But the more interesting story is what happened next.

The same companies that automated aggressively also started hiring again, just for different work.

• Not the old roles.

• Not the old workflows.

• Not the same expectation.

New roles built around managing, correcting, orchestrating, and improving AI systems.

Every new role being created around AI is essentially a quality control layer. Agents break without humans behind them. The companies that understand this are hiring for exactly that.

The “AI is killing jobs” panic got the headline. The second half of the story never did.

Every time a major technology shift happened, the panic came first, and the opportunity came second. It happened with ATMs, with the internet, and with mobile. The people who moved early captured the new roles. Everyone else spent two years catching up.

If you are trying to figure out where to position yourself in all of this, I put together a consolidated guide.

Read: AI Engineer Job Guide 2026

What Agents Actually Change?

Agents have taken over the execution layer. Outbound prospecting, follow-ups, reporting, and content drafts that work are no longer yours.

But every task agents take over creates a new gap. Strategy, QA, orchestration, tooling, customer experience, none of that runs itself. Someone still has to own it.

The companies hiring aggressively right now are hiring for exactly those gaps. I broke down what this looks like inside real teams.

Tune in to the best parts:

1. [4:03] The boring tasks agents are already doing, so your sales team does not have to.

2. [5:46] The tools making this happen inside real companies right now.

3. [7:38] Why your team is not getting smaller, just doing different work.

Watch the full video here

The roles being created right now that did not exist five years ago

The real question is not which jobs AI is replacing but which jobs exist specifically because AI cannot own its own outcomes.

AI Engineer salaries jumped to an average of $206,000 in 2025, a $50,000 increase from the year before, and that is just the baseline. The specialized roles being built around agentic AI are commanding 30 to 50 percent above that.

Look at these titles carefully. None of them existed five years ago. AI did not create them by being powerful. It created them by being incomplete.

Every gap in quality, workflow, customer experience, tooling, and revenue operations became a job title. That is what agentic AI actually does to a workforce.

Before you build around any of these roles, understand what you are actually signing up for. Agents produce output, but they do not carry accountability.

Someone on your team still does. These three questions will tell you if your team is ready for that.

❓What happens when your agent gets it wrong?

❓Who checks the work it produces?

❓What does your team do with the time saved?

Smart companies already know these answers, and the way most of them are handling this shift might surprise you.

The Signal Everyone Is Walking Past Every Single Day

The companies that already know those answers are showing you exactly where they are. Every AI job posting is a live signal about where a company is in its AI maturity curve. Most teams are walking past it without reading it.

Stop asking whether AI will replace you. Ask which part of your role becomes more valuable when agents handle the repetitive work. The job board is already answering that question.

Ritesh Osta built an entire framework around turning these signals into a live pipeline. Here is what that looks like in practice.

• [5:29] Why every AI job posting tells you exactly where a company is in its AI journey

• [14:50] How to identify which parts of your role agents cannot replace

• [22:01] How Ritesh’s team builds an entire pipeline from job board signals in under 30 seconds

The “AI kills jobs” story has been running for two years, and almost nobody stayed long enough to read what happened next at those same companies.

As Jensen Huang put it, you are not going to lose your job to AI. You are going to lose it to someone who uses AI efficiently.

The market is not shrinking. It is being rebuilt around roles, budgets, and infrastructure that most people have not positioned themselves for yet.

Being in that first group is a choice you make before the shift, not after it.

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Now you should have all the reasons to smile, right? DAMN... you simply can’t.

Though your velocity is at its peak. PR count went off the roof. And demo day energy was immaculate.

But the scenario changes after three sprints, whether it is agile or lean.

• Bugs multiply quietly

• Senior engineers “just tweak” things for hours

• Roadmaps look impressive, but retention is slipping

• Decision makers stay happy, your codebase does not

And unknowingly, you just entered the era of AI Slop.

I’ve spent 17 years building SaaS products, and this is what happens when teams start relying heavily on generated output. The problem isn’t speed, it’s lack of control over what actually gets shipped. I broke this down here.

Where AI Slop Starts Hurting Your Team

The Hidden Costs That Never Make It to the Dashboard

AI Slop starts quietly, right when everything seems to be working. It doesn’t arrive all at once; it creeps in through small shifts:

• Speed replaces thinking

• Output replaces ownership

• “It works” replaces “it’s right.”

There is no warning sign. No dramatic entrance.

Debug debt grows because the code is compiled and the tests are passed. Nobody questions it. By the time someone traces the root cause to an AI-generated function from two sprints ago, the fix costs ten times what a single review would have taken.

False productivity keeps metrics healthy while the backlog absorbs the damage. A Workday survey of 1,600 companies found that nearly 40% of AI’s stated value is being lost to rework and misalignment.

Decision atrophy is the quietest cost. Engineers stop questioning outputs, and one day, they can’t make the call when it matters.

I keep seeing this pattern across teams. The slop does not announce itself. It hides inside green CI pipelines and closed Jira tickets.

Watch: The Real Cost of AI Slop

What I have covered:

• [1:24] The rework tax: 40% of AI’s value is lost because employees spend nearly 2 hours fixing every single instance of AI Slop.

• [2:14] The real cost: For a 10,000-person company, unquestioned AI output costs over $9 million in lost productivity every year.

• [3:06] The trust trap: Polished output gets passed on unreviewed, and that is exactly how one person’s slop becomes the whole team’s problem

Your Best Engineers Are Paying a Tax Nobody Files

AI-generated output often looks right. It compiles, passes tests, and reads confidently. Your best engineers aren’t building; they’re fixing shortcuts, untangling logic, and reviewing work they didn’t create.

If AI saves five hours but creates eight hours of subtle maintenance later, you did not gain efficiency. You deferred clarity, and deferred clarity does not disappear.

You might also have Yes Man Engineers on your team who will embrace AI loudly, present the metrics proudly, and never raise a flag. They are your biggest risk.

I have written about this before, and I stand by every word. If output increases but outcomes do not, you are not scaling; you are decorating.

The GTM Risk Nobody Talks About in the Room

When engineering moves faster, GTM follows the noise. Marketing launches features before they mature, sales promises capabilities before edge cases are handled, and nothing damages trust faster than a feature that almost works.

Bad implementation does not stay technical. It leaks into sales calls and shows up in churn. Ritesh Osta, co-founder of Insightstap, has pointed to this clearly. The fix is not more tools. It is a cleaner execution.

Most teams add more wires. The best teams fix the flow. Want to go deeper into how teams are structuring this shift?

See What Top Teams Are Doing Differently

We are not in the "AI replaces engineers" era; we are in the "AI reveals weak engineering discipline" era. Discipline is the only thing that actually compounds here.

If you are building SaaS and AI products with real constraints and care more about judgment than hype, this newsletter is for you.

We messed up our Wednesday’s publishing streak. The irony?

We were experimenting with AI-assisted workflows to move faster. Turns out, AI can help draft content, but it cannot ship discipline.

His routine remained the same every day. Every morning, he practiced on LeetCode. Every weekend, he designed system mocks. Every night, he took notes on transformer architecture until he could explain attention mechanisms from memory.

The preparation was textbook-perfect. And yet, the rejection emails kept coming.

Not because he wasn’t qualified — but because he was preparing for the wrong interview.

Across Blind, Reddit, and Glassdoor, the same pattern has been showing up since 2025. Engineers rejected by Anthropic, OpenAI, and Google are rarely underqualified. They’re simply preparing for a different evaluation than the one being run.

I failed my Anthropic interview and came to tell you all about it so you don’t have to

Most engineers I speak to are terrified of the coding round. That’s not what ends them. The onsite is where strong candidates fall apart.

Here’s why:

• They test how you reason about AI in production, not just how you code

• Safety intuition is evaluated as a hard signal, not a soft “nice-to-have.”

• One weak answer on AI mission alignment can end an otherwise strong candidacy

• Anthropic engineers take home between $471K and $890K — the numbers are right here. The preparation bar needs to match that.

Most candidates sail through every technical round. Then they fail the final one. They want to know what you’d refuse to build. Most engineers have no answer.

The guide every engineer wishes they had before walking in.

Read the AI Engineer Job Guide before your next interview round.

AI Engineer Job Guide 2026

Everyone Told You to Practice LeetCode. That’s the Problem.

A friend sent me a Blind post that night, and one line got me:

“Pretty ironic that if you use AI to study for an AI company, you will fail.”

The chosen engineers truly understood how AI functions in production without reading the few engineering blogs available on the internet.

After interviewing engineers for eighteen years, the most impressive candidates hardly ever had the best responses.

That’s what companies like Anthropic, Netflix, JP Morgan, and Goldman Sachs actually evaluate.

I covered this in my recent video. Three things most engineers never see:

1. AI doesn’t fail in the model. It fails in the layer nobody thinks about.

2. The clearest answer beats the smartest answer.

3. Shipping AI requires a different skill set than building AI.

Here’s what I think is breaking:

1. Prepping with AI for an AI interview — AI tools often make answers more complex. Frontier Labs rewards clarity and simplicity.

2. Building to impress, not to explain — The engineers who got in didn’t have the best answer. They had the clearest one.

3. Memorising answers instead of thinking — They don’t want to hear how Uber solved a problem; instead, they want to watch how you think in the room.

You had been ready, but not for the interview that was going to be conducted. That is precisely what they test.

See the complete video

What happens when AI-generated code fails?

It was midnight, and I was still tangled up with my AWS server. Setting it up for my site had been on my to-do list way too long. Honestly, there were so many dependencies, and I was running on nothing but a strong cup of coffee.

So, I did what every developer does now: I asked ChatGPT for help.

It fired back five different solutions, and “be careful, this might delete your files” warning signals. But once you see shell commands, your brain goes on autopilot, and your fingers just start copying and pasting.

The next morning, I had to rebuild from scratch. Coffee went from optional to absolutely necessary, pretty much the only thing keeping me functional.

So, who do you blame here? Me, or the AI?

For years, I kept seeing junior developers dropping AI-generated code straight into production without reviewing it, stating, “it worked on my machine.” When it didn’t work, they gave the same excuse: “That’s not my code. GPT did it.”

AI is just a tool. You’re still an engineer. If you paste that code and merge it into the main one, it’s yours.

Satya Nadella says more than 30% of Microsoft’s code comes from AI now. Sundar Pichai says Google is at 25%. Stanford found that when developers use AI, their code actually gets less secure, even though they feel more confident.

Anthropic gets it. So when you’re in the final interview round, they’re not grilling you on system design. They’re asking you something most engineers haven’t even thought about:

“When AI wrote the code, and it broke in production, who owns that?”

Here’s what that round actually tests.

The ones who got the job had already figured this out before they walked in for the interview. The others didn’t get the offer.

If you haven’t thought about this yet, start here.

When AI-Generated Code Fails

Rejected by Anthropic. Came back 6 months later. Here's what changed.

After the rejection, he didn’t bother updating his LinkedIn. He just sat, disappointed, for a bit. Then, instead of jumping back into preparation guides, he started digging through every real Anthropic interview story he could find. Not the polished advice threads, the raw ones.

One post hit him hard. An engineer who’d made it to the final round broke down every single question, every answer, and the exact moment the conversation went sideways.

That post made him rethink everything.

He stopped obsessing over getting the “right” answer. Instead, he tried to figure out what Anthropic actually cares about:

• Read their Constitutional AI paper

• Built a little project using their API

• Practiced explaining tough concepts

So when the accountability question came up in his next interview, he didn’t panic. He’d been thinking about that question for weeks.

He got the offer.

Then he wrote it all down.

Right away, people reacted. “Finally, someone said it.” “This is the only honest breakdown I’ve seen.”

Most engineers will just read this, bookmark it, and go back to LeetCode. But a few will actually follow that link right now.

210,000 people read this every week. Most of them found it one week too late.

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PwC reports productivity increases of 50% or more across development and service roles. Tasks that once required weeks now finish in days.

Here’s when we suggest people review and make decisions while agents scale.

What risk can be brought into this race?

💥Lack of accountability
💥Unchecked automated decisions
💥Legal and regulatory risk
💥Reputational damage

Here’s what I’ve learned building AI products for 18 years with over 100SaaS companies. AI accuracy isn’t just a technical problem. It’s an operational crisis that’s quietly destroying ROI across enterprises. And most companies are addressing it completely wrong.

Implement AI Accuracy

The Gap That’s Killing Your Enterprise AI Strategy

Most organizations invest in AI projects that never reach real operations.

McKinsey found that 90% of failures happen during the shift from testing to production. At the same time, Netflix saves over one billion dollars per year through AI-driven systems.
Here is what usually goes wrong.

1. Random experiments

2. Weak foundations

3. Confused structure

The line of discipline really makes the grass greener on the other end.

What I have covered:

1. The pilot trap: Companies run random AI experiments with no systematic approach.

2. Starting in the wrong place: Successful companies follow a four-layer framework. There’s a foundational layer that comes first—skipping it kills the project.

3. Picking the wrong operating model: JP Morgan and Goldman Sachs both use the same approach: federated and central governance, distributed execution. It’s about matching reality.

Watch What The Top 5% Do Differently

The AI Roadmap that Will Save You Days

I review many roadmaps where most begin with a technical ambition. They speak about advanced models and conversational capabilities.
This is where you are hitting the wrong button to start. Effective roadmaps begin with friction.

Before choosing a model, ask:

• Where do we lose time?

• Where do we lose money?

• Where do customers lose trust?

Technology is becoming interchangeable. Your advantage comes from system design.
There’s a four-step framework separating the 39% seeing returns from those stuck in pilot mode.

Step 1: Map friction.
Step 2: Simplify first.
Step 3: Assign ownership.
Step 4: Monitor and improve.

Read What Successful Teams Do

The Startup Model Is About to Change

What happens when the definition of a company fundamentally changes?

KPMG’s 2025 report describes organizations with 10 humans and 200 AI-powered “digital colleagues.” Every back-office job is automated.

It’s “augmented intelligence”—how well your human-AI teams work together.

This isn’t sci-fi — it’s the inevitable shape of what I call the ‘autonomous company.’ And it will render traditional startups obsolete.

How will AI change business models?

New Startups will have the advantage of— ✅Capital ✅Headcount ✅ Distribution

But you need to rethink everything to build for agents, not just humans. Your org chart shifts from “who does what” to “who oversees what systems.” You become less builder, more conductor.

We’re not just automating tasks. We’re redefining what a company is.

If this challenges your current thinking, stay with me for more every Wednesday.

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I’ve watched this pattern kill dozens of B2B companies and their annual predictable growth. When the pipeline reads CODE RED, everyone scrambles for more Ads, more SDRs, more sequences, and whatnot!

What winning companies DON’T do:
❌ Run more campaigns, hoping for better results
❌ Blast more sequences to hit activity metrics
❌ Wait for buyers to fill forms and raise their hands

What winning companies DO instead:
✅ Build systems that detect intent before conversion
✅ Activate on buying signals in real-time
✅ Make revenue predictable instead of a guessing game

In a recent webinar, I spoke with Ritesh Osta, who calls himself a “GTM Engineer” instead of a marketer. His take on modern GTM stuck with me:

“You don’t have a pipeline problem. You have a signal detection problem.“

I built a full walkthrough showing how this works in practice

Watch the Demo

Brought AI to Your GTM That Fails? Here’s A Free Playbook To Fix It

Most GTM runs on AI now, or so they think. AI tools are stacked into everything that promises to unlock revenue.

Take a deep breath and accept that AI doesn’t fix fragmented GTM, so let’s stop pretending.

Your GTM operates in silos, and you’re building an expensive noise machine that burns budget faster than before.

The companies who successfully implemented have gone with the architecture-first mode:

1. Building the Signal Layer to track real buying behavior

2. Enrichment Layer to build committees automatically

3. Routing Layer to prioritize by actual probability

4. Activation Layer to engage with context

5. Measurement Layer to leading indicators only

Want to successfully implement the architecture-first mode? Then give a try to my playbook.

Download GTM Playbook

Since B2B teams are abandoning lead generation, what’s next?

Companies building predictable growth aren’t generating more leads. They’re detecting signals earlier.
Someone on Reddit decoded it right:

The industry sold us a lie: more activity = more pipeline. It’s a fail-proof idea.

The shift:

The companies that adapted the shift aren’t guessing anymore. They know which accounts are in-market before competitors do. They activate at the exact moment when buying intent appears.

If your GTM motion still relies on blasting more sequences, you are failing.

I work with B2B teams to rebuild GTM architecture from lead gen to signal detection. If your pipeline looks healthy but revenue stays UNPREDICTABLE, we need to talk about what’s actually breaking.

Subscribe to my newsletter to tap into accelerating growth in building SaaS and AI products every week.

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This is something that nobody’s saying out loud: in 2026, your shiny SaaS dashboard is becoming the world’s most expensive database.

Here are the changes I have figured out.

AI agents don’t log in. They don’t navigate tabs or click buttons.

They work while you’re offline without pressing a single button:

• Capture leads automatically

• Write personalized follow-ups at 3 am

• Track buying signals across multiple channels

• Report revenue forecasts before your Monday standup

Your CRM isn’t dying. I believe it’s just getting demoted from “where work happens” to “where data sits.”

The shift to agent-driven work raises multiple questions. Here’s my study on how enterprises are deploying AI locally.

Read the Local AI Playbook

Choosing the Wrong AI Framework: A 3-Month Loss

I've analyzed over 100 SaaS products using AI frameworks this year. Here's the pattern I keep seeing:

Teams grab LangChain or AutoGen because they’re popular, and then the production hits. It is too late to go for creativity and rigid structure when all I suggest is to choose flexibility.

What’s actually happening:

• Avoid production teams’ panic at 2 am: Speed-focused frameworks demo beautifully. Then failures start with no way to trace what broke.

• Prototyping teams lose control: AI agents chatting creatively solve problems fast. But they traded control for speed without realizing it.

• Stop making structured work rigid: When workflows don’t fit neat boxes, they’re stuck rebuilding.

You can’t throw prompts together and hope. The framework you pick today determines if you ship next week or rebuild next quarter.

Watch the Free AI Framework

The AI We Use Today Is the Dumbest

I gave ChatGPT a simple task to compare two documents and tell me what's different. It analyzed and gave me a polished answer, but it was completely wrong.

That’s why ChatGPT forgets instructions mid-conversation. Why does it hallucinate with confidence? Why it’s like hiring a genius who forgets your name every time.

We’re climbing toward Artificial General Intelligence (AGI), which is an AI that actually reasons and adapts.

In SaaS, teams that miss this lesson pay for it quietly at first, through user distrust, churn, and feature abandonment - before the product is effectively demoted from “critical workflow” to “nice-to-have automation.”

The best teams plan for when AI fails, not just when it works.
Read why today's "dumb" AI is actually your advantage

$50/Month Is Cheap Until AI Agents Do It for Free

Are you taking the road where half of your SaaS stack is about to feel overpriced? It’s because AI started a war.

This is the above scene that you are fantasizing, but NOT ME, or any other chap in the same boat!

Take a breath…

Think less “assistant” and focus more on “operator who doesn’t sleep” with me. Hey there, I am Swarnendu De. Subscribe to my newsletter to tap into accelerating growth in building SaaS and AI products every week.

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Meet Peter Steinberger and his creation: OpenClaw.

After selling his previous company, Steinberger wanted an AI that could actually do real work. Hence, he built one, and the project went from zero to 9,000 GitHub stars in a single day.

But let me share the twist that turned the tables upside down.

Within 10 seconds of announcing a rebrand that finally brought OpenClaw to light, crypto scammers hijacked the original name and launched a fake $16 million token. The autonomous ability that made OpenClaw powerful was now a security risk.

What makes OpenClaw different:

• Catches problems before they happen: proactive, not reactive

• Works the night shift: fixes bugs, sends morning summaries

• Runs locally: your data never leaves your machine

How it works: Connects through WhatsApp Monitors your systems 24/7, executes commands, fixes issues, updates you that too without waiting for permission.

What Breaks Between AI Strategy and Execution? Scale AI in 4 Steps

Recently, I came across something that stuck with me. A company launched a customer-facing AI chatbot. A customer asked about a feature, and the bot confidently gave them the name, pricing, and timeline.

Everything was fake, and the feature didn’t exist.

Here’s what I think is breaking:

1. Starting with technology instead of friction- There’s a one-sentence test that forces you to define the real problem first—no tech jargon allowed.

2. Fighting on the wrong battleground- Your competitor can replicate your model choice tomorrow but the real advantage is hidden in what you feed it, not which API you call.

3. The bill nobody’s calculating- The number you need to model isn’t your current users, it’s what happens when you grow 10x.

The problem isn’t the AI. It’s the roadmap that led to shipping it.

Watch all 4 Steps

The Most Dangerous Engineer in the Room: The One Who Always Says “Yes”

There’s always that one engineer who never says no.

They accept impossible deadlines and nod to vague needs.
Everyone loves them until the system crashes and the team burns out.

Here’s the uncomfortable truth I have learned over 18 years- blind agreement is more dangerous than bad code.

In the case of Boeing’s 737 MAX, internal emails showed that engineers knew about alarming situations that could lead to a crisis. One even wrote: “Would you put your family on a MAX simulator-trained aircraft? I wouldn’t.“

But nobody stopped it as a matter of blind nodding effect, and the plane crashed.

You might think this doesn’t apply to your SaaS startup, but the same pattern plays out every day. The question isn’t whether you have a yes-man on your team. It’s whether you are a part of the culture where saying “no” takes more courage than it should.

Read the full article to learn how to build a team that challenges instead of just agrees.

Why Successful Micro-SaaS Founders Spend Hours Reading Complaints?

Every profitable micro SaaS starts where someone spots a problem that existing tools ignore.
Here, Reddit acts as a goldmine when people come and vent out on the broken workflows.

For instance, we come across a task management problem.
A project manager posted:

The comments exploded. “Fully agree.“ “Welcome to the club.“
Someone could use this as a budding idea to build an AI that auto-follows up on incomplete tasks and highlights delays with a paid program.

How much can you actually make?
Micro-SaaS tools solving single workflow problems are hitting $10K-$50K MRR.

Swarnendu's Newsletter is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

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Let’s have tea with OpenClaw (formerly Moltbot), an AI assistant that connects with you through WhatsApp. It gives you ideas, runs ahead to act without slowing down for instructions, and keeps you going all day.

What it does:

• Fixes bugs in your code while you rest and gives you an insightful result

• Sends you morning briefings with your schedule

• Watches your servers and immediately alerts you when something breaks

• You can use it for everything, from tracking to automation, just name it.

The creator called it “Claude with hands” or Clawdbot. It clicks, types, executes commands, and manages files when brought to life.

As the name suggests, the problem lies in its wild power. A recent report on 27th January 2026 accounts for a security research disclosure of 1,000+ online breaches on the web.

Who’s Accountable When AI-Generated Code Fails?

Now that coding is faster with AI, who shall be responsible for things when they break or do not work?
We all know the line when a code fails: “GPT wrote that section.” Although AI writes the code and works on it to get it merged, it doesn’t have a master.

The real problem isn’t bad AI code. It’s that teams never changed how they work after adding AI. It can work to save your time with new-age deployments, but are you ready for it?

Adding a member must redirect a change in the workflow of the entire team. Even if the member is AI, teamwork remains the same, and hence, we are stuck with this big problem of ownership.

The line between decisions made by humans and that of AI’s thrusting capability is still blurred. This is a conversation you need to have to boost your team’s confidence and efficiency.

Read: Making AI Accountable in Your Team

The Data Problem That Breaks Every AI Project

Coping up with time when AI agents calculate and deliver before your instruction, the first question I ask here is simple: “Where is your data?”

You can’t build a reliable AI agent when your data lives in 12 different places. That’s like hiring a genius who can’t find any of your files.

Here’s what everyone skips, then wonders why their AI fails:

• Why does your data foundation matter more than your AI model?

• How to catch buying signals the moment they happen, not days later?

• When should AI stop suggesting and actually start taking action?
  
  

  Watch How AI Runs Clean with Better Data

Got A Prompt Certificate? Test Here

Stop Collecting Certifications. Start Building Systems.

Python and prompt engineering are the go-to meal for all now. LinkedIn is full of AI certifications and courses, yet you are missing something important to make a mark in 2026.

The real test isn’t what you learned in courses. It’s whether you can build AI systems with real-time deployment.

Test and build systems after you have answers to these:

• Can you design workflows with segregation in AI tasks?

• Can you integrate AI with real systems of CRM, database, etc.?

• Can you lay out boundaries for agents to perform?

Engineers who understand this are not applying for jobs but creating roles that didn’t exist last year.
The skills that matter aren’t about prompts. They’re about making AI do real work while you sleep.

Swarnendu's Newsletter is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

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The real story:

• Your CRM used to need 5 clicks to qualify a lead. Now, agents do it in 2 seconds while you sleep.

• Your ad budget needed a marketing analyst to optimize. Today, agents shift-spend between channels every hour.

• Your PM tool needed someone to assign tasks every morning. Right now, agents balance workloads when someone gets overloaded.

Satya Nadella said, “Agents with basic database abilities could replace dozens of standalone tools.”

Is SaaS dead? No, went from running the show to supporting it, and I am sharing it with you.
- SaaS gives you a complete record
- Agents give you better execution

The winners in 2026 won’t be the tools with the most features. They’ll be the platforms that:

• Make it easy for agents to get in and get data

• Expose what agents need to execute, not what looks good in demos

• Wire systems together—the UI is just the facade now

I’ve already tested the best tools and platforms for building AI agents across real projects, so you don’t have to figure it out yourself.

1. No-Code / Low-Code Platforms

2. Tools for MVPs & Lightweight Applications

3. Production-Ready Frameworks for Enterprise Deployment
   

   Get the SaaS Success Strategy Framework

   ---

Your Most Expensive Mistake - Hiring Too Many People

Late for a project? Hire someone. Too many support requirements? Hire someone. Confusing dashboard? Hire a data guy.

We think every problem needs more people, but it doesn’t

Here is what happened: Ericsson hired fast to speed things up. But the quality dropped to 38%. Managers quit simultaneously, and the work got slower instead of getting quicker.

Hiring can feel like a meaningful action, but usually it just makes things messier and goes beyond the budget.

Ask these questions first:

• How to fix your bottlenecks?

• How to motivate talent?

• How to improvise workflows to unlock growth?
  

Read the full and simple framework

Stuck at 2 Deals a Month? You’re Scaling the Wrong Thing

You have a solid product with a good sales team that closes end-to-end deals a month. But you are still unable to grow the number of deals to 10 a month.

Here’s why: You just cannot hire more people for sales as it escalates cost, requires more training, and delays decision-making.

SAP hit billions without a single internal salesperson doing implementations. Oracle copied the playbook. What did they do in the ‘90s that you are ignoring today?
The solution lies in finding partners.

-How partners, agencies, or consultants, who already work with your ideal customers, lift the game up.
-How the commission favours you to close deals with customers who already trust them.
-How you just grow your sales team by 100 without hiring anyone.

Get the full framework for building a partner-led growth strategy by Ritesh Osta, a top-rated GTM Engineer.

Still Manually Connecting Your Tools? Agents Did That Yesterday.

The manual jobs didn’t disappear, it moved.
Agents run the workflows now, and humans design the systems behind them.

Sales didn’t get better at preparation—agents took it over.

By the time a human joins the call, the lead is already qualified, researched, scored, and paired with the right proof points.

The real shift: Winners in 2026 won’t manage work better. They’ll design systems that manage themselves. Agents aren’t replacing tools, but they’re replacing the tiring manual coordination.

Hiring fails as humans shouldn’t be the glue between systems. GTM engineering works because you’re designing outcomes, not chasing heroic wins.

Most founders are hiring their fifth salesperson when they need their first intelligent framework. Now, what works is building with agents from day one.

The numbers are real:

Klarna’s AI handles 2.3 million conversations monthly—roughly 700 full-time interns’ work. Resolution times: 11 minutes to 2 minutes.

Shopify won’t hire junior roles unless managers prove AI can’t do it first.

The pattern: data entry (95% automated), research (85%), lead qualification (75%), customer support (90%).

But here’s the catch: Klarna had to rehire humans in 2025 after their AI-only approach tanked service quality. Agents need oversight, not replacement.

I mapped out 6 real agent systems that are reshaping the junior roles:
Read: How AI Agents Are Changing Junior Roles

The question isn’t whether AI can handle these tasks. It’s whether you’re building the right guide.

How Duolingo Serves 500 Million Users, Zero Crashes

Adding AI to your architecture is still on a debatable ride.

Meanwhile, Duolingo delivers 1.25 billion personalized exercises daily—each adapted in real-time to define proficiency.

Their Birdbrain model predicts the exact probability you’ll get a specific exercise right at this moment. According to Duolingo’s publicly reported studies, learners achieve 90% conversational accuracy in 4-6 weeks.

I noticed the key difference, i.e., Duolingo nurtured AI as an architect from day one.

I mapped out how they built this to achieve lightning-fast response time.
Watch: Duolingo AI’s Revolution

Where Did 98% of Your Buyers Go? You Missed AI GTM Engine

My friend Ritesh Osta is a top rated GTM engineer and he just shared something wild: 98% of B2B buyers research your product and never fill out a form.

Key Insights Covered:

1. 98% of B2B buyers never fill your forms - they research on ChatGPT, Reddit, G2, and LinkedIn instead

2. Dark Funnel signals you can track - job postings, website visits, competitor comparisons

3. AI agents help to hunt - they detect signals, enrich data, and trigger personalized outreach at scale

4. Why this works now - AI tools and intent data are finally affordable, not just for enterprise budgets

Here is the entire framework to get your leads back on track

Watch Now

Stop Guessing. Start Building With a Winning Framework

Most founders waste months building AI products wrong by copying surface patterns instead of understanding system design.

I’ve spent 17 years building AI products for 100+ SaaS companies. This is distilled into a strategic framework that takes you from concept to production-ready systems.

Build Your Strategy

Swarnendu's Newsletter is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Subscribe now

This isn’t just AI being added to SaaS. AI is changing how SaaS is built at a fundamental level.

Yet most people still think building a SaaS means big teams, big budgets, and long timelines. I want you to forget that.

Instead, think Micro-SaaS: small, focused products that solve one clear problem and make money without complexity.

If I were starting today, here’s the simple approach I’d follow:

1. Think small. Don’t build a Swiss Army knife. Build a pair of scissors. Find one annoying problem people complain about in Slack, Reddit, or Discord—and solve just that with one great feature. If something doesn’t help fix that problem, remove it.

2. Move fast. Don’t worry about “scaling later.” Use the tools you already know. Code if you can. No-code if you can’t. The goal is to launch in weeks, not months.

3. Design for sharing. Your product shouldn’t sit quietly. Get users to a quick win, meet them where they already hang out, and make the product useful enough that sharing feels natural.

I’ve put together a step-by-step blueprint for how to launch a Micro SaaS in 16 weeks (yes, it takes that much time)

Watch the Full Framework

Inside, you'll discover:

3:19 - Architecture & Build Plan
7:00 - Private Beta & Early Validation
10:58 - Flywheel Growth Plan
12:32 - Scaling Without Breaking

Is Your Manager Micromanaging Less with AI?

You are fortunate, if this is happening to you. Though not everyone is as fortunate as you are. But I am seeing my managers have started micromanaging less with AI - and that’s a good thing.

In my latest look at the workplace, I’ve found that AI is actually hovering for them. Instead of a manager breathing down your neck, AI is now tracking your every click and second of “idle time.” But here’s the twist: if you play it right, this same technology can actually be your ticket to total autonomy.

I believe you can turn the tables. Instead of being “monitored,” you can use AI to automate the status updates and reports that managers usually nag you for. I want you to stop being the victim of the “digital shadow” and start using it to prove your value without ever saying a word.

Want to know how to make your manager “disappear” using AI? You should check out the full breakdown here.

The SaaS Rewrite: This Model Is Already Breaking

The SaaS business model is being rewritten in real time. For years, SaaS growth followed one clean formula:

More users → more seats → more ARR.

That formula breaks the moment AI starts doing human work. And that moment is already here.

Klarna’s AI assistant now handles nearly two-thirds of all customer service chats across 23 markets - work that once required around 700 human agents. Resolution time dropped from 11 minutes to 2 minutes. The outcome wasn’t efficiency theatre. It was roughly $40 million added to profit.

This is why seat-based pricing is collapsing.

As Bain & Company states plainly:

“When customers need fewer humans to operate software, pricing tied to headcount stops working.”

That reality has already forced a shift. Companies like Snowflake, Twilio, and AWS no longer price around users. They price around consumption and outcomes. Snowflake now generates nearly 90% of its revenue this way. SaaS companies that follow this model see materially higher retention than those that don’t.

But here’s the uncomfortable truth.

Consumption pricing isn’t the disruption. It’s the baseline.

The real shift is deeper. The companies that win don’t just sell software anymore—they become embedded infrastructure. Quietly indispensable. Financially unavoidable.

This isn’t the future of SaaS.
It’s the present.
Until next week,

Swarnendu
Co-founder, Innofied & AllRide Apps
18 years in SaaS and AI | Author of “Orchestrate: Mastering AI in Business Automation”

What we discussed in this podcast:

Building Scalable SaaS & AI Products - I’ve revealed the foundations of cloud-native systems that scale—but there’s one architecture mistake that silently kills 90% of SaaS products.

AI for Business Automation - I’ve shared where AI actually drives ROI—but which workflows should you automate first? The answer might surprise you.

Mentoring & Leadership - I’ve discussed aligning tech with business goals—but there’s a leadership blind spot that derails even the strongest teams.

From Vision to Execution - I’ve outlined my concept-to-market process—but most founders waste months in validation. I reveal the exact fix.

Want to know what separates SaaS products that scale from those that stall? The full answers—and frameworks you can actually use—are waiting in the complete podcast.

Listen to the Podcast

SaaS: Real Flaws, Real Stories and Real Fixes

In this video, I've broken down the most costly mistakes in enterprise SaaS—from building without validation to treating security as an afterthought. Drawing from 600+ products over 18 years, I've revealed why companies that scale and those that stall aren't separated by innovation, but by execution. But there's one mistake I haven't mentioned yet that's more expensive than all the others combined. Want to know what it is?

Watch On Youtube

• Scalability and Backend Architecture (2:58) I’ve explained why multi-tenant architecture must be built from day one to avoid performance bottlenecks as enterprises scale.

• Performance and Speed (5:11) I’ve emphasized that slow performance destroys enterprise trust, with users assuming the entire system is unreliable if responses lag.

• CI/CD and Deployment Practices (7:11) I’ve demonstrated how automated deployment pipelines dramatically reduce deployment time and errors while enabling rapid iteration.

• Security as a Non-Negotiable (10:43) I’ve outlined why security must be built-in from the start, as enterprise buyers won’t proceed without clear encryption, audit logs, and access controls.

Why AI App-Building Skills Are About to Become Essential for Your Career

Teachers, marketers, accountants, and HR professionals are now building small AI-powered apps to work faster and smarter. I’ve watched this shift closely—people who can build even simple AI apps are moving ahead significantly faster. They’re not just using technology; they’re shaping it.

But there’s a specific skill within AI app-building that separates experimenters from career accelerators. What is it?

Read Full Article

Build with Swarnendu

Have a SaaS or AI product idea and want to validate it before you build? I offer free 30-minute strategy sessions to help founders and product leaders turn early ideas into scalable, investor-ready products — grounded in the same frameworks I use with global clients.

You’ll walk away with clarity on product direction, architecture choices, and the right path to MVP.

Book your free session → swarnendu.de/free-session

Book Your Free Session

Advertise Your Brand Here

Get your product or service featured in my newsletter, read weekly by over 200,000 SaaS founders, product leaders, and AI engineers.

My audience includes the people shaping the next wave of SaaS and AI innovation — from startup founders to enterprise CTOs making million-dollar tech decisions. If your product or service helps them build, scale, or grow smarter, this is where you want to be.

Ad placements fill up quickly — reserve your spot early.

Email sponsor@swarnendu.de to advertise in an upcoming issue.

Swarnendu’s Newsletter is a reader-supported publication. To receive new posts on building SaaS and AI products, subscribe to my newsletter.

Dr. Eva-Marie Muller-Stuler is a leading data and AI expert, currently serving as EY’s Data & AI Consulting Leader for the MENA region and founder of Dreva AI. With a PhD in mathematics and over two decades of global experience across IBM, EY, and Fortune 500 clients, she is known for building ethical, responsible AI frameworks and promoting diversity in technology. Her work focuses on scaling trustworthy AI systems that drive real business and societal impact.

We discussed:

1. Why do experts still struggle to decode LLM decision-making? [12:36]

2. How do enterprises use AI APIs like OpenAI, Anthropic, etc., safely? [17:07]

3. Why being seen as unethical can sink a company? [18:56]

4. The Builder Playbook – Ethics by design, insider threats, data masking [20:18]

5. The Consumer Playbook – Avoiding leaks, deepfakes, and misuse [24:22]

Watch on YouTube

Where to find Dr. Eva Marie

LinkedIn: https://ae.linkedin.com/in/dreva

Instagram: https://www.instagram.com/dreva_mullerstuler/

Website: https://dreva.ai

Nobody Wants to Pay for Your AI, Unless…

[Weekly Newsletter Article] You can build the smartest AI in the room — but if it doesn’t solve a real problem, no one’s pulling out their wallet. Here’s the uncomfortable truth about getting people to pay for AI. 

AI is everywhere today.

It writes social posts, suggests email replies, recommends your next binge-watch, and quietly powers tools you use without even noticing.

It’s in your phone, your search engine, your work apps , probably even in that fridge you thought was just “smart” enough to make ice.

And yet, despite all that presence in our lives, there’s one little snag: people love using AI… they just don’t love paying for it.

Why?

Read Full Article Here

Build with Swarnendu

Have a SaaS or AI product idea and want to validate it before you build? I offer free 30-minute strategy sessions to help founders and product leaders turn early ideas into scalable, investor-ready products — grounded in the same frameworks I use with global clients.

You’ll walk away with clarity on product direction, architecture choices, and the right path to MVP.

Book your free session → swarnendu.de/free-session

Book Your Free Session

Advertise Your Brand Here

Get your product or service featured in my newsletter, read weekly by over 200,000 SaaS founders, product leaders, and AI engineers.

My audience includes the people shaping the next wave of SaaS and AI innovation — from startup founders to enterprise CTOs making million-dollar tech decisions. If your product or service helps them build, scale, or grow smarter, this is where you want to be.

Ad placements fill up quickly — reserve your spot early.

Email sponsor@swarnendu.de to advertise in an upcoming issue.

The one who nods along in every sprint meeting, signs off on impossible timelines, accepts vague requirements without a blink, and makes even the most absurd product decisions sound doable. Everyone loves them, until the build fails, the feature backfires, or the whole system breaks under scale.

They’re not a bad coder. They’re not lazy. In fact, they’re often seen as the “go-to person”, calm, collaborative, and dependable. But they’re also the most dangerous person in the room.

Because here’s something I learned over the years:

In tech, blind agreement isn’t helpful. It’s hazardous.

I’ve seen it time and again: startups that crash from burnout, launches that go sideways, teams that lose their best talent, not because of bad code, but because no one said no. Because someone, somewhere, agreed when they should’ve questioned.

So, what do you think?

Why it happens, what it costs us, and what we can actually do about the “Yes-Man Engineer”.

Why they show up, why they thrive in unhealthy engineering cultures, and why their silence can sink your software.

Let’s see.

Why Some Engineers Always Say Yes

You’ve probably met this person. You’ll find them in every team. The engineer who works late, agrees to every request, jumps on urgent fixes without pushback, and somehow makes even the messiest deadlines look achievable. In fact, there’s often more than one.

They aren’t loud. They don’t complain. And on the surface, they seem like every manager’s dream: productive, polite, and reliable. But if you’ve led a team long enough, you know what follows: missed expectations, rising stress, and a product that’s quietly rotting from the inside.

Because somewhere along the way, we started mistaking quiet agreement for leadership. And in that silence, problems grow. Blind yes don’t just derail sprints, they corrode culture.

“Yes” is easy. “No” takes courage.

And if you’ve built or led software teams, you know exactly how rare that courage is, especially when there are deadlines, investor pressure, or a “quick hack” breathing down your neck.

Back in 1986, a NASA engineer named Roger Boisjoly saw something others overlooked or ignored. He noticed the O-rings on the space shuttle Challenger might fail in cold weather, leading to a catastrophic outcome. He voiced his concerns, warned his leadership, and even pleaded for a delay. But management, pressured by deadlines and politics, chose to proceed. Roger said no. They said yes. The Challenger launched and exploded just 73 seconds later.

Seven astronauts lost their lives.

That moment became one of the clearest, most tragic examples of what happens when technical dissent is ignored. It wasn’t just a technical failure. It was a cultural one, a system that punished hesitation and rewarded agreement, even when it was deadly.

A Pattern That Still Exists

Think the Challenger story is too old? Fast forward to Boeing’s 737 MAX. Internal emails later revealed engineers joking about safety checks, regulatory blind spots, and knowingly letting things slip.

One Boeing engineer infamously wrote, “Would you put your family on a MAX simulator-trained aircraft? I wouldn’t.”

But the project moved on. Boxes got ticked. The software got shipped. And two planes crashed.

You don’t need to be in aerospace for this to matter. In SaaS, in cloud, in mobile, this kind of agree-to-everything mindset leads to bloated features, insecure code, technical debt, and a culture where real issues get buried under fake harmony.

The Real Reasons Behind the Nod

So why do smart engineers keep nodding?

• Fear of being the bottleneck.

• Performance reviews that reward compliance over courage.

• Poor leadership that doesn’t create space for challenge.

• And let’s be honest, sometimes just sheer burnout.

Saying yes becomes a survival instinct.

We’ve all heard stories like this in retros: “I knew it wouldn’t scale, but we didn’t have time.”

That’s not laziness. That’s a systemic failure to prioritise dissent.

What Team Silence Really Means

In high-performing teams, there’s an illusion of harmony. Everyone seems aligned. Until you peel back the sprint board and find band-aid over band-aid, tech debt creeping like mold in a basement.

Groupthink isn’t just a term from psychology textbooks; it’s live and kicking in JIRA comments and code reviews.

The signs?

• Nobody asks why.

• Dissenters get labelled as “difficult.”

• Teams default to past decisions without re-evaluation.

A yes-man doesn’t always wear a smile. Sometimes, they sit quietly, letting flawed ideas pass just to avoid confrontation.

What Healthy Engineering Teams Do

Ah, now let’s talk solutions. Because no, the answer isn’t to hire contrarians who fight every spec doc like it’s a courtroom. 🙂

Great engineering cultures do this instead:

1. Normalise Saying No: In my teams, we celebrate engineers who raise red flags. “No” means someone’s thinking.

2. Create Safety for Dissent: Psychological safety isn’t a nice-to-have. It’s a product quality metric.

3. Reward Insight, Not Agreement: Promote those who challenge, not just those who deliver.

4. Run Red Team Reviews: Borrow this from security, have teams try to break the idea before it goes into build.

5. Make Leaders Model Disagreement: If your tech lead never admits uncertainty, you’re training a team of nodders.

When Saying ‘Yes’ Starts Breaking Things

At one startup I advised, there were two engineers everyone counted on. Smart, fast, calm under pressure, and always willing to say yes. Product wanted to add five features in a single sprint? They said yes. Leadership wanted a redesign done in two weeks? Yes again. Sales needed a custom API by Monday? You guessed it, yes.

They weren’t trying to impress. They genuinely wanted to help. But over time, the quality dropped. Tech debt ballooned. Bugs started slipping into production. The team began burning out, quietly.

One day, during a retro, one of those engineers finally said, “I knew this wouldn’t work. I just didn’t want to block anything.”

That was the wake-up call.

We didn’t need new tools or more process. We needed to fix the culture. I asked the team to start doing pre-mortems. Anonymous feedback. Red flags became a standard part of planning. PMs were told they could push back. Engineers were encouraged to say: “this feels wrong.”

The velocity dipped for a few weeks. But then? Stability returned. Confidence grew. The yes-men became respected challengers.

Sometimes it’s easy to find - just by questioning internally.

It’s Your Responsibility to Turn That Yes-Man into a No-Man

If you are a founder or a leader, then…

You build the culture.

You decide whether disagreement is punished or welcomed. Whether engineers feel safe to flag a bad idea or are pressured to say yes to keep the sprint moving.

Product teams don’t grow because everyone agrees. They grow when someone dares to question, to pause, to ask, “Is this the right thing to build?”

So create that space. Encourage that voice. Reward that friction.

Because the first time a quiet engineer tells you, “This won’t scale,” and you listen, you’ve already made your product stronger.

And if you’re the yes-man reading this: try saying no once this week. Even just once.

That small refusal might be the most powerful thing you contribute all month.