Coder Legion - Recent posts tagged aistrategy

What Did It Cost the Customer to Send This Signal? Everything Else Is a Footnote.

Sun, 18 Oct 2026 04:20:21 +0000

Part 3 of 4 on IBR’s Mini Strategy Handbook for AI Founders. You’ve launched, you’ve started distributing, and now numbers are arriving. This is the review about which of them to believe.

Two founders walk into two investor meetings on the same morning. The first opens with a screenshot: 100,000 on the waitlist, a LinkedIn post with 40,000 views, a launch that “broke the site.” The room nods. The second opens with a sentence: eleven customers fired their existing vendor to switch to us; nine have already renewed. No screenshot. Eleven names.

One of them has traction. The other has a mirage. And, as Richa Sati’s The Signal Cost Principle points out with some sadness, almost everyone in the room is wired to be more impressed by the first one.

The whole review, and it’s the longest and most foundational in the handbook, reduces to one question, which the authors themselves say everything else is a footnote to: what did it cost the customer to send this signal?

Signal strength is proportional to signal cost
A like costs a thumb-twitch. A purchase costs money. A renewal costs a year of continued trust. A vendor replacement costs budget, politics, and a career-risk bet by whoever championed you internally. These signals were never equal. We just pretend they are, because the cheap ones are so much easier to collect, and because a big number on a slide feels like progress in a way that “nine renewals” doesn’t.

The handbook draws this as a Commitment Ladder, and it’s worth internalizing the two instructions that come with it: read signals near the bottom with suspicion; act on signals near the top with confidence. Bigger, louder and prettier are not the same thing as true.

I’d add the connection to Part 2. The founder channel, done right, generates a lot of bottom-rung signal, views, comments, followers, and it’s intoxicating precisely because it arrives first and in volume. This ladder is the antidote you drink the morning after a post goes well.

The famous number was never the number
My favourite section: four celebrated companies, and in every case the number they got famous for isn’t the one that convinced them internally. Dropbox’s 75,000-person waitlist from one Hacker News video is the legend; the signal that mattered was people trusting it with real files, inviting colleagues, and returning daily. Slack’s registered users made headlines; the internal bar was teams that exchanged 2,000 messages, of whom 93% were still active later. And Superhuman’s Rahul Vohra used the Sean Ellis test to measure product-market fit instead of arguing about it.\

The Superhuman detail is the one to steal. “Do you like the product?” costs nothing to answer, so everyone says yes. “How disappointed would you be if you could no longer use it?” forces the customer to imagine giving something up, so only “very disappointed” counts. The benchmark is 40%. Superhuman scored 22%, and instead of launching, they rebuilt. Slack, tested later, scored 51%. Note the shape of the trick: they made the question cost the customer something, and the truth arrived on its own.

The four traps, and their anatomy
Every false-traction trap has the same skeleton: a cheap signal, why founders believe it, why it’s weak, and the expensive question to ask instead.

The Applause Trap: 25,000 views on the demo post; what did anyone actually sacrifice because of it? The Friendly User Trap: friends and advisors say they’d love it; encouragement is free, would you put down a deposit today? The Waitlist Mirage: hundreds joining daily; joining costs four seconds and leaving costs nothing, so how many climbed the ladder, installed, paid, returned? And Pilot Purgatory, the enterprise founder’s special hell: a recognizable logo “testing” you; a pilot carries almost no organizational risk and is not a purchasing decision, so how many pilots replaced a vendor?

The definition that makes it click: false traction is not fake demand. Dropbox’s 75,000 were real people. It’s misinterpreted demand, a cheap signal read as an expensive one.

Where I’d argue with a word, gently
The word is “traction.” It’s a physics metaphor: grip, the thing that lets force become motion. But most of what founders call traction is attention, and the review’s own fourth law says it perfectly: marketing creates attention; commitment creates businesses; only the latter compounds. So I’d go one step further than the handbook. Rename the dashboard. Put attention metrics on one page and commitment metrics on another, and never let a number from the first page appear in a fundraising deck without its ladder rung labeled next to it. If your investor can’t tell whether “12,000 users” means “signed up” or “renewed,” you’re not reporting, you’re staging.

The five laws are worth carrying whole: never optimize a cheap signal; every metric should move customers up the ladder; friction isn’t always the enemy, a deposit or a security review filters intent from curiosity; attention versus commitment as above; and the strongest signal is the one the customer didn’t have to give, an unprompted referral beats any survey. The line I’ll keep, from the review: customers don’t lie, they tell the truth in proportion to what it costs them to tell it.

Which sets up Part 4 neatly. Suppose you’ve climbed the ladder, real purchases, real renewals, and you’re still losing money every month. The instinct is to cut. The handbook’s last review argues that’s often exactly the wrong move, and it has a formula and a case study to prove it.

-Hardik

I’ve spent about two decades building data and AI systems and, on the side, trading Indian equities, where the market delivers the Signal Cost lesson daily: the loud number is rarely the priced-in one. This is Part 3 of 4 on IBR’s handbook. Part 4, The Contribution Factor, is the finance lens that tells you whether to cut or spend, and it’s the one I’d hand every founder staring at red ink. Subscribe; it costs you an email address, which, by this ladder, means I should read it with suspicion.

Source: Ikana Business Review, Mini Strategy Handbook for AI Founders (August 2026), Review 03, “The Signal Cost Principle” by Richa Sati, including the Commitment Ladder, the four traps, and the five laws; full framework at ikanabusinessreview.com. The Dropbox, Slack, and Superhuman figures are as reported there. The “rename the dashboard” suggestion and the trading aside are mine.

Series map: Part 1 · The Inverted Launch → Part 2 · The Founder Is the Channel → Part 3 · The Signal Cost Principle (this) → Part 4 · The Contribution Factor.

You Don’t Win by What You Ship. You Win by What You Cut.

Wed, 30 Sep 2026 06:02:59 +0000

Part 1 of 4 · Notes on Ikana Business Review’s Mini Strategy Handbook for AI Founders: launch → distribute → read the signal → decide.

This one’s about launching, and it’s a framework I have personally, embarrassingly, half-implemented.

Ikana Business Review put out a Mini Strategy Handbook this month, four field-tested frameworks for AI founders, and I read it the way you read a doctor’s report about yourself: nodding, wincing, occasionally saying “well, yes, but.” It’s good. Good enough that I’m going to walk through it in four parts, in the order the handbook intends, launch, distribution, traction, growth, and, in my usual manner, agree with the substance while arguing with a word or two.

Part one is their first review: The Inverted Launch for AI, or ILA, from Vivek Bisht. Here’s the idea in one breath. For twenty years the playbook was sequential: ship one focused feature, validate it, layer more on over years. That worked because building was expensive and pivoting was slow. AI collapsed the cost of building, so the winning move flipped: ship a cohort of tools at once, watch which ones users actually pull on, cut the rest without sentiment, and reinforce the survivors. The unit of launch stops being the feature. It becomes the portfolio.

I have a confession that makes me the ideal reader of this review. I’ve shipped, this year, something north of eight products. Trinetra, Gambit, Helm, Faceoff, Sprout, Serum, etc. a couple of other items as well. I have run the first half of ILA with what can only be called enthusiasm. So take what follows as coming from someone who has field-tested the “launch wide” instruction and is still working on the “converge inward” part.

The Arc story, which is really a telemetry story
The handbook’s proof case is The Browser Company, and it’s a good one. Arc had accumulated Spaces, Easels, Boosts, Little Arc, Air Traffic Control, Live Folders, Calendar Preview. Power users adored it. Most users got lost. And the telemetry, once anyone was brave enough to read it, was brutal: only around five and a half percent of daily users regularly used more than one Space, about four percent used Live Folders, and Calendar Preview on hover was used by 0.4%. Meanwhile, two capabilities, chat-with-tabs and personalization, were used by roughly 40% and 37% of daily users.

So they put Arc into maintenance, rebuilt around the two things people actually used, and called it Dia. Atlassian bought the result for $610 million within a year of the prune. The handbook’s line, and it deserves the frame: sometimes the cut isn’t three features, it’s the whole product surface.

Notice what the story is not. It isn’t “they built the wrong thing.” Arc’s features were beautiful and people who used them loved them. It’s “they kept the wrong things too long.” That’s the entire inversion in one sentence, and it’s the sentence I’d tattoo on every founder who’s currently defending a feature because it demoed well.

Where I’d argue with a word: it’s not really about launching
Here’s my quibble, and it’s a friendly one. The framework is called the Inverted Launch, but launching wide is the easy half. Any founder with Claude Code and a weekend can ship five tools now; I have the repositories to prove it. The launch got cheap for everyone, which means launching wide isn’t an edge, it’s the entry fee.

The hard part, the part that actually inverts, is the roadmap. The old roadmap was addition: ship more over time. The new one is subtraction: cut what doesn’t earn pull. And subtraction is psychologically expensive in a way addition never was, because every tool you kill has a builder who loved it, a slide it appeared on, and a sunk cost that whispers “give it one more sprint.”

The table’s last row is the one that matters. Old: you win by what you ship. New: you win by what you cut. Which means the scarce skill in AI product-building is no longer engineering velocity, everyone has that now, it’s the willingness to read two to four weeks of live data and let it outrank your conviction, your investor’s opinion, and your ego. Building got cheap. Deciding didn’t.

Steal this: the prune, made concrete
The handbook gives five steps, launch wide, instrument everything, read the signal, cut, reinforce, and they’re right. I’d add teeth to step four, because in my experience step four is where founders quietly stop. Before you keep any tool past its trial window, ask three questions.

Would you build it today, from zero, knowing what the data says? If no, it’s sunk cost with a UI. Does it move the pull metric, or just the roadmap slide? If the slide, cut. And who’s loudly defending it: users, or the person who built it? If the builder, cut, kindly, and buy them a coffee, because they’ll build the next winner.

One caveat the handbook doesn’t dwell on and I will, since Part 2 depends on it: launching wide has a distribution cost. Five tools means five stories to tell, five audiences to find, and a founder attention span divided by five. The cohort launch works only if you’re ruthless enough to converge fast, because until you do, your marketing is as diluted as your roadmap. Which is a fair description of my own year, and precisely why Part 2, on distribution, is the one I’m most nervous to write.

You don’t win by what you ship. I’ve shipped plenty. Time to go win by what I cut.

-Hardik

I’ve spent about two decades building data and AI systems and, this year, testing the “launch wide” half of this framework more thoroughly than anyone should. Next up, Part 2: The Founder Is the Channel, on why distribution at seed stage is a person problem wearing a marketing costume, written by a person with that exact problem. Subscribe so you don’t miss me being uncomfortable in public.

Series map: Part 1 · The Inverted Launch (this) → Part 2 · The Founder Is the Channel → Part 3 · The Signal Cost Principle → Part 4 · The Contribution Factor.

Source: Ikana Business Review, Mini Strategy Handbook for AI Founders (August 2026 edition), Review 01, “The Inverted Launch for AI (ILA)” by Vivek Bisht. The Arc/Dia telemetry figures and the Atlassian acquisition are as reported there; the full breakdown is at ikanabusinessreview.com. Commentary, the three prune questions, and the distribution caveat are mine.

The 95% Enterprise AI Failure Stat Is Wrong. Here's What the Data Actually Shows.

Tue, 09 Jun 2026 20:23:10 +0000

If you've spent any time in IT circles over the past year, you've probably heard the number: 95% of enterprise AI pilots fail to deliver meaningful business impact. It's been cited in presentations, board meetings, and media coverage so often it's become accepted truth.

Tom Zehren isn't buying it.

Zehren, CEO of Info-Tech Research Group, opened Info-Tech LIVE 2026 in Las Vegas by taking direct aim at the statistic — and the research behind it. "The challenge when media picks up research is truncated information," he told a packed Grand Ballroom. "The shorter you make information, the older you make the information, the easier it travels."

His assessment of the source? "It's an eight-page marketing memo. And it's terrible research."

The specific study Zehren was referring to — widely cited as MIT research — surveyed a relatively small sample and was conducted roughly a year ago, when most enterprise AI deployments were early-stage ChatGPT experiments, not production agentic systems. "They talked about Gen AI one year ago," Zehren said. "What they were really talking about is chat." He also noted a fundamental timing problem: many pilots counted as failures simply hadn't completed yet.

His larger point wasn't to defend AI hype. It was to argue that bad data leads to bad decisions — and that IT leaders need to dig into the details before accepting a narrative.

What Info-Tech's Data Actually Shows

To back his argument, Zehren presented results from two large-scale surveys Info-Tech conducted across roughly 550 IT executives, with a second survey focused specifically on the software development lifecycle.

The findings tell a different story than the 95% headline. Forty-two percent of organizations have already deployed AI solutions across multiple departments — not just pilots — and have seen measurable impact. Another third have deployed customer-facing AI solutions. And perhaps most importantly, the top drivers for AI adoption aren't cost savings. They're growth and removing operational constraints that have held IT back for years.

"Cost reduction is at the bottom," Zehren said. "People are primarily trying to drive growth."

On the development side, 84% of software developers are now using AI across the entire software development lifecycle — not just for code generation, but for testing, documentation, and design. Sixty percent say AI-generated code requires more oversight, but Zehren sees that number differently than most. "If you flip that around, you get to a third of people saying it requires the same level or less. And leading software companies have already moved past the phase of fixing bugs from AI-generated code. The vast majority of code that is prompt engineered by developers at those companies is ready for production."

Five Hypes Worth Examining

Zehren structured his keynote around five AI narratives he believes deserve more scrutiny.

The Klarna story — often cited as proof that AI saves millions — is more nuanced than the headlines suggest. The company reduced its contact center headcount from 3,000 to 2,300 agents and saved approximately $40 million. But customer satisfaction tanked when emotional, complex cases like fraud disputes had no clear path to a human agent. The mistake, Zehren explained, was routing all calls in a specific region to AI agents only — eliminating the human escalation option entirely. Compare that to AWS, which used AI agents to migrate thousands of legacy Java applications to Java 17 — a use case with clear parameters, measurable output, and no customer-facing complexity. "You are not AWS," Zehren noted. "You don't have 30,000 applications running around."

On job displacement, Zehren aligned with World Economic Forum projections: roughly 190 million jobs created globally by 2030 against 92 million displaced — a net gain. The bigger challenge isn't job loss. It's the skills gap. "AI is not going to take your job," he said. "Someone who knows how to use AI is going to take your job."

On SaaS, he sees pressure building but not collapse. Seat compression, autonomous workflows, and the shifting economics of build versus buy are all real forces — but the major SaaS players are evolving their models rather than being displaced overnight.

The Bottleneck Nobody Talks About

In a media roundtable following the keynote, Zehren got more specific about where organizations actually get stuck. The problem isn't model quality or infrastructure. It's organizational.

"The biggest bottleneck, besides data, is the gap between business intelligence and understanding of workflows, and how you attach AI to get value out of them," he said. "That thinking and that knowledge sits in two very different heads. How do you bring those heads together in a structured way?"

It's a deceptively simple observation — and one that explains why Info-Tech's value proposition has sharpened in the AI era. The firm has grown more than 350% recently, even as AI has put pressure on traditional research and advisory models. The reason, according to Zehren, is the combination of the "what" and the "how." "If you have a fancy stat and you don't know how to exploit that — that's the problem. We can combine those two things."

As one participant in the roundtable put it plainly: "AI democratized access to research. That's no longer the valuable piece. The value now is the advisory piece — here's what you do, here's what it means, here's the step."

Zehren's closing argument was direct: this is IT's moment, not IT's threat. "We believe this is actually IT's moment to step up and move from the 'please give me this app' mode into the push mode — where IT understands the technology best and steps up as an organizational leader."

The organizations that figure that out first, he suggested, won't be asking whether AI delivers value. They'll already know.

Helping Clients Move from Pilot to Production: The Agentic AI Governance Playbook

Mon, 08 Jun 2026 13:00:00 +0000

Most agentic AI pilots succeed. Most agentic AI production deployments don't — at least not on the first try.

That gap isn't a technology problem. The agents work. The problem is that organizations treat the move from pilot to production as a scaling exercise when it's actually a governance exercise. And governance is the part nobody planned for.

Here's what the organizations getting it right have figured out.

Why Pilots Don't Predict Production

A pilot is a controlled environment. A small team. A defined use case. Friendly data. Someone watching closely. When it works, the organization celebrates and immediately starts planning to roll it out everywhere.

That's when the real questions surface.

Who decides which processes agents can touch? What happens when an agent makes a decision that a human would have escalated? Who owns the output — the agent, the team that built it, or the business unit that deployed it? How do you audit what an agent did three months ago when something goes sideways?

These aren't edge cases. They're the operating reality of production agentic AI. And they require answers before you scale, not after.

The Four Governance Layers That Actually Matter

Organizations that successfully move from pilot to production tend to build governance around four distinct layers.

Scope definition. Every agent needs a clear mandate — what it can do, what it can't do, and what it must escalate to a human. This sounds obvious in a pilot, where the boundaries are implicit. In production, implicit doesn't hold. Scope needs to be explicit, documented, and enforced technically, not just described in a policy document.

Data access controls. Agents that can access everything will eventually touch something they shouldn't. Production deployments require the same access control discipline applied to human users — least privilege, audit trails, and clear rules about what data an agent can read versus what it can act on. The security conversation your organization had about human access to sensitive systems needs to happen again, this time about agents.

Decision authority. Not every decision should be autonomous. The governance playbook needs to define which decisions agents make, which ones they recommend and humans approve, and which ones they flag and hand off entirely. That tiering isn't static — it should evolve as the organization builds confidence through demonstrated performance. But it needs to exist from day one.

Monitoring and accountability. In production, you need to know what your agents are doing in real time and be able to reconstruct what they did after the fact. This means logging agent actions, not just outcomes. It means setting thresholds that trigger human review. And it means assigning clear ownership — someone who is accountable when an agent operates outside expected parameters.

Start With the Foundation You Already Have

One of the most useful reframes for organizations making this transition: you don't have to build governance from scratch.

Your existing compliance frameworks, access control policies, and audit processes are a starting point. The question isn't "how do we govern AI agents?" It's "how do we extend the governance we already apply to people and systems to cover agents?"

That reframe changes the conversation. It moves governance from an abstract AI challenge to a practical extension of existing IT and risk management practices. Most organizations are further along than they think — they just haven't connected the dots yet.

The Rollout Pattern That Works

The organizations with the cleanest production transitions follow a consistent pattern.

They start with a single, high-impact, low-risk process — one where the agent assists a human rather than making autonomous decisions. They instrument it thoroughly. They measure everything. And they use those early results to build the organizational confidence that makes broader deployment possible.

From there, expansion happens in deliberate steps. Each new process gets the same governance treatment as the first. Scope defined. Access controlled. Decision authority tiered. Monitoring in place. It takes longer than a rapid rollout, but it holds up.

The organizations that skip steps tend to end up rebuilding trust after something goes wrong. That's a longer road than taking the governance work seriously up front.

The Competitive Reality

The gap between organizations that govern agentic AI well and those that don't is becoming a competitive variable. It determines which organizations can scale with confidence and which ones stall after every incident.

Governance isn't the obstacle to agentic AI deployment. Done right, it's what makes deployment sustainable. The playbook isn't complicated — but it has to exist before you need it, not after.

Why Regulated Industries Are Leading the Agentic AI Revolution

Mon, 01 Jun 2026 13:00:00 +0000

Here's something that surprises most people: some of the boldest agentic AI deployments happening right now are in the most heavily regulated industries in the world.

Banking. Healthcare. Insurance. These are sectors where compliance isn't optional, where errors have real consequences, and where IT governance is among the most stringent anywhere. Conventional wisdom says these organizations move slowly on new technology.

The reality is more interesting — and more instructive for every industry watching from the sidelines.

Regulated industries have natural advantages when it comes to agentic AI. Their processes are well-defined. Their rules are documented. Their compliance requirements, while complex, create clear boundaries within which agents can operate confidently. The same rigor that makes these industries seem slow to adopt new technology also makes them well-suited to deploy it responsibly.

The Branch Banking Example

Consider what's happening at some major financial institutions right now. When a customer walks into a branch, there are hundreds of possible reasons for that visit — opening an account, disputing a charge, updating personal information, requesting a wire transfer, and dozens of other scenarios that each require a specific form or procedure.

Historically, getting the right answer to a teller or customer service representative meant navigating a complex web of documentation. Some requests were straightforward. Others required research, phone calls, and waiting — with the customer standing there the whole time.

One institution solved this by deploying an AI agent that branch staff can query in plain language. Describe what the customer needs, and the agent instantly identifies the correct procedure from hundreds of possibilities. What used to take 20 minutes now takes one. The customer experience improves. The employee experience improves. And the risk is minimal — if the agent surfaces the wrong procedure, it takes another minute to find the right one.

That's the insight worth holding onto. The first move wasn't the most complex use case. It was the highest-impact, lowest-risk one. The agent didn't approve loans or make financial decisions. It helped people find the right process faster. Simple, concrete, and immediately valuable.

Building on Early Success

What happened after that first deployment is just as instructive as the deployment itself.

Once the organization saw what one well-designed agent could do, ideas started coming from everywhere. Teams that had previously struggled to justify AI projects were suddenly generating backlogs of hundreds of similar opportunities. The conversation shifted from "can we justify the ROI?" to "how fast can we build the next one?"

That momentum is self-reinforcing. Early wins build organizational confidence. Confidence accelerates adoption. Adoption generates the experience needed to take on more complex use cases.

A second deployment at the same institution tackled power of attorney verification — one of the more painful administrative processes in financial services. Previously, verifying that someone had legal authority to act on another person's account required scanning documents, emailing them to a central team, and waiting through a lengthy phone verification process. With the customer standing at the counter. Often in an emotionally difficult situation.

An AI agent now handles the verification automatically. A branch employee photographs the relevant documents, the agent extracts and validates the information, and within seconds the employee gets confirmation. A 20-minute process became a matter of moments — with full compliance maintained throughout.

Why This Applies Beyond Banking

The lessons from regulated financial services translate directly to other industries facing similar dynamics.

Healthcare organizations deal with prior authorization processes that can take days and require multiple calls between providers, payers, and pharmacies. The same approach — identify the high-impact, low-risk workflow, deploy a focused agent, measure results — applies directly. The boundaries are different. The principle is the same.

Manufacturers managing complex supply chains spend significant human time cross-referencing orders, tracking delivery timelines, and flagging potential disruptions. Agents can monitor those data sources continuously and surface issues before they become problems — without replacing the humans making the final decisions.

Retailers processing returns, insurers handling claims, logistics companies managing documentation — every sector has processes that fit this pattern. High volume, rules-based, time-consuming, and directly connected to both customer and employee experience.

The Question Worth Asking Now

The organizations moving fastest on agentic AI aren't necessarily the ones with the largest technology budgets or the most sophisticated data infrastructure. They're the ones asking the right first question: where do we have a high-impact, low-risk process that a focused agent could improve today?

That question has a concrete answer in almost every organization across every industry. The regulated world figured that out early. Everyone else is catching up.

Finding your answer is the first step. Everything else follows from there.

Is DevRel Dead or Just Disaggregated?

Thu, 07 May 2026 17:39:00 +0000

The "USB-C Moment" for Developer Experience

We’ve entered what I call the "USB-C Momentstrong text" for AI systems—a period of rapid standardization where the Model Context Protocol (MCP) is doing for AI tools what universal ports did for hardware. But this shift raises an existential question for those of us in leadership: If an LLM can write the boilerplate, explain the API, and debug the integration, is Developer Relations still relevant?

The short answer: The traditional "Tutorial-as-a-Service" model of DevRel is dead. The new model is just beginning.

From Documentation to Context-Provisioning

Historically, DevRel was the bridge between a complex product and a developer’s terminal. We spent our cycles on documentation, "Hello World" samples, and answering syntax questions. Today, developers are increasingly using agentic workflows to handle that "Glue Code".

In this era, DevRel's value prop shifts from content to context:

Context-First Engineering: Our job is no longer just writing docs for humans; it’s ensuring our systems are "model-ready." This means architecting MCP servers so that an AI agent can navigate our product's capabilities with high-reasoning synthesis.
The Auditor Role: We are moving into a "Forensic" era of advocacy. DevRel must act as the "Guardian," providing human-in-the-loop governance to ensure that synthetic code generated for our platforms is secure, ethical, and performant.
Systems Over Syntax: We aren't teaching people where the semicolon goes; we are teaching them how our tool fits into their "Enterprise Agentic Memory".

The Disaggregation of the Role

DevRel isn't disappearing; it’s being absorbed into the very fabric of the product.

Builders need us to provide high-integrity digital twins of our systems.
Technical Leaders need us to prove scalability and governance—the "Final Boss" of enterprise adoption.
Domain Operators need us to translate technical capabilities into resilient, real-world systems, like an "Agile Harvest" for agriculture or a "Sovereign Vault" for data.

The Verdict

If your DevRel strategy is still measured by the number of blog posts published, you’re trailing the curve. In the AI era, we are the Context Engineers. We provide the structured reality that prevents AI agents from hallucinating our product’s value.

What do you think?

Is your team still focused on "Developer" outreach, or have you pivoted to "Builder" relations? I’d love to hear how you're retooling your advocacy for 2026.