency, and importance weighting. Not through hard deletes. The Stanford Generative Agents paper from 2023 formalized this with a weighted computation of recency, relevance, and importance that lets information naturally fade when it stops being useful.

This matters especially in regulated industries. If you hard-delete, you lose the audit trail. If you forget through decay scoring, the information is still recoverable when the compliance team comes knocking.

Mapping your knowledge with graphs

Sudhir Hasbe brought me the third piece. His argument: even if you nail context management and memory architecture, you still need a way to map the relationships between your enterprise data assets. Hence, knowledge graphs.

He gave an example from EA Sports. They have massive amounts of data in Snowflake, but when a user asks a business question, the agent needs to know which tables matter, what "FIFA" means in context (this year's game? last year's? the organization?), and how different data assets relate to each other. A knowledge graph sitting alongside the data lake acts as a semantic map, letting the agent reason about where to look before it starts querying.

This is why experience matters for us humans - we start to get a sense of what matters. We’ve made a map, and we’ve assigned mental ranks. We have a sense of where to look.

The accuracy numbers are striking for agents when their own information is graphed. Pure vector-based RAG typically lands around 60-65% accuracy for complex enterprise queries. Add a graph layer for metadata and relationships, and customers are getting above 90%. Add reasoning-engine tooling on top of that and you're pushing 95-97%.

This is also how humans operate. We are constantly building our own knowledge graphs, usually without thinking about it. Experience teaches us what matters and where to look. Companies formalize this as organizational memory—you may have heard it called “tribal knowledge.” Enterprise AI needs the same thing.

A fraud detection agent’s decisions need to be accessible to a customer service agent when someone calls asking why their transaction was blocked. That requires shared, structured memory across agents, not just individual agent state.

Fragmentation becomes the enemy.

The three layer data stack for agents

If you synthesize what I heard across these three conversations, a clear architecture emerges for your AI agents:

Layer 1: Context management. How information gets into the context window for any given query. This is what Michel's context store solves. Schema-aware, entity-resolved, pre-structured data that prevents context poisoning before it starts.

Layer 2: Memory architecture. How information persists across interactions, sessions, and time. This is Richmond's domain. Memory types mapped to cognitive functions, with CRUD operations (or CRUF, as Richmond joked, since you don't delete, you forget) and decay logic built into the agent harness.

Layer 3: Knowledge infrastructure. How enterprise data assets relate to each other and how agents discover what they need. This is Sudhir's knowledge graph layer. Semantic mapping, context graphs that capture decision rationale, and multi-hop reasoning across data that lives in dozens of systems.

The teams that are getting agent systems to production are building all three, and thinking deeply about how they interact.

Why This Matters Now

78% of enterprises have AI agent pilots. But a recent survey says only 14% have scaled one to production. Gartner thinks it’ll get worse before it gets better. They predicted last year that over 40% of agentic AI projects will be canceled by 2027. After these three conversations, I have a clearer theory about why.

The industry has been optimizing for model capability. Better reasoning, longer context windows, more parameters. Those improvements are real. But the gap between a model that can theoretically handle a million token context window and an agent that actually uses those tokens well is enormous. That gap is filled by context management, memory architecture, and knowledge infrastructure. The boring stuff. The plumbing.

As Richmond said: memory should be a first-class primitive, not an afterthought.

Yes, we need to engineer context for our agents. Yes, prompting can help.

But building evolving machines requires information retention. Structured memory. Persistent, and also designed to decay.

The teams that figure this out first will be the ones shipping agents that actually work. Everyone else will keep blaming the model.

This essay draws from three recent or upcoming Chain of Thought episodes:

• Michel Tricot (Airbyte) on context poisoning and context stores

• Richmond Alake (Oracle) on memory engineering as a discipline

  • Out this week and one of my favorites! Can’t recommend this episode enough. It’s what triggered this whole essay for me.

• Sudhir Hasbe (Neo4j) on knowledge graphs for enterprise AI (coming soon)

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A single Gong query. That's all it took to context poison his AI agent.

, co-founder of Airbyte (the open source data integration platform with 600+ connectors and a $1.5 billion valuation), demoed it live for me. Two versions of the same agent task: find all sales calls for a specific rep.

The context store version: 45 calls, about a minute, reasonable token usage.

The raw API version: the agent had to paginate through every call because Gong's API doesn't let you filter by user. Three minutes. 30,000 extra tokens all from one query.

Scale that across a real workload and you start to see why so many production agents are slow, expensive, and unreliable for reasons that have nothing to do with the model. You start to see why you’re hitting your Claude Code limits so fast.

Michel is building Airbyte's next product and rebuilding their business around this problem: an agent engine (now in public beta) built on the thesis that agents fail because of data, not models. We explored what this looks like in practice and why he’s fighting for new methods of data storage and context provisioning.

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The RAG death spiral:. Michel walked through the sequence most teams go through: you start with chunk-embed-retrieve. Accuracy is off. So you annotate chunks with metadata ("this sales conversation was about pricing, but let me also tag that Conor was on the call"). Before long, you've rebuilt data centralization with extra steps. The context store approach skips the embedding layer entirely, using schema structure and fuzzy matching to resolve entities across Salesforce, Zendesk, and Gong. Less elegant on paper. More reliable in production.

RAG isn't dead. But the chunk-embed-retrieve default that most teams start with is hitting a wall, and the fix looks a lot more like data engineering than most AI teams want to admit.

Context engineering is a real role: Michel thinks it'll eventually be automated, the same way prompt engineering became mechanical. But right now, someone has to design how information flows to agents: what freshness, what scope, what governance. It's closer to software engineering than data engineering. If someone on your team is already spending their weeks on this, they're a context engineer. They just probably don't have the title yet.

Context is how our agents understand the world. The model is rarely the bottleneck today. The context architecture is.

Watch the full episode 👇 or listen on your app of choice

Connect with Michel: LinkedIn · Agent Blueprint (Substack)

Links mentioned: Airbyte · Agent Engine docs · Agent Connector SDK

Coming up: Richmond Alake, Director of AI Developer Experience at Oracle, on why memory engineering deserves its own discipline and how to architect agent memory systems that work in production. Plus Sudhir Hasbe, President and CPO of Neo4j, on why he thinks knowledge graphs are the missing infrastructure layer for enterprise AI agents.

I’ll be bringing all of this together into my next longform essay in the next couple of weeks.

Cheers,
Conor

In case you missed it: Two weeks ago I sat down with Yujian Tang, the guy who started r/AI_Agents (now over 300K members) and just filed paperwork to launch his own AI venture fund. We got into the mechanics of starting a fund from scratch, why pre-seed AI valuations have doubled in two years, and what two failed startups taught him that successful founders sometimes miss. Listen here if you haven't caught it yet.

They worked at a profitable company. A growing company. Gross profit up 24% year over year. Cash App gross profit up 33%. Block (the company that operates Square, Cash App, Afterpay, and Tidal) wasn’t struggling; it was thriving.

Block CEO Jack Dorsey tweeted his reasoning: AI.

Narrative one: AI made me do it

AI is replacing workers; the future is here, brace yourself. He said more words than that, but it’s essentially what Jack said.

AI, paired with flatter, smaller teams, will enable a fundamentally different company architecture.

This is both true and not at all the whole story.

Regardless of what was true, Block’s stock surged over 20% on the news. The market saw 4,000 fewer salaries against the same revenue guidance and did the math. They saw a CEO truly committing to AI and liked the new narrative.

For those 4,000 people, the math looks very different. They’re updating LinkedIn profiles, texting former colleagues, running the numbers on severance and mortgage payments to see if they’ll be alright on their 20 weeks’ pay plus one week per year of tenure. As anybody looking for a job will tell you, the job market sucks right now.

While everybody was running the numbers, a counter-narrative to Jack’s ‘I had to do this because of AI’ rapidly emerged.

Narrative two: Jack over-hired

In December 2019, Block had 3,835 employees. By December 2022, that number was 12,428. The company more than tripled in three years. Some of that growth came through the $29 billion Afterpay acquisition in 2022, but organic hiring was massive too. Headcount jumped 46% in 2022 alone. Block peaked at 12,985 employees in 2023.

This was the ZIRP-era playbook in full swing: cheap capital, aggressive expansion, hire ahead of the growth curve. Block was hardly alone. Every major tech company did some version of this.

What makes Block’s version stand out is the gap between that hiring and the products it produced. Jack Dorsey is an extraordinary product creator. Twitter, Square’s point-of-sale system, Cash App: three undeniable hits. Few founders can claim even one.

But his track record as a business operator tells a different story.

Twitter is the clearest example. Jack co-founded the company, was pushed out as CEO in 2008, returned to the role in 2015, then spent years splitting his time between Twitter and Square while the platform stagnated. Twitter never evolved its core product meaningfully, monetized primarily through low-margin ads, and became one of the most notoriously bloated companies in Silicon Valley.

Jack eventually stepped down as CEO in 2021. When Elon Musk acquired the company in 2022 for $44 billion, burdened it with debt, renamed it X, and cut roughly 80% of the staff, the platform kept functioning (mostly).

CEOs around the world absorbed two lessons:

1. You can cut your staff far deeper than conventional wisdom and keep going

2. Jack vastly over-hired

Then there’s Square. Square had a dominant low-end point-of-sale system but failed to move upmarket, largely because of a strategic decision to avoid field sales reps. That opened the door for Toast and Clover to seal off the mid-market opportunity. Square only started hiring field sales within the past year, which is malpractice. Online commerce went to Shopify.

Launched in 2013, Cash App pioneered peer-to-peer payments, then watched Venmo and Zelle take mindshare (please Zelle me vs Venmo, it’s so much faster), Robinhood and Coinbase take trading volume, and Affirm and Klarna take buy-now-pay-later. The Afterpay acquisition was orthogonal at best at a massive price, took years to integrate, and still isn’t fully there.

Oh, and he bought Tidal for ~$300 million for some reason?

Tidal was acquired to put Jay-Z on the board. The company continues to invest in proprietary Bitcoin hardware, and something called ‘neighborhoods’ by Cash App—which I frankly didn’t even know existed until I saw complaints about their head of GTM building a startup for the last 8 months instead of doing his job.

Clearly, Block had overhired. But if the output wasn’t there at 10,000 people, then AI tools aren’t “replacing” productive capacity that existed. They’re providing a forward-looking narrative for a company that couldn’t translate headcount into product velocity.

Back in January 2024, Block laid off roughly 1,000 people. In March 2025, another 931 people were let go, 748 open roles were frozen, 193 managers were demoted to individual contributors, and 80 management roles were eliminated entirely. Jack sent an all-lowercase email (read it in full here) to explain:

“none of the above points are trying to hit a specific financial target, replacing folks with AI, or changing our headcount cap. they are specific to our needs around strategy, raising the bar and acting faster on performance, and flattening our org so we can move faster and with less abstraction.”

That was eleven months ago.

But AI has accelerated since then. If you tried AI coding two years back and weren’t impressed, I challenge you to try Claude Code or Codex today. People are building effective AI coworkers. I just spent the weekend having parallel agents in Claude Code wipe out my administrative backlog and automate my work reporting.

So it wasn’t a huge surprise when, in early February of this year, Bloomberg reported Block was planning to cut up to 10% of its remaining workforce as part of an “efficiency push.” This time, the cuts came paired with something new: a company-wide mandate requiring every employee to use generative AI tools daily, with AI fluency built into performance reviews. Employees were also required to send Jack weekly emails detailing their five top accomplishments, echoing the inane Elon Musk-era DOGE requirement for federal workers. Jack reportedly uses generative AI to summarize the thousands of responses.

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The internal reaction was severe.

“Morale is probably the worst I’ve felt in four years,” one employee complained at a recent staff meeting, according to a Wired report. “The overarching culture at Block is crumbling.”

On the AI mandates specifically, an employee told Wired: “Top-down mandates to use large language models are crazy. If the tool were good, we’d all just use it.”

At a company-wide meeting, Jack acknowledged receiving thousands of messages about “widespread concerns about layoffs,” “performance anxiety,” and “the tension between accelerating delivery through AI adoption versus maintaining code quality and engineering rigor.” His response: a sizable portion of the departing employees had been “phoning it in.”

Three weeks later, 4,000 more team members are gone. And the framing has shifted entirely. AI is now the explicit reason.

• March 2025: “We are not replacing people with AI.”

• Early February 2026: “Everyone must use AI daily. It’s in your performance review.”

• February 26, 2026: “AI tools have fundamentally changed what it means to run a company,” and 4,000 people are out.

AI washing

Since the 40% layoff announcement, a term has crystallized around what Block did: “AI washing.” Oxford Economics published a report in January 2026, finding that many layoffs CEOs were blaming on AI were actually corrections for overhiring. Their director of global macro research, Ben May, put it directly:

“We suspect some firms are trying to dress up layoffs as a good news story rather than a bad one, by pointing to technological change instead of past overhiring.”

Bloomberg ran a piece titled “Jack Dorsey’s 4,000 Job Cuts at Block Arouse Suspicions of AI-Washing.” And perhaps most tellingly, there’s this quote:

Challenger, Gray & Christmas tracked AI-cited layoff plans in 2025: roughly 55,000, or about 4.5% of the 1.2 million total. Then January 2026 saw 108,435 US job cuts, the highest monthly total since the 2009 financial crisis, and only 7,600 of them (7%) cited AI.

The skepticism became more pointed as details about the specific cuts emerged.

Block's former head of communications, writing in the New York Times, noted that the layoffs included shrinking the policy team and eliminating diversity and inclusion roles. His assessment: the reorganization "reads like standard prioritization and cost management, not an AI-driven reinvention."

Investor Marcelo Lima put it bluntly:

“Jack Dorsey is using AI as political cover to slash headcount but the reality is Block was incredibly bloated and mismanaged for years. Jack knew it, Amrita knew it, everyone knew it. They did nothing.”

Or to put it more succinctly: “This isn’t an AI story. It’s organizational bloat wearing an AI costume.”

Block’s leaders have not been happy about this, with Jack and others publicly clapping back on the social media platform he built.

But it’s hard to overcome a mismanagement narrative when you cut 4,000 employees five months after throwing a 68 million dollar company party with Jay-Z, Soulja Boy, and T-Pain performing. It’s not a good frame.

Putting the merits of the party aside (T-Pain is great live, for the record), the efficiency data does complicate the narrative.

But gross profit per employee only tells half the story. A different set of efficiency metrics, comparing how much of that gross profit Block actually keeps as operating income, paints a less flattering picture. For every dollar of gross profit, Block keeps 16 cents. PayPal keeps 41. Adyen keeps 53.

So was Block bloated? Compared to its own product velocity, arguably yes. The products stagnated while the headcount tripled. Compared to its fintech peers on a gross-profit-per-head basis, it was already the most efficient company in the group. Both of these things are in the data. The story you tell depends on which metric you lead with.

But to put it all in perspective: Block at under 6,000 employees is just a bit larger than in December 2020, when it had 5,477 people. Jack isn’t cutting to some radical AI-native future headcount. He’s cutting back to roughly where the company was before he tripled it.

So was the layoff AI-washing or AI-driven?

Personally, I think it’s both, and the ratio depends on which department you were in.

What’s happening inside Block

A few weeks ago, I interviewed Angie Jones, Block’s VP of Engineering for AI Tools & Enablement, for my podcast Chain of Thought. She walked me through one of the most ambitious AI deployments in the world: rolling out AI agents to all (then) 12,000 Block employees in eight weeks. She showed me live demos of Goose, Block’s extremely cool open-source AI agent.

A developer mentions a bug in Slack; Goose identifies the code, suggests three fixes, and opens a PR: five minutes, no IDE.

The throughline of that conversation was clear: AI tools paired with smaller, flatter teams are indeed changing what it means to build software and run a company. Almost word-for-word, the language Jack used in describing the decision to cut more than 1 out of every 3 Block team members.

Agents for everyone

The deployment started with an ML engineer named Bradley who was frustrated that building internal AI tools was too slow. Early attempts failed because the team built for engineers, and the broader workforce couldn’t even get past the installation and authentication steps. People got stuck on auth flows, couldn’t configure their environments, gave up before ever using the tool. The AI was fine. Onboarding was a bottleneck.

The breakthrough came when Angie’s team made a critical decision: instead of picking one AI model and forcing it on everyone, they let employees choose. Marketing folks gravitated toward ChatGPT. Engineers preferred Claude. Some teams used local models. Their agent framework, Goose, is fully open source and model-agnostic. It doesn’t care which model you use and has no tokens to sell. That flexibility drove adoption and within eight weeks, all 12,000 employees had access.

The governance side is where Angie’s account diverges most sharply from the typical “AI deployment” press release. People’s money runs through Block’s systems. You can’t just hand every employee an AI agent with access to production databases and hope for the best.

• Her team built data classification systems that define what Goose can and can’t see.

• They implemented MCP tool annotations that distinguish between read-only operations (safe to run autonomously) and destructive operations (require human approval).

• Authentication runs through OAuth with tokens stored in system keychains, a solution Angie herself described as “practical but temporary” while longer-term approaches are developed.

• They red-teamed the deployment and found surprises.

This wasn’t a company that just threw AI at the wall. There was engineering rigor behind it, the kind of rigor you’d expect from a fintech company and the kind that’s usually absent from the “we deployed AI to everyone” announcements.

There was also one particular example Angie told me that I think was indicative of how AI adoption actually works when it’s voluntary rather than mandated. An employee who was not a developer needed to solve a specific workflow problem. She used Goose to build her own MCP server, a tool that connects AI agents to external systems and data sources. When she finished, she asked Angie’s team where to submit it. They told her GitHub. Her response: “Oh, I don’t have a GitHub account. I’m not a developer.”

She built a functioning piece of software infrastructure without identifying as technical. This is not an uncommon story.

Software for everyone

Thirteen thousand people applied to Anthropic’s recent Opus 4.6 hackathon. Five hundred were accepted.

The winners?

A California attorney who built a permit-processing app in six days.

A cardiologist from Brussels who built a patient follow-up tool without knowing how to code.

A road technician from Uganda who built a system for infrastructure assessment.

People who understood problems deeply, and in fact, outperformed peers who understood the technology deeply as wrote.

This weekend, when I needed a tightly scoped Substack MCP server for a project, all I did was ask Opus 4.6, and go back to what I was working on. A few minutes later, it had built me the MCP I needed.

The phrase ‘paradigm shift’ has been overused in recent years, and is overused by AI models in writing, but I am hard-pressed to find other words that describe what is happening in coding today without sounding hyperbolic.

Anybody can build anything with software today, in natural language. The barrier to creation in software has never been this thin. AI coding has advanced with leaps and bounds. If you told me in 2020 what I’d be able to do with AI coding today, I’d think this was AGI. Coding is basically solved.

We have a set of amazing problem-solving tools at our fingertips. Deploying them properly, using them accurately, and effectively? Yes, that takes effort, infrastructure, and consideration. But they are increasingly impressively capable.

In an interview with Wired’s Steven Levy published after the layoff, Jack was more specific about the trigger than in any of his public posts. He pointed to a shift in December, naming Anthropic’s Opus 4.6 and OpenAI’s Codex 5.3 specifically. The models, he said, had gone from handling greenfield projects well to being effective on larger, established code bases. He described this as presenting “an option to dramatically change how any company is structured.”

When Levy asked directly whether this was AI washing, Jack pushed back and suggested that organizational bloat is “a function of inheriting a management structure and a hierarchy all the way back from the 1900s.” Levy’s response was immediate: “You built Block from scratch, Jack. It’s your company.” Jack’s answer: “Absolutely.”

Defending the decision

Since the layoff announcement, one of the few insiders to speak publicly with specific numbers is Cam Worboys, head of product design at Cash App. His account complicates the “AI washing” narrative in ways that are hard to dismiss.

In August 2025, Cash App’s design team shipped 36 PRs. By the last week of February 2026, they’d crossed 1,000 in production. Weekly averages climbed from 21 in December to 54 in January to 124 in February. That’s a 6x increase in output over three months.

Throughput is not everything - but it is certainly something.

Worboys also described the role transformation happening in practice. Product managers are writing copy. Data scientists are shipping features. Designers are functioning as directly responsible individuals more than pixel-pushers. He estimated that two years ago, a business of Cash App’s scale would need around 125 product designers. Today he thinks the number is closer to 25. “The tools aren’t perfect,” he wrote, “but individual leverage has 10x’d.”

His framework for understanding the shift: roughly 80% of work on most roadmaps involves improving things that already exist. Defined surfaces, clear scope, automatable. The other 20% requires invention, uncharted territory where craft and taste determine the outcome. AI handles the 80%. Humans focus on the 20%.

Whether you find this inspiring or alarming probably depends on which side of the layoff you’re on. For the designers and engineers who remain, it’s a vision of higher-leverage, more creative work. If you can get over the shock of losing your colleagues, perhaps there is a bright future ahead.

For the ones who were let go, it’s a senior leader at their former employer publicly arguing that a fraction of the old team can do the job. Both readings are valid. The production numbers, at least, are specific.

As Block’s leadership shifted into a press defensive, Block’s CFO Amita Ahuja gave a detailed defense in an interview. She laid out the gross profit per employee trajectory:

• roughly $500,000 pre-COVID

• flat through 2023

• climbing to about $750,000 in 2024

• $1 million in 2025.

• 2026 goal: $2 million per employee

Ahuja also provided context that most coverage has missed. Goose, Block’s internal AI agent, has been in production for approximately 18 months, predating the layoff announcement by over a year. Since September, she said, developer productivity has increased with a 40% rise in per-engineer AI tool usage for pushing code and features to production. A risk underwriting model that previously took a full quarter to build was completed in a fraction of that time. Block simultaneously raised its 2026 outlook: gross profit expected to grow 18% year over year, with profits climbing 54%.

These are specific, testable claims from a public company CFO. They’ll either hold up over the next four quarters or they won’t.

The question is what all of this evidence actually proves, and what it doesn’t.

Missing math

The productivity numbers are credible. I believe what Angie described. What I’m less certain about is the inferential leap from “employees save 50 to 75 percent of their time on common tasks” to “we need 40% fewer people.” Those are different claims.

Wharton professor Ethan Mollick, one of the most respected voices in AI research and someone who is broadly optimistic about the technology, put it plainly on LinkedIn:

“Given that effective AI tools are very new, and we have little sense of how to organize work around them, it is hard to imagine a firm-wide sudden 50%+ efficiency gain that justifies massive organizational cuts.”

He recommended taking the AI justification “with a grain of salt.” When someone who has spent years studying and advocating for AI in the workplace says the efficiency claims don’t add up, it’s worth paying attention.

Writer Om Malik sharpened the math further. Block’s 2026 guidance implies each remaining employee will need to produce 2.6 times what they did in 2025. That’s a 160% productivity jump in a single year, which seems challenging to say the least.

There will be vast organizational challenges from all the lost team members, and from trying to transition to a new way of working.

Where does the time go

When an employee saves 50% of their time on a task, where does the reclaimed time go? There are three possibilities, and each leads to a very different headcount outcome.

Possibility one: they do more. The same number of people ship more features, clear more tickets, and build more products. Headcount stays flat, output goes up. This is the productivity multiplier story, and it’s the one most engineering leaders would prefer to tell their teams. It’s also the one Angie’s examples in our conversation mostly support. The engineer who goes from bug-to-PR in five minutes doesn’t disappear. She handles more bugs, or shifts to higher-leverage work.

There is a tension in this option.

"If AI truly makes employees twice as effective, the ROI of each engineer increases, meaning you should want more engineers, not fewer. It requires us to accept that there's only a fixed amount of software engineering to be done, no new ideas, no unfinished business, nothing in the backlog." - AICE Labs Analyst

In other words, the "do more" path only fails as a business case if you believe there's nothing left to build. For a company like Block, which lost ground to competitors across nearly every product line, that's a hard case to make.

Possibility two: you need fewer. The same output gets maintained by fewer people. Headcount goes down, output stays flat. This is the story Wall Street is pricing into Block’s stock today. It’s the margin expansion trade. And it’s the story Jack is telling when he says “smaller and flatter teams” can “do more and do it better.”

Possibility three: the role changes. Some tasks within a role get automated. The role itself doesn’t disappear, but it transforms. Fewer people are doing the old version of the job, and more people are doing a new version that looks different. Junior roles compress. Senior roles expand. Management layers thin. This is the most likely outcome in practice and the hardest one to plan for.

It’s also something that many of us believe we are already seeing in hiring patterns.

At Block, it looks like Jack chose door number two for most of the company and door number three for whoever’s left. The 4,000 who are gone aren’t being “replaced by AI” in the literal sense. The remaining 6,000 are expected to produce the same output (or more) with AI tools filling the gaps. Whether that works depends entirely on whether the tools can actually absorb that load and whether the remaining employees have the organizational support to adapt.

There’s also a revealing tension in the reporting. Angie described enthusiastic adoption: employees voluntarily building tools, saving hours, and excited about what they could do. The anonymous employees quoted by Wired weeks before the cut told a very different story: “Top-down mandates to use large language models are crazy. If the tool were good, we’d all just use it.”

Both accounts are likely true. The people closest to Goose and Angie’s AI tools team were probably seeing meaningful wins. Other parts of the organization experienced the AI mandate as surveillance theater layered on top of rolling layoffs. The gap between those two experiences tells you something important about how AI deployment actually works inside large companies. The technology can be impressive, and the rollout can still be a mess. The tool can save time, and the mandate can destroy morale. These things coexist.

The layoffs can be both AI-washing and AI-driven.

The time savings are probably directionally correct. AI tools do make certain tasks dramatically faster. The question is whether you use that efficiency gain to build more, to cut, or to restructure. That’s a leadership decision, not a technology decision.

And the incentive structure just shifted, because every board in America watched Block’s stock jump 22% when they chose door number two.

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What Wall Street’s reaction tells us

Block reported Q4 2025 earnings the same day as the layoff announcement. The numbers were solid: gross profit up 24% year over year, Cash App gross profit up 33%, 2026 guidance of $12.2 billion in gross profit and $3.66 adjusted EPS. The company hit analyst expectations.

But the stock didn’t jump 20% on the earnings. It jumped on the headcount cut.

What Wall Street is pricing here is straightforward: a profitable, growing company just eliminated 40% of its operating cost structure while maintaining revenue guidance. That’s a margin expansion story. Whether AI is “the reason” or “the cover” is irrelevant to the trade. The math works either way.

There’s a third explanation for the layoffs that neither the AI optimists nor the bloat critics are discussing, and it may be the most structural. Anonymous investor @buccocapital laid it out in a longform tweet that’s been viewed over 223,000 times. His argument: calling these “AI-driven layoffs” is air cover for a financial problem that has nothing to do with AI capability.

The core issue is the math of stock-based compensation in a down market. Many software companies staffed up during COVID with equity-heavy compensation packages. Valuations have since reset, with investors now focused on free cash flow minus stock compensation.

For a surprising number of mature software companies, that adjusted number is close to zero. BuccoCapital used Atlassian and HubSpot as examples: 24 and 19 years old, respectively, 14,000 and 9,000 employees, and SBC-adjusted free cash flow that is, in his words, “basically ZERO.”

These companies face a double bind. Technical talent needs to get paid. Stocks are down 60-70% from recent highs. If you keep paying people in stock, dilution continues, and the stock gets punished further. If you switch to all-cash compensation, there’s no free cash flow left.

The only way out is fewer people.

And if you’re going to lay people off, well, AI sounds better than ‘we have financial problems’. Look at how much Block’s stock jumped.

The implications and the template

This framing doesn’t require you to believe AI is overhyped. It says: even if AI is doing everything Jack claims, the layoffs were structurally inevitable because the financial model of paying large software workforces with equity in a down market has stopped working. AI provides the narrative that makes the stock go up instead of down when you announce the correction.

That’s exactly what happened at Block. It will be a template for coming layoffs.

Jack himself said it in his shareholder letter: “Within the next year, I believe the majority of companies will reach the same conclusion and make similar structural changes.” Pinterest, CrowdStrike, and Chegg have already attributed layoffs to AI. Shopify CEO Tobi Lutke told employees in 2025 that teams must prove they can’t accomplish a task with AI before requesting additional headcount. Klarna celebrated AI replacing 700 customer service agents.

What got less attention: Klarna later had to rehire workers because the AI capabilities weren't robust enough to handle the workload. CEO Sebastian Siemiatkowski, speaking at a conference two weeks before Block's announcement, described a more measured approach than his earlier headlines suggested. Klarna reduced headcount by about half since 2022, mostly through natural attrition and a hiring freeze, letting the 20% annual turnover rate do the work over time without mass layoffs. The contrast with Block's approach is instructive. Klarna shrank gradually and discovered the limits of AI along the way. Block cut 40% in a day and is betting those limits won't matter.

The mechanism is clear. Block frames its cuts as an AI-native transformation. The stock pops 22%. Every board of directors in tech watches what happens. The playbook writes itself.

There’s a harder edge to this too. Finn Murphy articulated something most tech commentary won’t touch: big tech made a deal with society. Create a class of well-paid knowledge workers, let your founders and shareholders become wealthy beyond imagination, and society mostly leaves you alone. “Google could cut 90% of the headcount and generate 90% of the revenue,” Murphy wrote. “AI may be the excuse but this has always been an option.”

His warning is worth quoting: the displaced workers from this wave will be more politically potent than any previous wave of automation-displaced labor. They’re credentialed, networked, and accustomed to influence. They won’t accept their displacement quietly. How companies handle this transition will shape whether the regulatory response looks like collaboration or punishment.

The macro shadow

Four days before Jack posted his letter, a piece of financial writing went viral that now reads like it was scripted as prologue. “The 2028 Global Intelligence Crisis” by and is a thought experiment written as a fictional macro memo from June 2028, looking back at how AI-driven layoffs cascaded into a systemic economic crisis. It has over 8,000 likes and 1,600 restacks on Substack. Goldman Sachs and Citadel are circulating it internally. Reddit, policymakers, and institutional investors alike have all read it.

It’s a scenario, and the authors are explicit about that. They disclaim prediction entirely. Yet it’s resonating because it maps a feedback loop that starts with exactly what happened at Block today.

The loop works like this: AI improves, companies cut headcount, savings flow into more AI capability, AI improves further, companies cut more headcount. Displaced workers spend less. Consumer-facing businesses weaken. Those businesses invest more in AI to protect margins. The cycle accelerates. Unlike a typical recession, there’s no natural brake. The cause of the downturn keeps getting better and cheaper.

This cycle operates as OpEx substitution, not CapEx buildout. A company that spent $100 million on employees and $5 million on AI now spends $70 million on employees and $20 million on AI. Total spending is down. AI spending is up. Every company’s AI budget grows while its overall budget shrinks.

The Citrini piece specifically opens with early 2026 layoffs at profitable companies doing “exactly what layoffs are supposed to. Margins expanded, earnings beat, stocks rallied.” Block is that scene, playing out just days after the essay went viral. The piece even discusses payments companies facing disruption from AI agents routing transactions through stablecoins instead of intermediaries. Block is a payments company. The layers of irony are hard to ignore.

Even if AI can’t actually replace all the work companies think it can, the belief that it can is already reshaping negotiations, hiring, and market dynamics. The perception drives economic impact regardless of whether the technology fully delivers.

And then there’s a question of price. Will inference costs go down, or will they climb as inference providers need to raise prices to turn a profit? The current AI cost structure may look like the early days of Uber, where rides were subsidized to drive adoption. The economic case for AI replacement depends partly on AI staying cheap, and that’s an assumption, not a certainty.

You don’t need to believe the Citrini scenario plays out to its full extreme. What you need to do is stress-test your own organization against it. Are you thinking about the second-order effects on your customers and market? Because the boards of public companies are.

What this all means

So what are you supposed to do with all of this?

The instinct will be to pick a side. AI changes everything, or AI is overhyped, and this is just cost-cutting. Resist that instinct. The evidence supports an uncomfortable middle: AI is changing the math on team composition, AND companies overhired during ZIRP and are using AI as a convenient cover, AND the stock market just created a powerful incentive for every CEO to follow Jack’s playbook.

All three of those things are operating simultaneously. Here’s what I’d think about.

The question has shifted. It’s no longer “will AI affect my team’s size?” It’s “on what timeline and in what ways?” If you’re an engineering VP or director, the conversation about your team’s structure is coming whether you initiate it or not. The only variable is whether you’re the one framing it or whether it gets framed for you by a CEO who just watched Block’s stock pop 22%. Start modeling what your organization looks like with 20% fewer people and better tools. Start modeling what it looks like with the same people and 2x the output. Have both plans ready, because your leadership team will ask for one of them within the next twelve months.

Deploy before you cut. The Angie Jones interview is a useful playbook here. The companies that invest in making their existing people dramatically more productive with AI tools will outperform those that just cut headcount and hope the remaining people figure it out. Block’s anonymous employees complaining about top-down AI mandates are a warning: adoption that feels like surveillance breeds resentment, not productivity. Angie’s team got adoption by letting people choose their own models, solving their own problems, and building their own tools. The mandate approach, the weekly emails to the CEO, and the AI-fluency-in-your-performance-review approach produced cratering morale and employees quietly heading for the exits. If you’re deploying AI tools to your team(or any tools for that matter), the method matters as much as the technology.

The most striking part of Jack’s post-layoff interview with Wired wasn’t the defense of the cuts. It was his vision for what comes next. “If I were to build a company today I would have no management hierarchy whatsoever,” he told Steven Levy. “The company itself would be focused on all the artifacts of the work that we’re creating, with an intelligence layer on top that everyone in the company could have a conversation with and query and build intent into.”

“I want the company itself to feel like a mini AGI.”

He described a future where Block’s customers would create their own products and customizations, essentially vibe-coding the company to meet their specific needs.

When Levy suggested this aligned with predictions about half of white-collar work disappearing, Jack hedged on the timeline while doubling down on the direction: “I do believe that people will shift into other types of roles and work. But I can more or less guarantee the role of a company is going to be markedly different.”

When Levy asked whether he was trusting the remaining 6,000 employees to self-organize after losing half their colleagues, Jack said “Absolutely.” He called the shift “existential” and said every company that isn’t building itself as intelligence “is going to face something existential, and it’s going to happen over the next year or two.”

This is either the most clear-eyed strategic vision in tech right now, or it’s a CEO who just cut 4,000 jobs, rationalizing the decision with increasingly abstract language about the future. The distance between those two readings is where every leader should be spending their time.

Watch Block over the next several months. The signals will be specific. If Block executes well at 6,000, every board in tech will treat this as precedent. If they struggle, it becomes a cautionary tale. I’ll be watching:

• Square’s uptime and incident response times. If a 40% headcount cut affects reliability at a payments company, it will show up here first.

• Cash App’s app store ratings and customer support response times. These are lagging indicators, but they’re public.

• Employee attrition among the remaining 6,000. If Block’s best engineers leave because the workload became unsustainable or the culture never recovered from “crumbling,” the AI-efficiency thesis is damaged.

• And product velocity: does Block actually ship new features, or does the smaller team spend all its time maintaining what exists? Jack’s letter promised the remaining team would “do more and do it better.” That’s a testable claim. We’ll see.

The Shopify model deserves serious attention. Tobi Lutke’s directive (prove AI can’t do it before requesting headcount) is a more nuanced version of what Block is doing. It starts with the assumption that every new task should be attempted with AI first, and only when that fails do you add a person.

Over time, that changes team composition gradually rather than through a single traumatic event. It also creates organic learning: every failed attempt teaches the organization where AI actually works and where it doesn’t. If you’re looking for a playbook that doesn’t involve laying off 40% of your company and hoping for the best, this is closer to it.

Whoop offers an even sharper contrast. While Block was cutting 40% of its workforce, Whoop CEO Will Ahmed announced the fitness wearable company would nearly double its 800-person headcount this year.

His commentary on the broader trend was pointed: "There are a lot of companies that are doing layoffs right now and blaming it on AI. But they're actually doing layoffs because the businesses aren't performing particularly well. And it's a convenient excuse." His argument: investing in talent and investing in AI tools are decisions that can be made independently. One doesn't require the other.

Don’t underestimate the social contract. The displaced workers from this wave are credentialed, networked, and accustomed to influence. They’re Stanford-educated directors of product, senior engineers with extensive networks, people who know how to organize, lobby, and write op-eds. Finn Murphy’s framing is vivid, particularly in the context of broad social frustration, the rising prices brought on by illegal tariffs, and ballooning oil prices from war with Iran:

“the Robespierre of today is a forty year old Stanford educated father of 4 who contributed to the Review about to lose his director role in Big Tech.”

He’s describing someone with skills, connections, and motivation. How companies handle this transition will determine whether the regulatory response is collaborative or adversarial. The companies that treat displaced workers with dignity, provide meaningful transition support, and are honest about their reasoning will face a very different political environment than those that frame layoffs as visionary AI strategy while their stock price jumps.

One more thing. If you’re one of the 4,000 people affected by today’s announcement and you’re reading this: the skills you built at Block are in demand. The fact that a CEO decided your role was redundant to an AI tool does not mean your capabilities are redundant. Companies that are thoughtful about AI deployment need experienced people who understand what it takes to build and operate software at scale. That knowledge doesn’t become less valuable overnight.

In the days after the announcement, one story captured something that the stock charts and headcount numbers can't. Naoko Takeda, a data scientist on Block's Cash App team, survived the cuts. She was offered a 90% pay raise to stay. She quit within 24 hours. Her LinkedIn post explaining why went viral: she said she would rather see her peers keep their jobs than personally profit from their departure. Whether you agree with her decision or not, Takeda named something that the productivity metrics and margin expansion math tend to obscure.

There are people on both sides of a layoff who are making decisions based on values that don't show up in a gross-profit-per-employee calculation. The 4,000 who were cut are navigating a brutal job market. The 6,000 who remain are navigating a workplace where the culture was already described as "crumbling" before the cut.

Both groups deserve more than a bumper sticker about the future.

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If you want to understand what Block’s AI deployment looked like from the inside, the full Angie Jones conversation is worth your time. Watch on YouTube or listen on Apple Podcasts or anywhere else you get your podcasts.

Note - this newsletter used to be called Test Lab - to simplify things, I’ve consolidated it under the Chain of Thought name.

Not a tool. Not a chatbot. A system that handles meeting transcripts, Jira tickets, calendar management, and blog drafts, in sequence, with context carrying through from task to task, while Sterling focuses on the work that actually requires him.

He called it MARVIN. Now, MARVIN has nearly 900 GitHub stars. And watching it run live in this episode is different from reading about it, because the impressive part isn’t any single capability. It’s the handoffs.

Most AI demos show you one thing done well. MARVIN shows you what happens between things. The moment a meeting ends, the tickets update themselves. The moment a draft appears, before Sterling has thought to ask for one. That connective tissue is what separates a useful tool from something that changes how you work.

Treat your AI like a coworker

The mental model Sterling used to get there is worth stealing. He treated MARVIN like a new hire, not software. Written rules. Consistent feedback. Specific onboarding. Patience with early mistakes. He wasn’t configuring a product. He was managing someone through a learning curve, just like you would with a newly joined teammate. That approach produced a system reliable enough to trust with 90% of his day.

If you’re stuck with disappointing AI results, you probably skipped crucial onboarding. Context matters.

A few things from this conversation I keep thinking about:

1. The non-technical adoption story. Sterling rolled MARVIN out to 12+ colleagues at Postman, including people with no engineering background. They weren’t slower to adopt. They were faster. No legacy workflows to protect, and the value was immediate: their jobs are mostly text-based coordination work, which is exactly what MARVIN handles best.

2. Sub-agent architecture. Without defined personality rules and explicit constraints, AI assistants default to being generic. MARVIN has a specific tone, style, and task scope built in. Those constraints arguably make it trustworthy. Flexibility without structure produces inconsistency, and inconsistency is why most people abandon these tools.

3. The DIY vs. big tech gap is wider than most people expect. Big tech assistants optimize for everyone. MARVIN is optimized for Sterling’s exact workflow. The difference between “good enough broadly” and “right for me specifically” is significant. Open source on top of a capable model closes it.

This episode is worth watching rather than just listening. Sterling demos MARVIN live, and the screen shares matter.

Also on Spotify, Apple Podcasts, or wherever you listen.

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But while what Sterling built with MARVIN is impressive, it also touches something that makes a lot of people uneasy. Replacement.

Not Sterling replacing himself, exactly. Instead, the question underneath it: if an individual engineer can build an AI that handles 90% of his day in 40 days, what does that mean at scale?

I’ve spoken before about AI agents as ‘async junior digital employees’, and written about the rise of tireless digital workers. The individual version of this story, one person extending their capacity, building something that amplifies their work, feels exciting.

But what happens when it goes far enough that the agent can actually replace the role? Not extend it. Replace it.

We’ve all consumed that kind of dystopian science fiction.

Now, some CEOs are trying it for themselves. The results are complicated.

I’m writing about what that looks like next week. Warts and all.

See you then.

- Conor

Also available on Spotify, Apple Podcasts, or wherever else you listen

Why This Episode

Fin, Intercom’s AI customer service agent now resolves over a million conversations per week(probably more since we recorded). It’s one of the most successful enterprise examples of agents in action.

Fergal Reid is the Chief AI Officer at Intercom, where he has scaled their AI team from 10 people to 55 and has plans to more than double again this year. He’s responsible for Fin, and took us through their thought process, their approach, and the pivots they’ve made along the way.

What makes this conversation particularly valuable is the strategic reversal at its center. Two years ago, Intercom’s position was clear: frontier LLMs are improving so fast, don’t bother fine-tuning. Just get good at prompt engineering and ride the wave. Today, they’re heavily invested in post-training open-weight models, running fine-tuned Qwen models at scale in production, and saving hundreds of thousands of dollars a month in the process.

This episode walks through the full decision process. Not just what they switched to, but why, how they evaluated it, what their training infrastructure looks like, and how they think about defensibility as an application-layer company in an era where the labs keep moving upstack. If you’re building AI products and trying to figure out when to invest in post-training versus riding frontier APIs, this is the episode for you.

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Key Takeaways

1. Intercom saved ~$250K/month by replacing GPT with a fine-tuned 14B Qwen model for a single task

Before any query hits Fin’s RAG pipeline, an LLM summarizes and canonicalizes the user’s question, because end users ask things colloquially, inaccurately, or ambiguously. Intercom was running GPT for this step and spending roughly a quarter million dollars a month on it. They fine-tuned a 14 billion parameter Qwen model to do the same task, saved almost all of that cost, and got more fine-grained control over model behavior in the process. You don’t need frontier intelligence for every step in your pipeline.

2. Most of Fin’s improvement came from surrounding systems, not the core LLM

Fin’s resolution rate has gone from ~30% at launch to nearly 70% today. But the vast majority of those gains didn’t come from upgrading the frontier model at the center (Claude Sonnet as of December). They came from the cloud of 10-15 surrounding systems: a custom retrieval model fine-tuned on production resolution signals, a custom re-ranker built on ModernBERT that outperformed Cohere’s best offering, and the query canonicalization pipeline. Effective context engineering, getting the right information to the model, matters more than the model itself once you’re past a certain intelligence threshold.

3. Higher latency counterintuitively increases resolution rates, including hard resolutions

This was one of the most surprising findings Fergal shared. You’d expect faster responses to create better user experiences. But in production A/B tests at scale (multi-million interaction tests), increased latency consistently led to more resolutions, and not just soft resolutions (where the user disappears). Hard resolutions went up too: where users explicitly confirmed their question was answered. Fergal’s hypothesis is that more latency increases the end user’s perception that the system did real work.

The takeaway for builders: you cannot rely on backtests alone. Production signals surface confounders you’d never anticipate.

4. Per-customer prompt engineering is technical debt disguised as customization

Fergal drew a sharp line between two types of AI products. The first: hand-engineered prompts customized per customer by a forward-deployed engineer. It feels great on week one. The second: a standardized, scientifically optimizable system that gets better over time through rigorous A/B testing. Fergal argued that prompt branching per customer is the AI equivalent of branching your database per customer. It seems responsive, but it’s unmaintainable and prevents systematic improvement. Intercom has bet on the second approach, and it’s the foundation that makes their post-training and evaluation infrastructure possible.

5. The application layer has a real hand to play

When asked whether companies like Intercom can survive if the labs keep moving upstack, Fergal was candid: he doesn’t know for sure. But his thesis is that for specific domains like customer service, model intelligence saturates and a 140 IQ human isn’t meaningfully better than a 110 IQ human at answering support questions. What matters at that point is expertise, vertical tuning, and the product harness around the model. Intercom’s bet is on vertical integration: custom post-trained models, proprietary retrieval systems, and a unified “customer agent” that handles service, sales, and success in one system. Whether the bitter lesson catches up remains to be seen.

Full Transcript

Available on Spotify.

Timestamps

• 00:00 — Intro

• 00:46 — Why Intercom Completely Reversed Their Fine-Tuning Position

• 08:00 — The $250K/Month Summarization Task (Query Canonicalization)

• 11:25 — Training Infrastructure: H200s, LoRA to Full SFT, and GRPO

• 14:09 — Why Qwen Models Specifically Work for Production

• 18:03 — Goodhart’s Law: When Benchmarks Lie

• 19:47 — A/B Testing AI in Production: Soft vs. Hard Resolutions

• 25:09 — The Latency Paradox: Why Slower Responses Get More Resolutions

• 26:33 — Why Per-Customer Prompt Branching Is Technical Debt

• 28:51 — Sponsor: Galileo

• 29:36 — Hiring Scientists, Not Just Engineers

• 32:15 — Context Engineering: Intercom’s Full RAG Pipeline

• 35:35 — Customer Agent, Voice, and What’s Next for Fin

• 39:30 — Vertical Integration: Can App Companies Outrun the Labs?

• 47:45 — When Engineers Laughed at Claude Code

• 52:23 — Closing Thoughts

If You Liked This Episode

Related Episode:

If you liked Fergal’s breakdown of Fin, check out my conversation with Angie Jones (VP of Engineering, Block) on how her team deployed AI agents to all 12,000 Block employees in 8 weeks at the start of 2024. Includes a live demo of Goose, their open-source agent that lets employees choose their own models.

Related Article:

🤫 Sneak peek

For those of you who read to the end, here’s an early look at some upcoming episodes:

• Alex Ratner, Co-founder & CEO of Snorkel AI

• Sterling Chin, showcasing his MARVIN agent chief of staff (think focused OpenClaw)

• many more exciting guests!

If you know someone we should have on the show, reply and let me know.

Chat soon,
Conor

Links & Resources

Connect with Fergal:

• X/Twitter: @fergal_reid

• LinkedIn: linkedin.com/in/fergalreid

Connect with Conor:

• YouTube: @ConorBronsdon

• X/Twitter: @ConorBronsdon

• LinkedIn: linkedin.com/in/conorbronsdon

Products & Tools Mentioned:

• Fin — Intercom’s AI customer service agent

• Intercom — Customer messaging platform

• Claude Code — Anthropic’s agentic coding tool

• ModernBERT — The architecture behind Intercom’s custom re-ranker

• Qwen models — Alibaba’s open-weight model family

• Galileo — Episode sponsor; multi-agent systems guide available free

Subscribe to Test Lab for episode breakdowns, occasional essays, and strategic thinking on AI, developer tools, and the business of technology.

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You’re used to getting occasional essays from me on AI strategy, dev tools, market trends, and mental models. Starting now, I’m also sharing episode breakdowns from Chain of Thought: my podcast where I interview the people actually building the AI infrastructure we’re all navigating.

What to expect going forward:

• 1-2 podcast emails per month (key takeaways, timestamps, full transcript)

• Continued occasional essays when I have something worth saying

• No fluff, no daily spam

I think you’ll find these conversations valuable. The guests are people like Paige Bailey (Google DeepMind), Chip Huyen, and the teams building the tools we’re all learning to use.

First up: episode 48 with Paige on where Google’s AI platform is heading - and a bit on what data centers in space may look like 👀

Also available on Spotify, Apple Podcasts, or wherever else you prefer to listen.

Why This Episode

Paige Bailey leads AI Developer Relations at Google DeepMind, where she sits at the intersection of frontier research and what developers actually ship.

In the past few months alone, her team has launched Gemini 3, shipped Anti-Gravity (Google’s new agentic IDE), and released what they’re calling the largest open-source interpretability toolkit ever. She also found time to build a gamified NASA curriculum using AI Studio, just to prove what’s possible.

This conversation covers the practical (when to use Flash vs Pro, how to think about model selection) and the ambitious (AI-powered robotics on Raspberry Pi, data centers in orbit). It’s an excellent window into how Google is thinking about the next phase of AI development.

Note: Paige demos several tools during this episode. If you’re listening audio-only, I’d recommend checking out the video version on YouTube to see the screen shares.

Key Takeaways

1. “Vibe coding” is real, and Google is betting big on it

Paige demoed Space Math Academy, the gamified NASA curriculum she built using AI Studio’s Build feature.

The workflow: describe what you want in natural language, point at UI elements to request changes (”make this button blue”), and deploy to Cloud Run.

Developers are being unblocked to aggressively ship, non-technical people are being unlocked to build, and Google is seeing massive interest in their new Build features.

2. Project Suncatcher: Google is exploring data centers in space

Google is actively researching orbital data centers that would run on continuous solar power, eliminating the energy sustainability concerns around AI infrastructure. It sounds like science fiction, but Paige positioned it as a serious long-term investment, not a moonshot. The narrative around AI’s environmental impact (which debunked on the podcast in December) may look very different in 5-10 years.

3. The next frontier is “bits to atoms” - and it’s already happening

Google’s robotics work is now accessible to hobbyists. Paige showed us Stanford Puppers—quadruped robots running Gemini on Raspberry Pi that developers can build and program themselves. Google is opening up a robotics trusted tester program.

Her framing: we’ve spent years on AI that understands and reasons. Gemini 3 is about AI that acts, and acting in the physical world is the logical next step.

4. Gemini 3 Flash outperforms Pro for most agentic use cases at a fraction of the cost

The Flash vs Pro decision isn’t just about capability tiers anymore. Paige showed that Flash actually beats Pro on several agentic benchmarks, particularly browser operation and tool use.

Practical guidance: start with Flash ($0.10/1M input tokens) and only move to Pro when you hit specific reasoning limitations. Most developers are over-provisioning.

5. If you’re feeling overwhelmed by the pace of releases, you’re not alone

Paige’s closing advice resonated: even people inside Google feel overwhelmed by the release velocity. Her recommendation isn’t to try to track everything. It’s to build intuition by actually testing the tools yourself. Pick one thing and go deep rather than trying to stay current on everything.

It’s going to keep changing anyway.

Leave a comment

Full Transcript

Available on Spotify.

Timestamps

• 00:00 — Introduction

• 01:24 — Paige’s background & connection to Modular

• 02:29 — Gemini integration across Google products

• 03:11 — Jules, Gemini CLI & Anti-Gravity IDE overview

• 03:56 — Gemini 3 Flash vs Pro: live demo & pricing

• 05:51 — Google’s hardware advantage: TPUs & JAX

• 06:19 — Choosing the right Gemini model

• 09:51 — TensorFlow history & evolution to JAX

• 11:54 — NeurIPS 2025 & Google’s research culture

• 14:14 — Google Brain to DeepMind: the merger story

• 15:34 — PaLM II to Gemini: scaling from 40 people

• 17:55 — Anti-Gravity IDE deep dive

• 20:15 — Gemma open source models

• 20:56 — How to use Anti-Gravity for development

• 24:04 — MCP Protocol & Chrome DevTools integration

• 27:03 — Gemini CLI in Google Colab

• 27:46 — Image generation & AI Studio traffic spikes

• 28:15 — Space Math Academy: gamified NASA curriculum

• 31:39 — Vibe coding: building with AI Studio & Anti-Gravity

• 36:18 — AI from bits to atoms: the robotics frontier

• 37:14 — Stanford Puppers: Gemini on Raspberry Pi robots

• 38:16 — Google’s robotics trusted tester program

• 40:26 — AI in scientific research & automation

• 42:41 — Project Suncatcher: data centers in space

• 45:17 — Sustainable AI infrastructure

• 47:31 — Non-dystopian sci-fi futures

• 48:05 — Closing thoughts & resources

Links & Resources

Connect with Paige:

• Website: webpaige.dev

• LinkedIn: linkedin.com/in/dynamicwebpaige

• X/Twitter: @DynamicWebPaige

Tools & Projects Mentioned:

• AI Studio — Try Gemini models and build apps

• AI Studio Build — Deploy apps from natural language

• Space Math Academy — Paige’s gamified NASA curriculum project

• NASA Space Math — The original curriculum

• Gemini API Pricing

• Stanford Pupper Documentation — Build your own AI-powered robot

• Google DeepMind

Related Episode:

Related Article:

🤫Sneak peek

For those of you who read to the end, here’s an early look at some upcoming episodes:

• Angie Jones - VP Engineering, AI Tools & Enablement - Block (with a live demo of Goose!)

• Intercom’s Chief AI Strategist, Fergal Reid, takes us inside their wildly successful AI agent Fin

• + many more exciting guests!

If you know someone we should have on the show, reply and let me know.

Subscribe to Test Lab for episode breakdowns, occasional essays, and strategic thinking on AI, developer tools, and the business of technology.

Subscribe now

Atlassian’s largest-ever acquisition, $1 billion for developer productivity and intelligence platform DX, signals that the entire developer productivity market is entering a new phase.

With enterprises struggling to quantify returns on AI-driven software development investments, Atlassian’s new acquisition positions them to capitalize on a critical market gap. This acquisition isn't just about one company; it demonstrates that AI measurement capabilities and platform consolidation will reshape who wins and who gets left behind.

The implications extend far across the competitive landscape - creating potentially unexpected opportunities for companies like LinearB and exposing the looming specter of a Microsoft counter-acquisition.

$5 Million to $1 Billion

Two years ago, while working at fellow developer productivity platform LinearB, I wrote a memo warning that DX, newly out of stealth, was positioned to become a serious competitive threat. Their strategic execution stood out from the 20+ other small up-and-coming competitors.

DX was focused and highly intentional. The self-reinforcing elements of their strategy accelerated their progress towards unicorn status:

Strategic Angel Investors as Market Validators

Alongside traditional VCs, DX targeted multiple crucial strategic advisors and angels with both deep knowledge about leading engineering teams and massive influence to help DX go to market and build awareness.

DX’s bevy of developer productivity-aligned strategic angels and advisors included:

• Nicole Forsgren: Researcher and thought leader - the influential force behind the industry-standard DORA (DevOps Research and Assessment) metrics framework, and much more

• : creator of , the #1 Technology newsletter on Substack with 1M+ subscribers

• Will Larson: well-known CTO, author, and writer of the deeply admired tech blog Irrational Exuberance

• Nat Friedman: former CEO of GitHub, 230k+ X followers, etc etc

• : former GitHub CTO, influential investor, CEO & co-founder of Poolside AI).

These are just some of the well-known leaders and technology tastemakers who invested in DX. These weren't just investors; they are software engineering's most trusted voices, providing both credibility and distribution reach that no amount of marketing spend could replicate. The strategic value of these relationships is now even clearer: Jason Warner was just named to Atlassian's Board of Directors, creating a direct bridge between DX's investor network and Atlassian's strategic direction.

This pattern extends beyond just acquiring companies; it's about acquiring strategic networks. Jason Warner's appointment to Atlassian's Board immediately following the DX acquisition isn't coincidental. Successful acquisitions increasingly involve bringing key advisors and strategic thinkers into the acquiring company's governance structure(Reid Hoffman joining Microsoft’s board after LinkedIn’s acquisition is perhaps the most famous example), ensuring that the acquired company's strategic relationships and market insights become part of the acquirer's competitive advantage.

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Founder-Led Thought Leadership That Actually Executed

While many founders talk about thought leadership, DX co-founder actually did it. He built his LinkedIn following to 25,000+ followers by consistently posting valuable content, launched a successful podcast, and published research with GitHub and other orgs central to developers’ perspectives. This is harder than it seems - it’s easy for busy founders to get distracted and fail to fully execute on such a strategy. But when done right, strong founder brands have massive leverage in deals and in shaping public narratives.

DX reinforced this strategy by building internal expertise and authority, encouraging all of their employees to share their perspective and content on LinkedIn, then strategically hiring yet another influential voice in engineering leadership and productivity with CTO in 2023, and even picking up Deputy CTO and developer experience influencer in early 2025. They built up multiple in-house company ‘influencers’ on LinkedIn, and used their already established strategic alliances to validate, support, and distribute DX’s content and approach.

Together, their constellation of influence created an echo chamber effect. If you were at all interested in developer experience or engineering productivity, you would hear DX’s perspective. This second layer of reinforcement built upon and extended DX’s reputation, already sterling from their association with leading investors and allies.

Research-First Positioning

DX has positioned itself as the authority on developer experience measurement, not just another analytics tool. They have published papers, collaborated with GitHub on research, and established themselves as a source of truth for how to measure developer productivity, creating their own Core 4 framework, while associating themselves with some of the most well-known and cutting-edge software engineering teams and tools, such as their recent Claude Code integration. Increasingly, they have driven the conversation around developer productivity measurement.

Crucially, this research-led approach added further backing to their already established strengths:

• Strategic investors: validation via authority of funders and attached brands

• Thought leadership: narrative and persuasive argument

• Research-backed: the numbers and science behind their perspective.

DX emerged from stealth in 2022, years behind established players such as LinearB, Jellyfish, Code Climate, and others. But DX knew that being late meant they needed advantages beyond product development. They systematically built authority and influence, creating a self-reinforcing strategy across their investors, leadership, and research.

Together, these approaches buttress one another - and their message lets them fly, to a rumored $40M+ in ARR in just 3 years out of stealth.

The Message Behind the Strategy

Above all, DX found product-market fit by understanding a fundamental tension in developer productivity tools. They recognized that developers resist being measured, that quantifying code impact requires more than just quantitative signals, and they entered the market with a qualitative-first, survey-driven approach focused on developer experience rather than pure productivity metrics.

This became DX's strategic wedge against established players. While competitors tracked engineering productivity through dashboards and metrics, DX positioned themselves as developer champions - exactly the audience they needed to win over to close enterprise deals. Their DORA-backed credentials provided the authority to back up this positioning, but the developer-first narrative solved a critical adoption problem that had plagued the category.

One persistent challenge for developer productivity tools has been overcoming the perception that they're surveillance systems imposed by management. There’s a reason many of us pushed back at McKinsey’s 2023 claims about measuring developer productivity, and companies can’t typically afford to alienate top technical talent.

DX defanged this concern from day one by framing their approach around improving developer experience rather than measuring developer output. They turned potential resistance into a competitive advantage, making it harder for metrics-focused competitors to win over developer stakeholders who influence purchasing decisions.

The pattern here extends beyond developer tools. Late entrants who become category leaders typically succeed not simply because they build better features, but because they execute better on market positioning, community building, and strategic partnerships. It’s part of what makes them so valuable.

Why $1B Makes Strategic Sense for Atlassian

Atlassian's willingness to pay $1 billion for DX isn't just about acquiring a developer productivity tool but positioning for what CEO Mike Cannon-Brookes calls "Engineering Intelligence for the AI Era."

The timing couldn't be more critical. The broader DevOps market is exploding, growing from $12.5 billion in 2024 to a projected $37.3 billion by 2029. But within that growth, there's a specific gold rush happening around AI measurement and ROI tracking. As companies pour money into AI developer tools like GitHub Copilot, Cursor, OpenAI’s Codex, and Claude Code, they're struggling to answer basic questions: Are these tools actually making developers more productive? Which ones should we invest in? How do we measure ROI?

For Atlassian, this creates a powerful integration thesis. By combining DX's intelligence layer with Jira, Bitbucket, and their other tools, they can offer something no competitor can: complete software delivery intelligence within a single ecosystem, integrated with project management and their collaboration suite. With 300,000+ organizations using Atlassian tools globally and 90% of DX's existing customers already using Atlassian products, the distribution synergies are obvious.

This also follows Atlassian's recent pattern of aggressive AI-era investments. Earlier this month, they acquired The Browser Company for $610 million, signaling they're serious about building and adding AI-native experiences across their platform. The DX acquisition completes the picture. Atlassian now has both AI-powered tools and the intelligence layer to measure their impact.

Increasingly, this puts pressure on Microsoft, GitLab, and other platform vendors to acquire or develop similar capabilities to avoid being left behind. Microsoft poses the most potent threat with its integrated GitHub, Azure DevOps, and Teams ecosystem, but lacks the specialized developer productivity measurement capabilities that DX brings. This gap gives Atlassian meaningful differentiation in the race to quantify AI development investments. I expect Microsoft/GitHub to make a counter-acquisition in the next 18 months.

The DX deal also positions Atlassian ahead of project management competitors like Asana and Monday.com, which lack developer-focused tooling and productivity analytics. As enterprises increasingly demand integrated platforms over point solutions, Atlassian's combined offering of project management, collaboration, and developer intelligence becomes significantly more defensible.

This integrated approach addresses enterprise decision fatigue around developer tooling, where organizations managing dozens of development tools increasingly prefer consolidated platforms. The 90% overlap between DX's customer base and Atlassian's existing users enables Atlassian to serve enterprises that want fewer vendor relationships and less integration complexity, not more specialized point solutions.

The timing reflects a critical enterprise need: IT spending on AI development tools now exceeds traditional productivity software costs by 300-400%, yet most organizations struggle to measure returns on these investments. DX's analytics platform addresses this measurement gap, helping justify and optimize AI-focused development expenditures.

Market Consolidation Patterns

This isn't the first major deal in the space, and it definitely won't be the last.

Harness has been particularly active, acquiring Propelo (now known as Harness Software Engineering Insights) in January 2023 and Armory's assets in January 2024. These deals signal that platform companies are moving aggressively to consolidate specialized tools rather than build competing solutions in-house.

The math behind this trend is compelling. The DevOps market is projected to reach $43.17 billion by 2030, growing at a compound annual growth rate (CAGR) of 21.76%. But within that growth, there's increasing pressure for consolidation, and AI’s influence is accelerating everything, while expanding the potential pool of rewards. Enterprise buyers are tired of managing dozens of point solutions and want integrated platforms to handle their entire software delivery lifecycle. They’re also desperate to quantify and track the massive capex spend they’re putting into AI.

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This creates both opportunity and urgency for remaining independent players. Buyers want alternatives to vendor lock-in, creating openings for cross-platform solutions. However, the window for strategic exits is narrowing as the biggest platform players make their moves, and valuations reach a fever pitch, signaling the potential for the AI bubble to deflate.

The choice facing most remaining companies is stark: get acquired by a platform player, or build enough scale and integration to become a platform themselves. Very few will succeed at the latter, so we should expect additional acquisitions over the next 12-18 months.

This is particularly interesting because each major acquisition changes everyone else's competitive landscape. DX being absorbed into Atlassian's ecosystem means they'll likely stop pursuing integrations with competing platforms like GitHub and GitLab. That creates openings for other players who can serve multi-platform organizations.

Impact on the Developer Productivity Landscape

DX’s acquisition represents stellar validation for the developer productivity space. A $1 billion exit proves that what was once seen as a nice-to-have category is actually a strategic imperative for enterprises seeking to unlock AI-enabled engineering. The return to higher valuation levels also signals that the market has recovered from the 2023 downturn, a marked positive for the entire industry. Previously, only fully AI-native companies had been getting these kinds of price tags.

Interestingly, the acquisition also creates several strategic advantages for DX competitor LinearB and other developer productivity tools:

Ecosystem Constraints Create Opportunities

DX is now locked into the Atlassian ecosystem. Atlassian will use them to solve Atlassian's problems. They'll optimize for Jira, Bitbucket, and Confluence integrations but likely won't pursue deep partnerships with GitHub, GitLab, or other competing platforms. This constraint creates a significant opportunity for cross-platform solutions like LinearB, which can serve multi-vendor environments that DX can no longer easily address within Atlassian's ecosystem.

AI Code Review Differentiation

LinearB has positioned itself well in the expanding AI code review market through its platform approach, but faces formidable competition from specialized players. The space has attracted significant venture investment, with CodeRabbit hitting a $550 million valuation and emerging as a developer favorite through its sophisticated multi-agent architecture that creates dynamic task graphs. Meanwhile, Qodo Merge leads the open-source segment, and GitHub Copilot leverages native integration advantages despite mixed early reviews. Graphite has also developed a cult following (and I had a great conversation with their co-founder on Chain of Thought).

LinearB's differentiation lies in combining AI code review with broader workflow automation and productivity measurement, an integrated approach that will prove valuable in the post-DX acquisition landscape. While pure-play code review tools excel at catching bugs and style issues, LinearB's platform can connect code quality improvements to broader developer experience metrics and business outcomes. This positions them favorably for enterprise customers seeking consolidated tooling rather than managing multiple specialized vendors.

However, execution remains critical. The AI code review market is moving fast, with well-funded competitors rapidly improving their capabilities and new startups emerging seemingly every week. LinearB's success will depend on seamlessly integrating its code review features with its existing productivity platform while staying on the cutting edge of AI software development workflow governance, particularly with the rise of AI agents.

As I’ve written before, faster code reviews are perhaps the highest leverage point for improving your team’s software delivery speed.

Market Segment Opportunities

LinearB's strategic positioning with its new AI adoption-focused Essentials package at $19/month captures smaller teams and growing companies that prefer flexibility over Atlassian's increasingly enterprise-oriented approach. As DX gets absorbed into a larger, more complex sales process, there's an opportunity to capture customers who want simpler, more focused solutions.

Dev Interrupted

2024 is the year GenAI code hits adolescence

Until now, generative AI in software development has been in its infancy. While maturing, it has lacked autonomy and required constant supervision from developers. It could not independently make significant contributions without human oversight. In 2024, the use of Generative AI in software development will reach adolescence – by the end of the year, G…

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2 years ago · 9 likes · Conor Bronsdon

The broader competitive reshuffling also creates urgency for other players. GitHub, which has been organically building developer productivity features, may feel pressure to make an acquisition to compete with Atlassian's integrated offering. GitLab, similarly, might look to acquire measurement capabilities rather than build them in-house.

My prediction: we'll see 2-3 more major acquisitions in this space over the next 18 months as platform players move to establish their positions before the market fully consolidates. Alongside LinearB, Jellyfish is likely to be a key target and could be paired with one of the AI-native code review startups to flesh out a platform ecosystem. Multiple smaller software development analytics startups, such as Swarmia, Sleuth, Allstacks, or Hivel, could also be targets for acquisition.

Pattern Recognition Across Markets

This acquisition fits a pattern we’ve seen across multiple industries: specialized tools getting absorbed by larger platforms during periods of rapid technological change.

In marketing technology, we saw HubSpot acquire dozens of specialized tools to build its integrated platform. In security, Palo Alto Networks has been on an acquisition spree, buying everything from cloud security to identity management companies. The same pattern is now playing out in developer tools.

The driving force is always the same: customers get tired of managing dozens of point solutions and start demanding integrated experiences. Platforms race to consolidate the most valuable specialized tools before competitors do. The result is that independent tools that might have thrived in a fragmented market get absorbed into larger ecosystems.

This acquisition provides a useful framework for thinking about strategic positioning for founders in adjacent categories such as design tools, data platforms, or infrastructure management. Are you building features that a platform company could replicate? Or are you building strategic advantages (community, research authority, unique partnerships) that would be valuable for a platform to acquire rather than compete with?

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The window for strategic exits in many categories may narrow faster than most founders realize. AI is accelerating product development cycles, making it easier for platforms to spin up competing features. But it's still hard to build community, authority, and strategic relationships on top of happy customers, precisely what made DX valuable enough for a $1 billion acquisition. It’s also why the media brand I built for LinearB has proven so valuable for them - both in their own customer acquisition and likely in the case of LinearB’s own eventual acquisition.

Looking Ahead

Developer productivity tools have officially moved from a nice-to-have to a strategic imperative in the AI era. The DX acquisition proves companies will pay significant premiums for tools that help them understand and optimize their engineering investments.

But this is just the beginning of a broader market transformation. In the next few years, the platforms that can provide measurement and optimization across the entire development toolchain, not just within single ecosystems, will win.

For engineering leaders, this means the tools you choose today will increasingly determine your strategic flexibility tomorrow. Betting on single-vendor solutions might give you better integration, but it could also lock you into platform - or AI models - that may not serve your needs as the market evolves.

The lesson for founders and investors is clear: strategic positioning matters just as much as product development in rapidly growing markets. DX didn't win because it built substantially better analytics; it won because it built better relationships, authority, and market positioning.

Or, tell me what I got wrong 😉

Disclosure: Author holds LinearB equity from previous employment. This post was for informational purposes only and is not intended as financial advice.

Meanwhile, 19-year-old founders are building $20M ARR businesses in 12 months using the same AI tools.

The difference isn't the technology. Its execution.

Here's what MIT's GenAI Divide report reveals about the 5% who win:

They purchase specialized tools instead of building them internally (67% vs. 33% success rate). They empower line managers, not just central AI labs. They focus on back-office automation instead of flashy sales tools. Most importantly, they execute small, fast, evaluate, and iterate based on real feedback.

The 95% who fail? They're building elaborate strategy theater.

47-slide decks full of buzzwords. Implementation outsourced to consultants who've never deployed production AI. Timelines that ignore basic technical realities. Internal builds that reinvent wheels badly.

Generic GPTs can excel for individual use cases because of their flexibility, but they stall in enterprise use unless adapted for the reality of delivering consistent value. AI is not a magical silver bullet; it takes customization, context, and orchestration to make it successful.

This pattern extends far beyond AI:

Political campaigns commission expensive polling studies instead of talking to voters. Startups raise money on TAM analysis instead of proving people want their product. Companies hire strategy consultants to avoid the messy work of actually testing ideas.

I should know, I’ve done that work.

What separates the winners across any domain:

1. They solve real problems - Not the problems they want to exist, but the ones that actually cost people time/money/sanity

2. They start small and prove it works - As Paul Graham says, "Do things that don't scale." Pilots with 10 users before platforms for 10,000 - and learn by doing.

3. They execute faster than they plan - Less PowerPoint, more prototypes, constant adjustment based on real feedback

4. They build reliable partnerships - It’s hard going it alone. In complicated technical integrations - which AI is,

Two frameworks to start with:

The Theater Test: If your strategy looks more impressive in PowerPoint than in production, you're building theater. Great strategies are often boring documents focused on measurable outcomes and specific next steps. War is logistics.

The "5% Principle": In any hyped technology category, ~90-95% of participants fail while 5% capture disproportionate value. Your job is figuring out which group you're in before you waste time and money.

What's coming in this newsletter:

• Strategic frameworks for cutting through hype in any industry

• Real deployment stories from AI, politics, and business. Why systems fail and occasionally succeed

• Pattern recognition across domains (the same execution mistakes keep happening everywhere)

• Practical insights for making better decisions under uncertainty, and for navigating the future

Want better strategic instincts? Hit reply or leave a comment and tell me about a decision you're wrestling with. I read every response, and the best questions will become newsletter deep-dives.

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Talk soon,
Conor

P.S. - I went quiet here for 8 months because I was busy with Galileo and overthinking this newsletter strategy. The irony isn't lost on me.

I'm Conor Bronsdon, Head of Developer Awareness at Galileo.ai and host of the Chain of Thought Podcast. I help teams cut through complexity and execute what matters. You can find me on LinkedIn where I share insights with ~10,000 followers.

To many people, AI is typing sentences into ChatGPT, prompting it to converse with you, and helping you with tasks. Others may point out the AI/ML underpinnings of the algorithms that serve us content across the internet, or an AI Overview taking the place of traditional Google search results.

But increasingly, AI is digital employees, building and working on behalf of companies. You may have heard them referred to as AI agents. 

An AI agent is a software application that uses Large Langauge Models (LLMs) to perform specific tasks autonomously. These agents make contextually relevant actions based on sensory input, whether in physical, virtual, or mixed reality environments.

AI agents are designed to perceive their environments, make decisions, and take actions to achieve specific goals. Unlike your interactions with Claude or ChatGPT, they don’t require continual prompting—once set up, they solve the problem they’re built for.

Agents are problem-solving machines, and increasingly they’re becoming digital employees, with the potential to further shift job market dynamics.

AI’s evolving labor impacts

We’re already seeing AI impact labor considerations for major corporations: Amazon CEO Andy Jassy claims their GenAI code assistance Amazon Q has already saved the company 4,500 developer-years of work in foundational code modernization efforts.

Yet many software engineers see GenAI code assistants as solving the wrong problem. Instead of unblocking workflow challenges and crowded calendars that keep engineers from programming, code assistants speed up code creation, the part of their job many developers enjoy the most.

Yes, there is value in accelerating the tedious but critical development tasks of updating foundational software or writing tests. However, that doesn’t change the fact that many engineers don’t think coding assistants work well enough yet. While they hear engineering executives say that this will give teams time for new, innovative feature work, for many engineers, AI is an irritant and a source of stress about a rapidly changing future.

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Will I lose my job? Am I being replaced? Many of us have had such thoughts. With Gartner predicting that enterprise usage of Generative AI tools to develop, test, and operate software will 10x by 2027, many engineers are anxious about what the future of software development looks like. 

For much more on this topic, listen to my conversation with Amir Behbehani, Founder and Chief AI Engineer @ memra 👇

Too much momentum

The angst around AI’s current effectiveness may hinder some AI transformation efforts, but it won’t stop them. From Amazon’s example alone, the efficiency gains in enhanced security and reduced infrastructure costs are estimated to save the company $260M annually. These improvements can be foundational—when I spoke with GitHub's then Deputy CSO, Jacob DePriest, he highlighted that the secure-by-design approach of integrating security filtering into AI code assistants like GitHub’s Copilot can catch and weed out potential vulnerabilities before they emerge.

There’s too much potential upside for enterprises to not invest in GenAI tooling, let alone ignore the opportunity to put their own GenAI applications into production. They can’t risk being left behind.

And agents are a clear next step in AI infrastructure. Yes, it’s great to enhance your team’s productivity: but when you can create tireless digital employees who don’t sleep, don’t eat, and continually solve problems for you—with infrastructure costs but no salary? That’s an entirely new paradigm.

Unlike traditional robotic process automation (RPA), which operates on a set of predefined rules, Agentic AI works with knowledge and context, making real-time decisions and adapting dynamically. This allows agents to go beyond automating workflows and become part of an organization’s operational fabric, executing more than just repetitive tasks.

From deterministic to non-deterministic code

Automation was already a hot topic in software development before AI agent capabilities evolved, with one study finding that over the last two years alone bot-created Pull Requests (PRs) in key open-source repos surged from 5% to 15% . LinearB’s research further found that 13.3% of all pull requests in their data set were bot-created, highlighting the increasing reliance on automation to scale software development.

However, these traditional software bots rely on deterministic code—where given inputs always produce predictable outputs. Rather than executing static code, AI agents interact with dynamic environments, making decisions based on a range of factors, including real-time data and historical context. Agents differentiate by leveraging both long-term memory (such as vector or graph databases) to store knowledge over extended periods, and short-term memory which allows them to adapt in real-time to changes in their environment. This memory system provides the ability to carry out complex, multi-step tasks that require ongoing contextual awareness.

This shift challenges the way engineers approach software development, moving from manual coding tasks to managing agents that continuously improve and adapt to changing environments. And software development will hardly be the last area impacted.

Rapidly addressing ‘black box’ concerns

While the promise of Agentic AI is substantial, it comes with challenges, particularly around security and transparency. LLM decision-making processes are often opaque. However, most AI agents are designed to maintain real-time auditing and traceability. By caching their reasoning processes, these agents provide an auditable trail of decisions, ensuring that organizations can maintain oversight and accountability.

There are other important considerations—LLMs have biases just as humans do but once again, the train has left the station—if a company can code a tireless, problem-solving digital employee, they will. Agents for software development, customer support, sales, and much more are popping up all over.

Trust + safety

We are at a turning point. While there are valid concerns about AI’s water and energy usage, the technology is too promising for builders to ignore. Pandora’s box is open, and we must deal with the consequences—we should be encouraged to see nuclear power rapidly gaining support, given its incredible power generation ratio and how safe it is compared to sources like oil and gas. Nuclear, like AI, has been the victim of bad press—for nuclear, it was fossil fuel company propaganda, for AI it was science fiction.

From simple task automation to full-scale digital employees, AI is transforming how code is written, reviewed, and integrated. AI agents are rapidly being integrated into enterprise workflows, in software, CS, sales, and beyond.

But there is a risk. Just like a human may lie, or misremember, AI can hallucinate, providing incorrect or misleading results. As agents and other AI applications are increasingly put into production worldwide, we need a trust and safety layer for AI that gives engineers visibility into compound AI systems, lets us measure success, and enables evaluations and iteration to improve AI applications—and eliminate hallucinations.

My new job

These are the exact problems Galileo is solving with its Evaluation Intelligence Platform, and it’s why last month I joined Galileo as Head of Developer Awareness.

AI has only begun to reshape the world, more changes are coming whether you want them or not. Companies will continue to try to build digital employees. It may upend the entire labor market entirely. To backstop those changes, we need effective AI evaluations to be a core principle of this technological transformation, and of how we build AI.

So that’s what I’ll be working on for the next couple of years, a thorny, but important problem. If you’ve got ideas, I’d love to hear from you.

What else have I been up to?

Given that it’s been a year since my last edition of this newsletter—👋 that reminds me, welcome new readers!—I want to share a couple highlights from this past year that folks may enjoy.

#1? Penny

That’s right, Katherine and I added a third cat! She’s stolen our hearts, and been a deep distraction from me sitting down to write 😅

Leaving Dev Interrupted

On the podcasting side, I recently left my role as Host of to move to Galileo. However, I’m still appearing in a couple more recorded episodes through the end of this fourth season. Here are three of the DI articles and episodes I’m particularly proud of from this past year:

Dev Interrupted

2024 is the year GenAI code hits adolescence

Until now, generative AI in software development has been in its infancy. While maturing, it has lacked autonomy and required constant supervision from developers. It could not independently make significant contributions without human oversight. In 2024, the use of Generative AI in software development will reach adolescence – by the end of the year, G…

Read more

2 years ago · 8 likes · Conor Bronsdon

Dev Interrupted

Netflix's Blueprint for Building Inspired Engineering Teams | Carol Barrett

On this week's episode, we're joined by Carol Barrett, an Engineering Leader in Consumer Identity and Access at Netflix. Host Conor Bronsdon interviews Carol to learn about Netflix’s blueprint for success when building engineering teams and how they foster a culture where purpose is at the forefront…

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2 years ago · 14 likes · Conor Bronsdon

Dev Interrupted

Analysis: The Quickest Path To Halving Software Delivery Time

In the last 12 months, the engineering landscape has been consumed with efficiency. The rise of AI and the end of zero interest rates has shifted tech’s growth-at-all-costs mindset to doing more with what you have…

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2 years ago · 17 likes · 4 comments · Conor Bronsdon and Ben Lloyd Pearson

Thanks for tuning in for another edition of Test Lab + let me know what you thought by leaving a comment, it’s always great to hear from readers.

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Hope you’re well,

Conor

Your content doesn’t need to be perfect

Yes, I mean you too. I know—particularly when you’re creating content in public, it’s hard to truly believe this. Yet the truth is, creating content and publishing it, then iterating on feedback from that content, is immensely powerful: that practice is how you improve.

Fighting too hard to create perfect content from the start simply creates a blocker to you iterating and learning by doing. It’s why MrBeast famously tells creators who ask for his advice to go create 100 videos and then get back to him:

“When I was a smaller channel, the videos I was making sucked,” Donaldson (aka MrBeast) admitted. “When I meet smaller creators, my advice is just to make 100 videos and improve one thing each video.” Basically, the king of YouTube argues that good content will rise to the top.

It’s also why top startup founders will tell you not to over strategize else you risk analysis paralysis. Building your MVP, and trying to acquire customers will help you find a better path than thousands of hours spent obsessing over your pitch deck.

I absolutely fell into this trap in 2023. Coming into the year, I told myself I’d be sending this email out 1-2 times a month. Instead, the last I sent was.. January 17th. Not only did I break my writing habit, I also limited my upside.

Publishing has unlimited upside

You’ve probably heard of the 1% rule: a common rule of thumb that suggests that about 1% of users create content, 9% engage with it (for example likes - which you should do to this post 😉), and 90% are lurkers who observe.

Yes, this varies from platform to platform and user to user, but it’s broadly true. Just by publishing you’re putting yourself in the 1% of people with major upside from online content.

Don’t underestimate the value of that upside. Take Zoe Sugg for example:

Zoe is a multiple-time founder, author, and internet personality who got her start on YouTube where she amassed nearly 5M subscribers—she’s also grown across other platforms including more than 9 million followers on Instagram.

How did she build such an incredible audience?

She posted. Zoe was an ordinary young woman with a passion for beauty and fashion who started creating lifestyle, makeup, and fashion content from her bedroom to post on YouTube. She learned, and she kept at it.

Today, she’s a globally recognized name in her space who has also branched out to write a series of young adult novels that broke sales records, as well as founding multiple apps for digital templates and photo editing.

She turned her passion into a thriving career by leveraging the opportunities provided by the internet, and she’s far from alone.

Beauty or business, posting lets you grow

Look at . is one of my favorite writers on the internet with more than 212,000 subscribers on Substack. He and his brother Dan have spun out companies, a VC fund, and much more, all from writing on the internet.

How did it happen?

While unemployed with a kid on the way, Packy chose to write a free newsletter for 400 subscribers over finding a job with benefits and a salary. Three years later, Packy's newsletter 'Not Boring' is the #1 Business newsletter on Substack with 187,000 subscribers. And his success doesn't end there.

Consistency drives quality

The act of being consistent at your chosen craft, whether that’s coding, writing, video creation, or anything else, is a massive unlock.

It’s the #1 piece of advice I hear from incredible writers I’ve talked to: set aside time daily to just write, no distractions. Build the habit. Build the space in your life.

Not only do you practice and hone your skill, but if you’re doing so in public (ie by publishing) you’re creating the opportunity for feedback which can be used to iterate and hone your skill.

Stop worrying about perfection. Getting over that fear, and posting, increases your output, and therefore your capacity to get closer to perfection.

Losing the habit is easy

If you’re still reading, you’ve probably gotten the idea: practice more, publish more, and oh yeah—I didn’t do a good job of that thus far in 2023.

Building habits can be hard, but losing them is often easy. It was easy for me to go ‘oh you’re busy with work and you lost weeks of being able to type due to shoulder surgery’ and kill the habit. We all need to be intentional about carving out time for creativity, it’s good for us.

So make sure that you’re not only reminding yourself of why you’re doing what you’re doing but also removing friction from the habit. If you want to get really serious, build in cues and rewards to encourage the habit.

Why am I writing?

I’ll be honest, I’m writing to help me think through the world. Of course, I love that 656 of you find value in my writing enough to subscribe and follow along, but I write because I enjoy it and want to hone my craft: the connections made and the conversations started are a side benefit.

So if like many of us you let the perfect be the enemy of the good, I implore you: go publish your draft.

Shout out to for reminding me last week at DevOps Enterprise Summit that it’s okay to not always be perfectly happy with what I’m putting out—and that writing doesn’t have to feel like a scary chore. I see she wrote something similar in this week 👀

What else is going on?

2023 has been a whirlwind so far—shoulder surgery in February and the resulting PT have been a journey, and I’ve been busily recording podcasts and writing articles for , in between spending time on the road at conferences. I was also honored to be named one of 67 CTO Influencers You Should Follow by Lemon.io.

Events

• I recently joined the Gradle team at DPE Summit in SF, and had a chance to sit down with their CTO Justin Reock to talk about McKinsey’s Developer Productivity framework, and why it has major issues 👇

• Last week I was in Las Vegas for DevOps Enterprise Summit and had the opportunity to record 15 interviews for from the convention floor, including a session with Google’s DORA team, a chat with Accelerate co-author Gene Kim, GitHub’s Deputy CSO, and more - look for those to come out over the next few months.

• We’ll also be recording at LeadDev West Coast in Oakland October 16th-17th and LeadingEng on the 18th so if you’ll be in the bay next week, let me know! I’d love to meet up.

Other Content

For the software engineers and leaders who’ve subscribed, I recently wrote a couple of articles that you might find valuable:

Dev Interrupted

How Healthy Engineering Teams Invest Their Time

Successful engineering-led growth can only happen when the people leading dev orgs do two things: Improve the efficiency of their engineering teams so that they focus as much time as possible on building, solving problems, and improving features. Ensure that the work their teams spend their time on aligns with company goals…

Read more

3 years ago · 17 likes · Conor Bronsdon

Dev Interrupted

Are You Leading Your Dev Team in the Wrong Direction?

In college, my buddy Ethan did something both hilarious and instructive. He and his friends decided to drive from New York to New Orleans for Mardi Gras. They took shifts at the wheel so they wouldn’t have to stop. Somewhere in Virginia, one guy took over to drive through the night. Ethan woke up the following morning, and asked the driver, “How’s it goi…

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3 years ago · 9 likes · Conor Bronsdon

Plus here are two of the podcasts that I think y’all might enjoy from the 30~ I’ve done this year:

I’m hoping my incredible readers, both new and old, will forgive me for the infrequency of these emails, and that you continue to find value in them.

Thanks for reading, as always, you can reach out to me in the comments or on LinkedIn to provide feedback—I love hearing from you.

Cheers,
Conor

But it’s not just tech giants experiencing slow growth and cutting headcount. Private software companies are reacting to challenging market conditions by reducing the size of their workforce. We’re halfway through January, and layoffs are flying fast and furious.

Where did we go wrong?

Investors couldn’t see the party stopping.

In the heady investment boom of 2021, the markets were flush with cash—partly due to cash payments to Americans for COVID relief and major checks cut to businesses as PPP loans. We’d managed to push on through the shock of COVID and keep valuations high—too high. It felt like we were minting a new billion-dollar tech unicorn every other day. The Federal Reserve had interest rates at a historic low, and everyone had access to capital. We were in the midst of a nearly 15-year bull market.

Then the checks stopped flowing out, the cost of goods inflated, and the Fed hiked interest rates.

The music stopped, and quickly.

No more growth at all costs

If you’re a private software as a service (SaaS) business, your job is to grow quickly. If you talk to venture capitalists, they’ll tell you that a successful SaaS business selling to other businesses (B2B) should triple its annual reoccurring revenue (ARR) in one year, triple again the next, then double, and double again.

Here’s what that looks like for a company that starts with $1M in yearly revenue and some initial product-market fit:

• Year 1: $1M x 3 = $3 million ARR

• Year 2: $3 million x 3 = $9 million ARR

• Year 3: $9 million x 2 = $18 million ARR

• Year 4: $18 million x 2 = $36 million ARR

By the end of year 4, they expect that company to have at least grown 36x. To do that, companies were burning through cash and hiring rapidly.

But were they doing that efficiently?

The new (old) metrics that matter

The open secret to hypergrowth is that these companies usually aren’t profitable. They’re fueled by venture capital dollars with a growth-at-all-costs mindset, and too many companies grow inefficiently while trying to capture market share. The rationale is that with reoccurring revenue models, if you own enough of a market and can retain your customers (ie, make your product sticky), you’ll make lots of money in the long run.

Now, investors see choppy economic waters continuing for months, if not years. Consumers are tightening their wallets, with a cascade of revenue impacts across businesses. It’s getting a lot harder to sell software to other businesses—and to retain the customers you just spent so much acquiring.

With revenue and growth dipping, venture capitalists—who form the boards of these startups while they’re private—start looking more closely at efficiency metrics.

They ask:

• What’s your Customer Acquisition Cost (CAC) & CAC payback period?

• What’s your revenue churn rate?

• What’s your sales efficiency?

• What’s your monthly cash burn?

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CAC & CAC Payback

CAC = Total $ spent on sales & marketing over a period / the number of customer acquired within period

CAC measures the amount you spend on sales, marketing, and other associated costs to acquire a new customer. Flush with cash; many SaaS companies had been relying on high CAC spending, with the expectation that the lifetime value of the customer was worth it—and the assumption that they’d be able to raise more money at a good valuation to continue to fuel growth.

The second important dimension of CAC is the payback period. With B2B businesses, it will often take months to years to return the cost of acquiring their customers. The faster your business recovers CAC, the faster it can reinvest in acquiring more customers and continue to seize market share. The lower your CAC payback period, the more excited investors likely are—sub 12 months is considered especially strong as most B2B contracts are annual at least. But if 30%+ of your customers only stay a year and your CAC payback period is longer than 15 months (considered solid), you’ve got a problem.

Revenue Churn rate

Churn rate = (# of churned customers in a period ÷ total # of customers to start period) × 100

A crucial part of understanding if your company’s CAC and CAC payback period are acceptable to investors is your churn rate—both logo churn (ie, the number of customers/accounts leaving your service as a percentage of your overall customer count) and revenue churn (the amount of money leaving your revenue stream each month as a percentage of overall revenue).

In the current cost-cutting and efficiency environment, technology costs, particularly reoccurring subscriptions, are on the chopping block along with employee salaries. Hence many SaaS companies have seen churn rates increase, and their CAC payback period suddenly looks unsustainable. With pressure to cut costs and to cut CAC, companies cut headcounts.

The next year will be a proving ground—companies that can’t lower their CAC & churn rate may not survive the next year—or will be acquired by larger cash-rich companies.

It’s also important to understand that the monthly churn rate will compound over time, creating worsening problems. Sure, your 2% monthly revenue churn rate doesn’t seem like a lot, but annualized, that’s nearly 25% of your revenue that needs to be replaced by new growth just to maintain the same revenue figures—and for companies still looking to raise more money by hitting that next 2x growth year, that just creates a steeper hill to climb.

Sales Efficiency, aka the Magic Number

Sales Efficiency = amount of new revenue generated for every dollar invested in sales and marketing over the prior period

The ‘Magic Number’ or Sales Efficiency—is here to tell you how efficiently your company is gathering value by comparing the revenue you accrue over a given period to the sales + marketing spend over that period. It’s a crucial indicator of your overall company efficiency.

In the Scale Studio dataset of 1,000+ growth-stage SaaS and cloud businesses, the long-term median Sales Efficiency hoovers around 0.7.* That means that a typical SaaS startup generates $0.70 in new ARR for every $1.00 it spends on Sales & Marketing.

Occasionally, you’ll see companies with incredible efficiency models and execution that go above the number 1 in sales efficiency—these are the kind of companies that investors are beating down the doors of. They’re rare and have unique value propositions in their go-to-market model.

What happens if investors don’t like these numbers?

The short answer is reductions in workforce aka layoffs. The longer answer is that cash burn (operating cashflow less capital expenditure) is crucial context to the above metrics and that companies with high rates of cash burn look to reduce those by cutting expenses—of which salaries are generally the largest.

It’s also important to remember that this cost-cutting is part of why B2B companies are seeing increasing churn rates as companies look to cut technology costs. The companies doing the most cuts are generally those that faced the most investor pressure to grow rapidly during prior years—and built unsustainable headcount models with the expectation of low customer churn and another round of funding just around the corner.

If you found this information useful or informative - return the favor by sharing this post with someone else who you think might find it valuable ⬇️

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Dive deeper into SaaS and engineering metrics

• For a broader look at SaaS metrics than what I have above, check out David Sacks & Ethan Ruby’s seminal post on SaaS KPIs.

Bottom Up by David Sacks

The SaaS Metrics That Matter

One of the best features of SaaS businesses is how easy they are to measure. Only a handful of metrics really matter. This post breaks down those key performance indicators (KPIs), and provides the benchmarks that we at Craft like to see at the Series A stage in order to lead a new investment…

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5 years ago · 224 likes · 24 comments · David Sacks and Ethan Ruby

• I also interviewed Laura Tacho, VP of Engineering and Leadership Coach on the Dev Interrupted Podcast, about what metrics engineering leaders need to understand 👇

  Dev Interrupted

  Startup Growth & Metrics: What to Measure & When w/ VP of Engineering & Leadership Coach, Laura Tacho

  As your startup scales it's not enough to know what to measure - you have to know WHEN to measure. Metrics that are important in one growth phase might not be in the next. To help us make sense of it all, we invited Laura Tacho onto the show. Laura is a VP of Engineering & Leadership Coach, and an expert when it comes to startup growth and metrics, havin…

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  3 years ago · 2 likes · Dev Interrupted and Conor Bronsdon

What else I’m paying attention to

• A thought-provoking look at Big Tech and how they bring in revenue by Peter Yang, who also writes a great newsletter on the creator economy A couple of observations:

  • Microsoft has an intriguing position if they can complete a purchase of 49% of OpenAI and use it to enhance their diverse product revenue streams

    • Google has to be highly wary, given it’s reliance on Search and the potential Bing + ChatGPT integration

    • Pushing major amounts of AI onto Azure would further entrench their position as the #2 in the cloud market or even let them threaten AWS in size

  • Apple’s hardware/software orientation and incoming VR/AR product line could leave them in a great position to partner with some would-be rivals.

• Excited to see the FDA grant Accelerated Approval for Alzheimer’s Disease Treatment Leqembi, which has thus far shown efficacy at combatting one of the scariest threats to tens of millions of families worldwide. Lots more data to come, and the cost of care is a concern, but it’s a great sign to see Alzheimer treatment options improving from essentially none.

• I’ve enjoyed TheMoonMidas’s new daily AI bites newsletter, Artificial Genie. Last week’s post on stepping up your Twitter game with AI is an actionable favorite.

• Interested to see if the Terra blockchain can continue to climb back from the ashes of last year’s massive collapse. Building a better interchain wallet with Station is a step in the right direction.

New from me

• Not every tech company has stopped hiring. Well-capitalized startups, particularly in the AI/ML space, are still hiring to build out our foundations. LinearB, where I work, went from 40 to 95 team members in 2022 and plan to be at 120 at the end of 2023—which Jon Swartz highlighted in Market Watch.

  • If you’re looking for a new startup job, AI and climate tech are a good bet.

• In December, I noted that Dev Interrupted would be doubling down on YouTube. We’ve already seen some traction with shorts, including this one with Google’s Head of Developer Media Forrest Brazeal where he highlights why the phrase “skills gap” is often bull shit.

  • You can listen to my full interview with Forrest here.

What’s up next?

So I lied to you in my December edition 😅—I’d told you I was going to include some personal goals for 2023 and successes/failures from 2022.

But I got too fired up writing about the changes happening in SaaS, and I’ve kicked the can until the next edition—which will be headed your way next week. You can also expect to see more on what’s happening in AI, crypto, and the markets.

Thanks for reading another edition of Test Lab, and be sure to drop any feedback in the comments (or Tweet it at me).

Cheers,
Conor

When Twitter acquired Revue in January of 2021, many people (myself included) were excited to see what Twitter did for creators by tightly integrating a newsletter function into their already established platform. Twitter ran a similar playbook with major success against Clubhouse, spinning up Spaces and leveraging their already built-in audience base and “we’re the internet’s town hall” credibility to crush them.

But Revue never truly took off, and Substack maintained market dominance in the newsletter space—innovating faster, improving discoverability, and altogether kicking Revue’s teeth in. Now that Twitter has announced plans to shut down Revue altogether, I and many others are migrating our email lists over.

So, uh, welcome to my new Substack 🤷‍♂️

What to expect

Those of you who’ve followed my infrequent TestLab editions have an idea of what to expect (I’ve also added the back catalog to Substack)—but I’m making some changes.

1. More frequent updates

After a busy 2022 where I didn’t prioritize my personal writing, I’m committing to doing more in 2023, and Substack is going to be where I do that. If you stay subscribed to this newsletter, you’ll get an email twice a month in 2023.

2. What will I be writing about in 2023?

You can expect similar topics to what you saw in my Test Lab newsletter. I’ll be covering content on the future of society as driven by technology, such as:

• Software development

• The attention economy

• Climate tech

• Crypto/Web 3

• AI

I’m also very open to diving into particular topics that my readers bring to my attention—want to discuss some of the latest research on psychedelics or a big new story? Tweet or email me about it, and I’ll plan to include it in my next edition.

3. What will be in each email?

Twice a month, I’ll be sending out free newsletters featuring:

• A write-up on something I’m thinking about, generally aligned to the topics noted above. In this first edition, that’s laying out why I’m switching from Revue and what to expect in 2023.

• What I’m paying attention to: a section to feature some of the best content I’ve recently come across that I think subscribers to this newsletter will find valuable, particularly aligned with the themes of the technology and social changes driving the future and changing how we interact.

• Content from me: as per usual, I’ll include a short write-up on the content I’ve worked on recently, including podcasts, blogs, videos, and more.

• What’s next: I’ll close each edition with a sneak peek into some of the things I’ll be up to next.

I may evolve the format somewhat as the year goes on, but I’ll always plan to include these elements.

Have you subscribed?

Look, if you’ve read this far in this first edition—something is resonating with you. If you’ve already subscribed, thank you. I appreciate you.

If you’re not subscribed, but you’re still reading, doesn’t it seem like you probably ought to subscribe? Luckily it’s easy 👇

Subscribe now

Now that we’ve got that out of the way read on to see some of my favorite pieces of content from the past couple of weeks, get some news about my recent content, and hear more about what’s coming in 2023.

What I’m paying attention to

Some of my recent favs

• YouTube vs Twitch: Those of you who’ve been reading my content for a while know I’m a big believer in the online shift to an attention economy. One of the most successful such creators? Ludwig. Fresh off hosting a massive chess boxing event with more than 300,000 viewers, Ludwig released a video breakdown of his first-year performance after switching from Twitch to YouTube. If you’re interested in understanding the dynamics of modern internet content for the top echelon of creators, the first 12~ minutes of his breakdown are an intriguing and entertaining look behind the curtain.

• History’s greatest frauds: With the horror of the FTX collapse sweeping crypto and the broader financial markets (I’m still shaking my head at how SBF got released on bail), take some time to join the Acquired podcast on their three-and-a-half-hour deep dive into the backstory and complexity of the 2001 then-largest bankruptcy in US history: Enron. Ben and David’s storytelling and research bring the story to life and draw important parallels to recent events. Listen here.

• The impact of AI & ChatGPT: Look, it’s probably apparent to everyone that AI is changing the world—and it’s doing so in both obvious and non-obvious ways.
  
  My friend Luca Rossi dove deep into AI, the workflows it enables, how the models can improve, what these innovations mean for software development, and more.

  Refactoring

  AI & The Future of Coding 🤖

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  3 years ago · 12 likes · 5 comments · Luca Rossi

• Lead by listening: I had the pleasure of meeting Adrienne Shulman at DevOps Enterprise Summit this year and had a great conversation with her about building team culture and leading by listening. Adrienne’s recent article sharing five tips for leading a global team was an excellent follow-up. Read it here.

New from me

Some of the new content I’ve put out since I last emailed + exciting project updates

• Season 3 of Dev Interrupted is coming January 3rd & I have 8 new ideas: We had an incredible Season 2 of Dev Interrupted—440% growth in streams, breaking into Apple’s Top 100 US tech pods for the first time this week, recording in person pods at events, and much more.
  But I want to make Season 3 even better. I wrote up 8 ideas to help us improve in 2023.

  

• You Don't Need to Sacrifice Developer Experience for Productivity: I firmly believe that high-performing engineering teams are highly-engaged, not heavily policed in a way that inhibits culture. Why and how? Read my blog to find out.

• Snarky Twitter quotes: It’s always my snarky tweets that get quoted by the media 😂 such as this one about the audio mixup on Amazon’s Season 3 launch of their flagship Jack Ryan franchise. Reporters from Games Radar and Cinema Blend included it in their recaps of the audio syncing issues.

• Doubling down on YouTube: One of the big focuses for the Dev Interrupted team in 2023 is going to be building our YouTube presence even further. We saw great success in 2022, going from a tiny 179-subscriber channel with just a few uploads to over 1,000 subs today and a 438% increase in watch time as of December 23rd.

  

  • As part of that effort, we’ve started to put out some YouTube exclusive content, such as my interview with Adeva CEO Katerina Trajchevska, covering everything from Twitter trolls to test-driven development and how to craft the perfect MVP.

• Decentralizing the web on Flux: As part of the final edition of my Test Lab newsletter on Revue, I wrote about an upcoming Twitter Spaces I was co-hosting, discussing building decentralized infrastructure that could underpin the Web 3 movement—and why that decentralization movement matters to avoid single points of failure. We had a fantastic hour-long conversation that you can listen to here.

• 130k views and counting on DZone: Part of my role at LinearB involves writing about both our Dev Interrupted Podcast and the software engineering industry more broadly. I’ve had more than 130,000 views of my episode recaps and articles on DZone in the last year, and I just recently shared two new ones:

  • Treating Devs Like Human Beings: I was joined by three incredible engineering leaders for an empathetic look at the human element of software development

  • How to Train Devs to Disrupt Industries: Lessen’s CTO joins me on Dev Interrupted for a look at how to disrupt industries with developers through a phased approach

What’s up next?

I’ll be closing out 2022 and kicking off 2023 with a write-up of my approach to the next year, including:

• Core beliefs I have about the future

• Skills and habits I’m working to build

• Some successes from 2022 and some failures

• Resulting intentions for 2023

Happy holidays and talk to you in the new year,
Conor

PS - If you didn’t already, be sure to go back and vote in the poll at the start of this newsletter! I’d love your input on what to name it.

It's been a tough year for those of us who want to see a decentralized Web 3 future for finance that rewards creators. The market has dumped, Luna collapsed, and this new FTX misappropriation of funds scandal has deeply rocked the cryptocurrency industry. My heart goes out to those affected by this most recent disaster - and it's a clear sign to me that investors in the space should make sure they're evaluating their risk management strategies. It's a volatile space - don't make the same mistake I did with Luna.

Non FTX crypto news

But there's also a ton of exciting building going on in Web 3! And I've been continuing to interview founders to discuss the innovation happening in crypto.

In fact, our very next Twitter Space is lined up for today at 10:30am PT, 1:30pm ET. Set a reminder and join Shigeo808 and I live as we interview $FLUX's co-founder Daniel Keller.

If you're not able to make Monday's space, don't worry - I'll definitely share a link to the recording in the next edition of TestLab. For now, you can catch up on the most recent space below 👇

• On Tuesday, November 8th, Robert Alcorn, CEO and co-founder of Clearpool, joined Shigeo and I on Twitter Spaces. You can tune in to the recording here or read the AMA recap.

Podcasting in person again

The startup I work at, LinearB, has been a hive of activity all year - and I've made two big jumps at work as I'm now co-hosting half of our Dev Interrupted podcast episodes and was promoted to Director over the summer.

We're in an incredibly blessed position having runway and growth combining to let us continue to scale going into 2023 while many tech companies are laying off employees. I feel insanely lucky to be working at such an interesting - and exciting - company in what's a challenging time for many. In recognition of that luck, I'm also building some personal principles around my own capital and how I'm not just investing - but giving back (more on that in Test Lab to start 2023).

For now, here are some of my favorite pieces of engineering leadership content that I've published over the last few months:

• Leaders from CircleCI, Blockchain.com & Oliver Wyman, joined me at our 3rd Interact event to discuss how to guide an engineering org through uncertain times.

• Mark Cruth, a Modern Work Evangelist, shared Atlassian’s State of Teams Report with me and explained what the findings mean for dev teams everywhere.

• Nobody knows what dev teams need to grow and heal than Kelly Vaughn. She joined me for a stellar episode of Dev Interrupted - and this article is one of those that has helped me get more than 125,000 page views on DZone in the last year.

You can also expect to see Dev Interrupted and me on the road in person next year. We've built an in-person experience that we kicked off at September's DevOps Enterprise Summit(DOES) in Las Vegas and which we'll be bringing to major events in 2023. Look out for the Dev Interrupted Dome at your next engineering event - and shoot me a reply if you have an event you think we should be at.

Oh.. ps we eloped

Katherine and I figured the only way we could top going viral in March was to cap our whirlwind year by getting married! We eloped in a private ceremony in August and couldn't be happier.

How to grow your podcast

One final piece of content before I close out this 6th edition of Test Lab - I joined Brad Hammond on The Empathetic Marketer podcast to share my perspective on how to integrate a podcast into your content and demand strategy and discuss how we've grown Dev Interrupted at LinearB. If you're interested in the marketing and science behind podcasts, I definitely recommend checking this one out.

• How do you overcome growth barriers with effective marketing strategies? Listen as I uncover effective marketing strategies that have helped LinearB's podcast grow - and share some of the ingredients to our success.

Off to SE Asia

After a crazy quarter of work travel, I'm off to Vietnam and Thailand for two weeks of rest and relaxation with friends. We'll be visiting Hanoi, Bangkok, and Ho Chi Minh City. I'm incredibly excited to sample the cuisine, experience the culture, and explore. Plus, I'm planning to take the time to do some writing and finish up some 2023 goals that I'll be sharing in the 7th (or maybe 8th) edition of this newsletter.

If you've been to either country - I'd love recommendations! Drop me a reply to this email or DM me on Twitter; I'd love to hear from you.

Hope you've been well, and thanks again for staying with me for another edition of TestLab -

Conor

PS - If you find the content in this newsletter interesting - consider forwarding it to a friend who would find it valuable too.

• I hosted a live stream event for 1700 software engineering leaders - and we managed to crash it in the first 5 minutes. Here’s what we learned from fixing it.

I've hosted several recent episodes of Dev Interrupted, but I think my favorite might be this week's convo with engineering executive coach Lena Reinhard.

• Joshua Romoff, R&D scientist at Ubisoft La Forge joined me to talk all things video game development and Ubisoft's quest to build the perfect bot.

• Building a successful engineering organization is not easy. Whether it's people and processes, technology, culture, or how your team communicates - we talked about all of them on this panel.

I also especially enjoyed my Interact session with Fable's CEO Alwar Pillai on the topic of inclusive development - a must-watch for anyone building digital products.

Oh uh, I also lost $400k in Luna 😅

Alright.. so a lot has been going on the past couple months on the dev front - but there's been even more going on in crypto, including a massive market sell-off and the overnight bankruptcy of the Terra network which I was heavily invested in.

You can catch up on how I lost out during the Terra crypto crash in the ABC article below, and to learn about all the development happening in Africa's Web 3 communities, make sure to tune into my upcoming spaces on Twitter.

• One of the outcomes from the massive Luna crash? Some of my writing (see the Terra x Flux article I wrote) looks a little out of date - and I ended up on ABC Australia talking about how I screwed up my risk management with Luna/UST - I didn't think it would crash as fast as it did.

As a result, I'm out $400k. But I'm still here and I'm going to keep building.

I've also hosted several recent spaces bringing together different crypto and Web 3 communities - particularly as part of the IBC ecosystem - to talk about what the future looks like and what people are building. For many folks in the crypto community, it’s been a necessary process of discussing and healing from some challenging financial and personal losses. You can find them below, featuring more than 10,000 participants across the last couple of weeks 👇

• The Luna X SCRT community Twitter space I hosted with Tor Bair on May 13th

I've got a lot more coming on the Web 3 content front - so stay tuned for more spaces!

What about climate tech?

With how busy I've been on the Web 3 and dev content side of things, it's been a pretty quiet time for me on Green Economy content - I still have 3 episodes of Growing the Green Economy waiting to be edited, which I'll look to get out later this summer after which I'm planning to put the show on a longterm hiatus to let me focus elsewhere.

However, that doesn't mean there are no new updates - one of my 2021 articles was even recently quoted in this Seattle Times piece on high gas prices - and why they can be a good thing 👀

I'll be back soon with new podcast episodes to close out the first season of Growing the Green Economy - I appreciate all of your patience with me as I get around to editing them.

I really enjoy writing these updates - and I hope you're enjoying them as well! For me, they help to let me zoom out, document, and remind myself of all the things I'm creating - even when on a weekly basis I often beat myself up for not doing more 😅

Plus, I always love hearing from people - want to collaborate on an upcoming Twitter Spaces or have something you think I should look into?

Hit me up on Twitter, I'm always happy to chat.

Hope you have a great weekend,

Conor

Let me tell you - I was not prepared, at all, for the number of messages, notifications, and mentions I got this week.

2 viral pro tips

Part of the reason the tweet did as well as it did? Reply and quote tweet engagement. If you want to increase the engagement on your posts, particularly one that's doing well, you should encourage more comments.

One great way to do this? Reply to as many comments and quote tweets as you can. It'll drive additional engagement and turn the algorithm further in your failure. Don't underestimate the impact that these early responses can have on the viral growth of your engagement.

One mistake I made? I didn't realize how big the thread was going to get - and I casually fired off the tweet below sharing where we got different items for people who were interested.

I woke up the next day to find the main tweet with more than 50,000 likes (it now has over 605,000) and growing fast. The number of people who asked where we got the bean bag, the monitor, etc were innumerable - and if I'd used affiliate links in this post, we could have captured some of that demand and realized a benefit for the massive amount of free marketing we were giving to these companies. Always use affiliate links when you can.

Alright, enough about this insanely viral work from home tweet - let's dive into what else I've been up to:

Our oceans are in crisis; from trash to ocean acidification, climate change, and overfishing.

But there is reason for hope and National Geographic's Explorer-In-Residence Dr. Sylvia Earle joined me on my Growing the Green Economy Podcast to lay out why she's bullish on the future of the ocean and of the Blue economy. A world-renowned oceanographer and Founder of Mission Blue, Dr. Earle brings 86 years of experience to bear to explain the problems that plague the oceans, how we can fix them, and the inter-related nature of these challenges with the broader climate crisis.

This week I was joined on the Dev Interrupted Podcast by dev content creators/influencers Masha Zvereva (Stereotype Breakers) and Tiffany Janzen (TiffinTech) to discuss:

🤔 What Gen Z devs want from engineering leaders?

✅ How to make devs brag about your company

🚫 Why a dev might turn down a job

➕ More

Listen in 👇

Hosting INTERACT on April 7th

On April 7th I'll be hosting INTERACT, a virtual conference for more than 1,000 engineering leaders - and I'll be hosting an incredible panel, a session on building accessibility in development, and a screw-up story by one of the most successful CTOs in history.

If you're a dev or engineering leader, I highly recommend joining me for INTERACT. Learn more here.

Shankar Ramaswamy has led teams at Amazon, Google & PayPal, now the Head of Eng at DataStax, he joined me to share his secrets to leading 100's of devs.

Einat Orr, CEO at Treeverse, joined me to discuss the state of data & why having bad data might be worse than having no data at all. Consider yourselves a data nerd? This is a must listen.

Thanks for reading!

Got suggestions for my next newsletter? Want to collaborate on content? DM me on Twitter or reply to this email.

I'll be back with another issue in a few weeks - after I've gotten through INTERACT and hopefully once I've managed to finish some blogs.

As always, thanks for reading -

Conor

At the end of November, I published my 7th episode of my Growing the Green Economy Podcast featuring Aidan Reilly, co-founder of the Farmlink Project. They connect farmers to food banks, delivering millions of pounds of farm-fresh produce that would otherwise be wasted, to feed families.

Aidan and I talked about how Farmlink is diverting food waste and why spoilage in our food system is such a problem—with major untold impacts on climate change. Plus we dive into what's going on with supply chains, mutual aid concepts, and more. 

Listen in

Diving into DeFi & Web3

I'm a longtime believer in the potential of distributed ledger technologies like blockchain to address certain technological use cases - and I've been an investor in and user of cryptocurrency since late 2015.

As an active political participant with many leftist views, it's been frustrating seeing how the dynamics around crypto have evolved online, particularly as I've gotten more involved in decentralized finance (DeFi) and the future of the Web over the last couple of years.

I tweeted about my frustration with the current political dynamic and narrative around crypto and I think the resulting discussion was well worth a read. Read the whole comments to see my thoughts on how DAOs (Decentralized Autonomous Organizations) can enable worker power, the opportunities in DeFi to improve lives, and much more.

However, it is important to note that there are valid concerns in the space, one of which is the lack of regulatory guardrails and financial protections for consumers, which leaves too much open space for scammers and other bad actors. This week saw the exposure of one of the worst - and Bloomberg wrote all about it.

A decentralized-finance project called Wonderland is being rocked by controversy following the disclosure that it was being run in part by a felon with ties to one of the biggest cryptocurrency scandals.

Writing and Hosting Dev Interrupted

Season 2 of Dev Interrupted opens w/ Conor Bronsdon interviewing LinearB co-founders Dan Lines & Ori Keren about how they have 10xed their startup while 5,000 miles apart.

I've been hard at work the last couple of months on our Dev Interrupted project—producing the podcast, growing our team, launching our 2nd INTERACT conference which I'm once again hosting, and also doing some writing.

If you're interested in learning more about software development or engineering leadership, check out some of the podcasts I've hosted below - or either of the two articles I've recently written with the collaboration and help of Adam Noble.

+ keep your eyes peeled during Season 2 for several more episodes that I'll be hosting.

A good SRE engineer will tell you your service is never down. A great SRE engineer will tell you that’s not what you should be…

Move over, Han Solo. Kessel Run is more than fiction, it's the US government's attempt to build startup culture and DevOps into the DoD.

Brian Singer, co-founder & CPO at Nobl9 and Conor Bronsdon help us close out season 1 of the Dev Interrupted podcast with a discussion on entrepreneurship.

Can you do me a very quick favor?

Ratings and reviews are the way podcasts get discovered.

With the return of my Growing the Green Economy Podcast from hiatus, I could really use your review on Apple Podcasts or on Spotify - whichever is easiest for you.

Can you take 60 seconds to help me out?

Leave a review on Apple Podcasts

Leave a review on Spotify

Guest Appearance: Pathmonk Presents

Finally, I was happy to appear on a recent episode of the Pathmonk Presents podcast for marketers discussing how to build community in marketing and how that community development can drive awareness for customers. We get a bit nerdy, but if you're interested in marketing it's a good bite-size 18-minute pod.

Conor from LinearB offers us a great overview of whom they serve and why they do it while diving into the role of community growth in their buying journey.

Thank you

Thanks for reading this third edition of Test Lab - as always you can respond to this email or drop me a tweet with your feedback, or reach out if you want to collaborate on a piece of content.

Look out for a new Test Lab edition in the next few weeks with a fresh episode of Growing the Green Economy, an article or two that I'm working on about Web 3, and hopefully more exciting news!

Talk soon-

Conor

@ConorBronsdon

Acquired rocks. It's one of the best strategy and business podcasts out there, but what really sets it apart for me is the history they bring to each topic. Designed to tell the stories of great technology companies and what's made them successful, they always bring in-depth knowledge, amusing annecdotes, and much more. Their episode on the New York Times is a personal favorite - starting from the inception of the paper and running all the way through their hyper-successful transition to a digital-first strategy.

Every company has a story.

I hosted an engineering leadership conference

As part of my work with Dev Interrupted, I've worked with Margaret Shaeffer to launch INTERACT, a new conference for engineering leaders.

I hosted the conference's 'Screw Up Stories', a series of interviews with engineering leaders abou their biggest and most amusing screw-ups. Luca Rossi's (see below) was a personal favorite.

As Co-Founder and CTO, you have to make tough decisions with speed but sometimes those decisions bite you in the ass. Here's what happened - and the lessons ...

Some thoughts (in a 🧵) on Web 3.0 👇

Plus a great thread on the potential positive impact of decentralized autonomous organizations (DAOs) - if you're a believer in progressive politics but have concerns about blockchain technology due to what you've seen in the media, I highly reccomend giving this thread a read:

Coming soon..

I'm finally making a return to my Growing the Green Economy Podcast! Make sure to subscribe on your podcasting platform of choice so you don't miss out on my interview with the Co-Founder of the Farmlink project. New episode dropping Tuesday morning...

To combat the climate crisis, we need visionary leaders who are willing to act. In Growing the Green Economy, we tell the stories of the leaders, activists, and businesses that are helping build a sustainable future.

Last week I joined streamer (and friend) Ari Centauri on her Twitch channel to talk about the business and strategy of online content and the cross-pollination happening between the streaming community and the business community around digital content.

We also dig into community-oriented growth, political views, and a bit more.

Watch Conor's guest appearance on Ari's stream talking content, the internet, and much more.

My new video and article

Recently, most of my writing has centered on developers and engineering leadership - but any chance I get to combine DevOps and Star Wars in one article, I've got to take it.

Move over, Han Solo. Kessel Run is more than fiction, it's the US government's attempt to build startup culture into the DoD.

I've also launched Dev Interrupted as Editor-in-Chief, and I'll be not only producing the podcast but contributing articles and helping create videos like the one below.

Some of my favorites from this week

Mike and Ryan are two of my favorite urbanist writers - and their new article envisions how Seattle's Aurora Ave could change from a highway tearing the city in half to one of the most attractive places in the city.

Livability would flourish in surrounding neighborhoods if cars were removed from one of Seattle’s deadliest and most polluted streets. Today’s Aurora Avenue in Seattle is a wall, a loud…

Finally, I've got to include one of my favorite recent Twitter threads, providing six wonderfully useful G Suite features to make your life just a little bit easier⬇️

Coming Soon

I've been incredibly busy with my new job at LinearB launching Dev Interrupted and put my Growing the Green Economy Podcast on temporary hiatus - but we're back now! I've got three episodes recorded, including a new episode, recorded yesterday, and I'm stoked to keep talking about how we can confront the climate crisis together and - prosper as individuals and as a community.

Throwback of the week

If you missed it, I've started to create some content on YouTube, including this video examining why Tesla isn't going to save the planet.

Please, help me improve

Got suggestions for next week's episode? Want to collaborate on content? DM me on Twitter or reply to this email.