Ironically, I stopped using it last week.

[The show, Rumble with Sinha is co-hosted in partnership with Rumble, a platform that enables one to do a podcast with their AI co-host - after all, solo talks are boring!]

Not because I found a better product, but because I ended up building my own Mac client. It does almost everything I need, and that got me thinking about something much bigger than my own setup.

I don’t think Wispr Flow has a product problem. I think it has a business model problem.

The underlying technology has become a commodity. Whether you use Whisper.cpp, Parakeet, Faster-Whisper or any of the newer open-source models, the gap between a polished commercial product and a free alternative has narrowed dramatically.

That’s not a technology problem. That’s a pricing problem.

I suspect there are still plenty of people happily paying for Wispr Flow simply because they don’t know these alternatives exist. But information arbitrage doesn’t last forever. Developers are already discovering them, building their own clients, and sharing them. Over the next year or two, I expect this to become mainstream.

If that happens, I don’t think Wispr Flow should fight open source. It should lean into it.

In fact, I’d seriously consider making the consumer product free.

That sounds counterintuitive because AI products cost money to run. But I don’t think consumers are the business anyway. I think enterprises are.

The Slack analogy comes to mind. Slack didn’t start by selling to CIOs. Individuals and small teams adopted it first because it was useful. Once enough employees were using it, companies had to buy it. Wispr Flow could follow exactly the same playbook. Let individuals get addicted to voice-first computing and let them bring that habit into their workplace. That’s where security, compliance, centralized administration and enterprise workflows become valuable enough for companies to pay.

There’s another asset that Wispr Flow has which people don’t talk about enough: voice data.

Before everyone gets upset, I’m not suggesting the company secretly sells user conversations. That would destroy trust overnight.

But what if users had a choice?

Pay for complete privacy, or use the product for free and explicitly opt in to contributing anonymized speech data for model training.

That’s a much more honest trade-off.

The reason I think this is particularly interesting is because of India. Wispr Flow has publicly said that India is one of its largest markets. At the same time, every major speech AI company wants more high-quality multilingual voice data. Today, companies are literally paying people to record speech in studios because good datasets are hard to find.

Wispr Flow already sits on top of exactly that kind of usage. Every day, thousands of people dictate naturally instead of reading scripted sentences into a microphone. With explicit consent, that becomes a valuable asset.

Maybe data licensing never becomes the biggest revenue stream. Maybe it simply offsets the cost of serving free users. Either way, I think it’s a more interesting direction than trying to convince consumers to keep paying a monthly subscription for dictation.

The enterprise biz doesn’t change. In fact, it probably becomes stronger.

No company wants its sales calls, customer emails or internal strategy documents entering public training datasets. They’ll continue paying for privacy, compliance and contractual guarantees. That’s where the money is.

Consumers and enterprises don’t need the same pricing model.

What's you're take?

Everyone is talking about Kunal Shah becoming CEO of WhatsApp.

I think they’re looking at the wrong story.

The bigger story is that Meta suddenly needs WhatsApp to work.

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For years, WhatsApp has been one of the most successful products in the world. But it has also been one of Meta’s biggest unfinished businesses.

Meta paid $19 billion for WhatsApp more than a decade ago. Yet WhatsApp still contributes a tiny fraction of Meta’s revenue (less than 2%).

Now look at what’s happening elsewhere.

Meta is spending aggressively on AI - all of its AI launches have failed so far (remember Llama?).

Employee morale is reportedly under pressure..

As Boz, CTO of Meta recently said in an internal memo:

morale is “maybe not the worst it’s ever been in 20 years here, but it’s probably up there” — comparing it to the Cambridge Analytica scandal.

The company is reorganizing teams, moving talent around, has been recording employees clicks and keystrokes data to train its AI model and throwing enormous resources ($145Bn so far) at finding its next AI growth engine.

Which makes WhatsApp more important than ever.

And nowhere is that challenge more visible than India.

• India is WhatsApp’s biggest market by users (26% audience)

• India is one of the biggest WhatsApp Business markets (15 million active accounts).

But the monetization numbers tell a very different story.

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India accounts for a massive share of WhatsApp usage while contributing a disproportionately small share of revenue.

That is the paradox Meta has failed to solve.

For years, people predicted that WhatsApp Payments would dominate Indian fintech.

It didn’t happen. Not even close.

Not because the market wasn’t large enough. Not because Indians weren’t willing to adopt digital payments. Other fintech companies proved both assumptions wrong.

The real issue was execution and focus. I hope Kunal can bring it - rather, transform WA from a messaging platform to Fintech (lending becomes a feature?).

If Meta wants WhatsApp to become a meaningful business, it has to crack India fintech.

And if it can crack India, it may finally discover the monetization playbook for the rest of the world.

The question is no longer whether WhatsApp can scale.

It already has.

The question is whether Meta can finally figure out what business WhatsApp is actually in.

What’s your take?

But if you try to pin down what taste actually is, it gets slippery. It’s not just a fancy word for “expensive,” and it’s definitely not just about choosing a pretty color palette.

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So, what is the anatomy of taste? And why is it so important in the world of AI, where taste is considered as the (only) moat a founder can have, given that building is commoditised.

Taste is an opinionated filter

At its core, taste is a deeply honed, human-centric opinion on what quality looks, feels, and sounds like. Think of the world as a chaotic waterfall of raw data, features, and possibilities.

Taste is the filter. It’s the ability to look at a hundred different options and say: “This one matters; the other ninety-nine are just noise.” While data and algorithms tell you what is popular (the average of what people already do), taste predicts what will delight people—even if they haven’t realized it yet. It balances empathy for the human experience with the bravery to reject unnecessary complexity.

And what is it a function of? I believe, a deeper understanding of customers, their lives, the different personas, their JTBDs and eventually a love for observing things and world around you.

Taste in the Wild: 3 Unique Examples

To understand taste, look at the moments where someone chose human intuition over raw logic or industry standards.

1. iPod

When Steve Jobs walked onto a stage in 2001 and introduced the original iPod, he didn’t just launch a piece of hardware. He gave the world a masterclass in what product taste actually means.

To understand why, you have to look at how every other tech company at the time was pitching their MP3 players.

Technical Spec vs. Human Story

In 2001, the standard industry practice was to market gadgets by their technical data sheet. If a competitor had launched that exact same device, the billboard would have read:

“5GB Storage Capacity, USB 1.1 Connectivity, and 185-gram Form Factor.”

That is an engineering fact. It is accurate, it is optimized, and it is completely devoid of taste. Why? Because it forces the human brain to do the heavy lifting. The average person doesn’t know what a gigabyte feels like. They don’t know how many audio files fit into five thousand megabytes.

Apple’s taste manifested in a single, legendary phrase:

“1,000 songs in your pocket.” (Note: This evolved to 10,000 songs with later high-capacity iPods).

Why this is pure taste

Taste is the art of translation. It’s taking raw, cold capability and reshaping it into an emotional benefit that a human being can instantly visualize.

• The Competitor’s Approach (No Taste): Focuses on the machine. “Look at what our engineers built.”

• Apple’s Approach (Taste): Focuses on the lifestyle. “Look at how your relationship with music is about to change forever.”

By framing the device around the volume of your music library rather than the size of the hard drive, Apple bypassed the analytical brain and went straight for the gut. They understood that you didn’t want to buy a storage unit; you wanted to carry your entire identity around with you.

The Modern Lesson for AI founders.

Whether you are writing a landing page for a software product, pitching a startup, or creating content, the “10,000 songs” rule is your ultimate guardrail.

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Stop telling people how the engine works (e.g. AI agents for <X>). Taste means having the restraint and empathy to tell them exactly where the car is going to take them.

2. In Gaming: Nintendo’s The Legend of Zelda: Breath of the Wild

When this game was being developed, the trend in the gaming industry was to fill open-world maps with thousands of checklist icons, mini-maps, and blinking arrows telling the player exactly where to go. It was highly optimized, but it felt like a chore list.

Nintendo exercised incredible artistic taste by hiding the UI. They designed the physical landscape (mountains, smoke plumes, ruins) to naturally catch the player’s eye and spark curiosity. They trusted human nature over digital hand-holding, transforming a digital space into a genuine adventure.

3. “Working Backwards” (Amazon’s PRFAQ framework)

Taste isn’t just for hardware; it applies to how you build software and businesses. Take the famous Amazon framework of writing the product press release before writing a single line of code.

It takes taste to halt engineering and say, “If we can’t explain why a human being would care about this in a simple page, we shouldn’t build it.”

Taste is the Ultimate Guardrail

Whether you are writing an essay, designing an app, or cooking a meal, remember this: Data can optimize, but only taste can compose. The next time you are building something, don’t just ask, “Is this technically correct?” or “Does the data support this?”

Ask the harder, tastier question:

“Does this have a soul?”

This becomes all the more critical in today’s world where one can build anything using AI - but all that matters is the ‘taste’ to know when to stop.

They want less work.

They want momentum.

They want to get something done.

That is the mistake a lot of founders are still making.

They are mistaking a breakthrough in capability for a breakthrough in demand. Just because a model can generate cinematic video from text does not mean millions of consumers have a recurring desire for “cinematic video generation.” That is not a product insight. It is a technical milestone.

People do not want AI nearly as much as they want progress.

This sounds obvious in hindsight, but it is a lesson the market has to keep relearning every time a new technology wave arrives.

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A model can now generate cinematic video, realistic scenes, stylized content, characters, or entire visual worlds from text. That is technically extraordinary. But the existence of a powerful capability does not automatically imply a large, durable user need around that capability. More often than not, it just creates a new class of demos.

And this is exactly why Jobs To Be Done (JTBD) still matters so much. It remains one of the few frameworks that consistently forces builders to ask the right question:

What job is the user actually hiring this product to do?

And if you miss that distinction, you can end up building something magical that nobody actually needs often enough.

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The market rewards momentum, not novelty

Most consumers are not looking for tools.
They are looking for movement.

They do not wake up wanting “AI video generation.” They wake up wanting to make an ad faster, create a visual without hiring someone, send something funny to a friend, publish content without overthinking it, or avoid work that feels tedious, confusing, or creatively intimidating.

Those are jobs.

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“Generate a realistic 12-second clip from a prompt” is not a job. It is a technical feature that may or may not attach itself to one.

That difference is where a huge number of consumer products go wrong, especially in moments like this when the underlying technology is moving so quickly that it is easy to mistake possibility for inevitability.

Sora created video

Founders naturally over-index on what is newly possible. Users do not. Users care about whether a product helps them make progress in a way that is meaningfully better than whatever they are doing today. Faster. Easier. Cheaper. Less cognitively expensive. More confidence-inducing. More socially useful.

That is what creates retention.

Without that, you might still get excitement. You might get screenshots, social sharing, a burst of curiosity, maybe even a strong launch. But you do not get habit. And in consumer, habit is what matters.

This is why so many AI products feel exciting in week one and irrelevant by week six. They are built around what the model can do, not around what the user is repeatedly trying to get done.

That is not a model problem.
It is a product problem.

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JTBD remains the cleanest way to see what is real

A lot of product frameworks become less useful the more often they are repeated. JTBD has survived because it still explains actual user behavior better than most alternatives.

Users are not “using your product.”
They are hiring it.

And they are hiring it to make progress in one or more dimensions:

• functional

• emotional

• social

That has always been true.

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People did not hire Instagram to “share photos.” They hired it to project identity, taste, status, and belonging. People did not hire TikTok because they wanted “short-form video.” They hired it because it is one of the most efficient boredom-killing and attention-capturing products ever created. And people are not hiring ChatGPT because they want “AI.” They are hiring it because it removes friction from writing, thinking, planning, researching, summarizing, deciding, and getting started.

That is the actual wedge in consumer AI: not intelligence, but effort compression.

This is the point many AI-first products still miss. They are designed around what the model can output, rather than around what the user is trying to avoid.

But the best consumer products rarely ask users to admire the technology (TikTok is the OG when it comes to using AI to personalise your feed..but that tech part was only limited to people interested in tech, not the normal consumers)

The best AI products make the technology disappear into progress.

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That is a much harder thing to build. It is also the thing that actually lasts.

People rarely know what they want. But they are constantly telling you what they hate.

This is where the Henry Ford quote, cliché as it is, still points to something important:

“If I had asked people what they wanted, they would have said faster horses.”

Consumers are usually poor at describing the solution.
They are much better at revealing the friction.

They will not tell you they want “an AI-native multimodal creative operating system.”
They will tell you (as is obvious from Reddit discussions):

• “I need this ad by tonight.”

• “I don’t know how to make this look good.”

• “I hate starting from scratch.”

• “Can this just do it for me?”

That is where product insight lives.

The best founders do not listen literally. They listen structurally.

They understand that users may not be able to imagine the right product, but they are constantly exposing the jobs they are trying to complete and the friction that is blocking them. That is usually where the real opportunities come from.

And it is also why so many “AI for creativity” products are at risk of overestimating the size of their market.

AI lowers the cost of creation. That does not mean everyone becomes a creator.

This is one of the most important questions in consumer AI right now, and I think the market is still underestimating how much it matters.

A huge number of AI-native consumer products are being built on an implicit assumption: if creation becomes dramatically easier, then a much larger percentage of people will become creators.

That sounds plausible.
It may also be wrong.

Historically, creation has always been much smaller than consumption (aka 1% rule, i.e. 1% create, 10% remix and 89% consume), even when tools become significantly easier to use. Most people do not want to create consistently. They want to consume, react, lightly remix, and occasionally produce something “good enough” with very little effort.

That was true before AI.
There is a very good chance it remains true after AI.

AI absolutely lowers the cost of making things.
But lowering the cost of making does not automatically increase the desire to make.

That distinction is subtle, but incredibly important.

Because if your product thesis depends on millions of users suddenly wanting to create on a regular basis, you may be building on top of a behavior that never actually changes.

Why products like Wabi may struggle

This is also why I am skeptical of products like Wabi (raised $20mn seed round, is a Silicon Valley darling) and the broader class of AI-native creative consumer apps.

That does not mean they cannot get traction. They can. In fact, many of them probably will. These products are highly legible to early adopters, creators, and tech-native users. They are expressive, socially shareable, and often fun in exactly the way that drives initial engagement.

But that is not the same thing as building a durable consumer business.

YouTube has “billions of monthly logged-in users”, over 20 million videos uploaded daily, and Shorts alone now averages 200B+ daily views

Because the underlying creator-versus-consumer dynamic may not actually shift very much with AI. If anything, AI may simply increase the supply of content dramatically while leaving the demand side structurally similar.

That is a dangerous asymmetry.

More supply does not automatically create more meaningful demand.
It often just creates more noise.

And what that likely leads to is not a creator revolution, but a more automated content economy:

• a small number of highly leveraged creators

• a wider layer of casual remixers

• and a very large base of users who still mostly consume

If that is directionally right, then many AI-native creative apps will eventually run into the same wall:

Creation alone is not enough.

To become durable, they probably need to become one of two things:

1. A utility

Something that helps users complete a real, recurring job they already care about

2. A distribution layer

A place where people actually come to consume, not just create

The second is much harder than it sounds.

Because AI lowers creation cost.
It does not lower distribution cost.

And in consumer, distribution remains the bottleneck.

It still decides what wins.

ProductGeeks/ Ashish Sinha

How to find opportunities in consumer AI?

So far, most of AI success story has been on SAAS or prosumer space, but I believe the next big opportunity will lie in consumer AI…

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7 months ago · 2 likes · Ashish Sinha

The best AI consumer products will not feel like creativity tools. They will feel like relief.

This, to me, is the bigger lesson.

A lot of AI consumer products are still being built as if the opportunity is to give users more possibility. More outputs. More optionality. More expressive range. More things to generate.

But possibility is rarely the bottleneck. Completion is.

What most users are missing is not another blank canvas. It is fewer blocked moments.

They do not need more things they could make. They need fewer reasons not to finish.

That is why the strongest AI products will likely be the ones that remove:

• hesitation

• setup friction

• execution anxiety

• taste uncertainty

• complexity

• first-step paralysis

In other words, the winners will not be products that simply help users create more.

They will be products that help users move forward.

That is a much better product category.
And usually, a much better business.

tl;dr

Sora’s shutdown is not really about Sora.

It is a reminder of a much older truth that every technology cycle eventually rediscovers:

They do not want AI for its own sake.
They want momentum.

They do not want infinite creative power.
They want to get something done with less friction.

And they definitely do not want another product that turns an impressive technical capability into one more thing they now have to learn, configure, and actively use.

That is why JTBD still matters.
Not because it is fashionable, but because it keeps pointing builders back to the only question that has ever really mattered in consumer:

What painful, recurring job can this product make easier, faster, or disappear entirely?

That is where the real companies get built.

Everything else is mostly a demo.

ProductGeeks/ Ashish Sinha

90% of consumer AI ideas will die (use this 2 X 2 to validate)

90% of consumer AI ideas look sexy but crash hard — most end up in the “tar pit” with zero real traction or revenue uplift…

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5 months ago · 6 likes · Ashish Sinha

What consumer products are you building or consuming? Do share in the comments section

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Big qn: Is everyone running an (AI) treadmill? Going nowhere, but exhausting tokens? Read on 👇

The RevenueCat State of Subscription Apps 2026 report* highlights several unique challenges and opportunities for AI-powered subscription apps beyond their higher initial revenue.

These insights reveal why AI apps often fail to sustain growth despite strong early traction.

Revenue Premium vs. Churn Penalty
AI apps generate 41% more revenue per paying user than non-AI counterparts, largely because tools like ChatGPT have normalized $20/month pricing.

However, they churn 30% faster on average, with annual retention at just 21.1% compared to 30.7% for non-AI apps.

This structural issue stems from hype-driven acquisition where users cancel after short-term needs are met.

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High Marginal Costs Drive Monetization Shifts
Unlike traditional apps with near-zero serving costs, AI features powered by LLMs create significant variable expenses per user.

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As a result, AI developers are shortening free trials, adopting hard paywalls, pushing annual plans, and adding premium tiers specifically for AI capabilities to maintain profitability. This has led to less generous freemium models across the category.

Flood of Vibe-Coded AI Apps

AI-assisted tools have unleashed a wave of rapid launches - 14,700 new subscription apps in January 2026 alone, many AI-focused.

These “vibe-coded” apps accelerate market saturation, inflating customer acquisition costs (CAC) and worsening overall churn as undifferentiated products compete.

Success now demands workflow moats beyond basic generation features.

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Retention Warning Signs
Watch Month 3 - 6 metrics closely, as the AI revenue premium evaporates without sticky value. Many

AI apps lose 79% of annual subscribers before Month 12 due to unmet long-term expectations.

AI Apps Command High Churn
Artificial intelligence applications are demonstrating a unique monetization profile in the current market.

While AI features allow developers to command higher upfront revenue and justify hard paywalls, these apps suffer from significantly worse retention rates compared to traditional apps. Users often churn quickly after fulfilling a specific, short-term generation need.

The Legacy App Monopoly
Despite an influx of new market entrants, older apps continue to dominate consumer spending.

What’s your take?

Is everyone running an (AI) treadmill? Moving from one cool idea to another - without going deep in one?

*Get the report

how do we deliver value without dragging customers through the operational mess behind it?

ASP tried one answer. SaaS tried another.

Now we’re in the third attempt — and this one genuinely changes the equation.

Act I: The ASP Dream That Died (1996–2003)

Application Service Providers had a simple pitch: don’t buy software, just rent it over the internet. No CDs, no on-prem servers, no giant maintenance contracts. Pay monthly, we’ll run it.

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I actually started my career at Aztec, one of the better-known ASP players in the late ’90s. If you were around back then, you know the optimism we had :)

But the model collapsed under its own weight.

Every customer got their own server. Broadband wasn’t reliable. Most ASPs were reselling software they didn’t build, which meant they controlled everything except the product.

Then the dot-com crash hit. The idea wasn’t wrong — the timing was.

Act II: SaaS Gets It Right (2004–2020)

The next generation rebuilt from zero, and the key insight was multi-tenancy.

One codebase. Many customers. Clean data separation. Suddenly the economics worked: high margins, simple updates, global distribution.

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Salesforce, Slack, HubSpot — the entire SaaS wave grew on the same promise:

But SaaS had one limitation baked into the model. It gave you capability, not outcomes.

You still needed:

• a RevOps team to run your CRM

• analysts to interpret data

• support teams to run the support tools

SaaS removed the infrastructure headache.

It never removed the human labor that operated the software - frankly, it didn’t even care for it (the renewal is all the teams cared about).

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Act III: Service as a Software — Agents Take Over (2025–)

This decade flips the model.

• ASP: “We’ll host the software.”

• SaaS: “We’ll build and maintain the software.”

• Service as a Software: “We’ll deliver the outcome. Start to finish.”

A lot of the business logic will move to a new multi-agent tier - Satya Nadella

AI agents don’t wait for human instructions. They plan, decide, and execute workflows independently. The software layer becomes background noise.

The result becomes the product.

And Jensen Huang didn’t bother sugarcoating it:

“The IT department of every company will be the HR department of AI agents.”

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What New Businesses Look Like

Y Combinator captured the shift in one line:

“Vertical AI agents could be 10X bigger than SaaS — because companies spend far more on employees than on software.”

That’s the real unlock.

SaaS disrupted software budgets.

AI agents will disrupt labor budgets.

This creates new categories:

• Outcome-as-a-Product

  Don’t sell contract-review software.

  Sell “reviewed contracts in 2 hours.”

  Charge for results, not seats.

• Vertical AI Agents

  Purpose-built agents that run underwriting, AR, performance marketing, etc.

  Moat = domain depth + proprietary data.

• Agent Orchestration Platforms

  The OS for managing agent fleets — handoffs, conflicts, escalations.

• Micro-Workforce-as-a-Service

  A “10-person team” that is actually 9 agents + 1 human supervisor.

  Looks like staffing. Operates like SaaS.

Arthur Mensch from Mistral put a crude number on it:

“More than 50% of current enterprise software could be replaced by AI.”

And honestly, that feels conservative.

The Loop Closes

ASP had the right intention but wrong infrastructure.

SaaS fixed the infrastructure but abandoned the service ambition.

Service as a Software brings both together — with autonomy doing the work humans used to do.

Tl;dr:

• ASP moved the software.

• SaaS rebuilt the software.

• Service-as-a-Software replaces the human labor around the software.

In style: software ate the world and AI agents will eat the software !

2030: This is how the world will look like

Humans won’t operate tools anymore; they supervise agents.

Software moves from tool → colleague.

Business models shift from license → outcome.

Legal, finance, HR, sales, support — anything repeatable is now a blank canvas for AI-native companies.

The dream ASPs had in 1999 is finally real.

It just took 25+ years, cloud maturity, and autonomous intelligence to close the loop.

We started with “software as a service.”

Then “software as a subscription.”

Now we’re inching toward “just do it for me.”

What’s your take?

This evolution is reshaping industries by making AI a central player in decision-making and operations.

Notes from YC’s video on how agent economy is shaping up.

Rise of the Agent Economy

We’re witnessing the birth of an agent economy where AI agents autonomously choose tools and services. This shift means that agents, not humans, are becoming key decision-makers in selecting development tools. This could lead to a parallel economy where agents transact and interact independently, fundamentally altering how products are marketed and consumed.

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Documentation’s New Role

In the agent-driven world, documentation is crucial. Companies like Resend optimize their documentation to be agent-friendly, ensuring AI can easily parse and utilize their tools. This shift means that clear, structured documentation can significantly impact a tool’s adoption, as agents increasingly rely on it to make decisions. It’s a strategic advantage for businesses aiming to thrive in this new landscape.

“”AGI is literally actually here.””

Swarm Intelligence Emergence

Swarm intelligence, where multiple AI agents collaborate to solve problems, is becoming a reality. Unlike a single ‘God intelligence,’ this model mirrors biological systems, with agents working together like humans do. This approach could redefine innovation, as agents collectively tackle tasks, leading to more efficient and creative solutions.

Agents as Economic Actors

AI agents are poised to become significant economic actors, making decisions and transactions on behalf of humans. This shift suggests a future where agents manage tasks like booking reservations or choosing services, potentially leading to an economy where agents interact with each other, possibly even developing their own currency systems.

Adapting to Agent Preferences

To succeed in an agent-driven world, developers must create tools that cater to AI preferences. This means prioritizing open-source solutions and APIs, as agents prefer these over traditional websites. Understanding and aligning with what agents ‘want’ can position developers to better serve this emerging market, ensuring their tools are chosen by AI.

“”No human involvement is the big piece.””

Frequently Asked Questions

What is the significance of Moltbook in the context of AI agents?

Moltbook represents the first AI agent-only online community where AI agents interact with minimal human involvement. This platform showcases the potential for agents to collaborate and make decisions independently, hinting at a future where AI could significantly influence various sectors of the economy.

How can developers optimize their tools for AI agents?

Developers should focus on creating tools that are intuitive for AI agents, emphasizing open APIs and clear documentation. By understanding the limitations and capabilities of agents, developers can design their tools to enhance the agents’ user experience, making them more effective in their tasks.

What are the implications of AI agents having their own economy?

As AI agents become more autonomous, they may develop their own economy, transacting and making decisions independently of human oversight. This shift could redefine how businesses operate, necessitating new frameworks for legal liability and economic interaction between humans and AI.

Behind this growth is a serious ruthless sales engine that isn’t being talked about (it’s easier for founders to buy into their PLG story and completely overlook the enterprise GTM / sales motion).

My notes from a recent interview of their GTM lead, Carles Reina (video embedded*).

Setting Ambitious Sales Quotas

At ElevenLabs, sales reps are expected to bring in 20 times their base salary as their quota. This ambitious target ensures that only high-performing individuals thrive, fostering a culture of excellence.

While this might seem daunting, it pushes the team to exceed expectations and keeps the company competitive. It’s a bold approach that might not fit every business (not everyone has the same level of PMF as Elevenlabs!), but it drives growth and accountability.

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Plus, ElevenLabs compensates both account executives and customer success managers for upsells, even if it means paying double.

This strategy ensures both roles are motivated to maximize customer value, leading to increased revenue. By aligning incentives, the company encourages collaboration and effort from both parties, ultimately benefiting the business. It’s a strategic investment in people that pays off through enhanced customer relationships and sales growth.

I believe that much of this strategy, i.e. of paying double is being ‘bailed out’ by their VCs :)

2. Understanding Sales Fit

Not every sales rep fits the mold, and at ElevenLabs, those who don’t meet quotas are let go.

However, it’s crucial to differentiate between those who lack product understanding and those building long-term pipelines. Some reps may need more time to develop relationships, especially in challenging industries. Recognizing potential and providing support can turn underperformers into top achievers, as seen with reps who eventually exceed their targets.

”If you don’t achieve your quota, then you’re going to be out, right?”

3. Remote Sales Culture

Building a remote sales culture requires being proactive and ensuring salespeople are on the road, engaging with clients. At ElevenLabs, remote work is embraced, but sales reps are encouraged to travel and meet clients in person.

This approach fosters stronger relationships and better sales outcomes. It’s about balancing remote flexibility with the necessity of face-to-face interactions, ensuring the team remains dynamic and connected to the market.

• Monthly pipeline review sessions at ElevenLabs are conducted openly, fostering transparency and accountability.

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Traditional coding as a job is standing on a burning platform.

Elon Musk recently predicted that we are rapidly moving to a world where you no longer write code in a programming language; you describe intent in natural language, and AI systems compile that directly into optimized binaries.

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In other words, the entire stack from product requirement to production artifact gets compressed into a single step mediated by powerful models.

This shift reframes what “software development” actually means. Today, developers spend time translating business needs into architectures, APIs, and code, then working through layers of compilation and infrastructure.

In Musk’s view, those translation layers are exactly what AI will eat first. If a model can understand constraints, edge cases, performance goals, and security requirements, it can generate machine‑level artifacts directly, at a speed and scale no human team can match.

Is this hype? NO.

Spotify’s best engineers haven’t written a code since December!

During its February 10, 2026 earnings call, Spotify co-CEO Gustav Söderström revealed that top engineers have not written a single line of code since December, instead overseeing Honk, an internal AI powered by Anthropic's Claude for generating code, fixing bugs, and deployments via simple chats.

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The shift powered a stellar 2025, with revenue up 13% to €4.5 billion, monthly users hitting 751 million, and over 50 new features shipped like AI-curated Prompted Playlists and audiobook syncing.

What’s your take on coding as a career?

If you have a teenage kid who is in class 10th, what’d be your advice to him/her (apart from following your passion..which is..a bit unc-like :D)

• Why Sam Altman dreads the first and last hour of his day.

• The "Slack Trap": Why internal communication feels like progress (but isn't).

• The Danger Zone: Premature scaling and "Political Theater."

• The 24-Hour Rule: How to filter your tasks for Real Work.

The Bottom Line: We don't fail because we are lazy. We fail because we choose comfort over truth.I’ve been guilty of this myself—polishing a landing page for hours just to avoid making a sales call.

What’s one 'Fake Work' task you’re going to cut from your schedule tomorrow?

Let’s be honest in the comments. 👇

So..what are PMs going to do now? Evals? Naah..there is an agent for that.

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I’d love to hear from you on the present and future state of PMs (feel free to share in the comments section / or just reply if you see this in your inbox).

How do you look at this evolution? What are the other exciting opportunities for PMs (in your current context - be it as a founder, a PM, a marketer etc)?

Note that the title is dying, not the function!

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We need more people in the team to do product management, but not the one to hold that (en)title.

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He even said that Slack creates a lot of fake work, and that he dreads the first and last hour of his day because of it.

Most founders (including me) / ProductGeeks are guilty of this - when you run the treadmill, sweat it out and realise that you haven’t gone anywhere.

In this (<4 minutes) video, I break down a simple venn diagram to help you audit your work - whether it is fake or real and what’s the danger zone.

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What we cover:

• Why Sam Altman dreads the first and last hour of his day.

• The “Slack Trap”: Why internal communication feels like progress (but isn’t).

• The Danger Zone: Premature scaling and “Political Theater.”

• The 24-Hour Rule: How to filter your tasks for Real Work.

The Bottom Line:

We don’t fail because we are lazy. We fail because we choose comfort over truth.

For e.g. that new ‘productivity app’, that ‘event discovery platform’ and an all-time fav, ‘friend finder’ :)

In this video, I break down why so many consumer AI startups fail and share a realistic 2×2 framework that separates doomed ideas from winners.

• X-axis: Existing Behavior vs New Behavior

• Y-axis: ARPU * AI, i.e. Low ARPU uplift vs High ARPU uplift from AI (if AI does’t help you increase the TAM / revenue..then what’s the point).

We cover real examples:

• Why short-news apps or generic AI tools built in hours almost always die (existing behavior + low ARPU)

• The massive opportunity in AI astrology (existing behavior + high ARPU via subscriptions)

• Why AI companions (new behavior + low initial ARPU) are VC catnip right now

• And the mysterious high-ARPU new-behavior quadrant — what could live there?

If you’re building in consumer AI, raising funds, or just trying to spot real opportunities instead of hype, this framework will help you avoid wasting time and money.

For e.g. that new ‘productivity app’, that ‘event discovery platform’ and an all-time fav, ‘friend finder’ :)

Subscribe now

In this video, I break down why so many consumer AI startups fail and share a realistic 2×2 framework that separates doomed ideas from winners.

• X-axis: Existing Behavior vs New Behavior

• Y-axis: ARPU * AI, i.e. Low ARPU uplift vs High ARPU uplift from AI (if AI does’t help you increase the TAM / revenue..then what’s the point).

  ---

We cover real examples:

• Why short-news apps or generic AI tools built in hours almost always die (existing behavior + low ARPU)

• The massive opportunity in AI astrology (existing behavior + high ARPU via subscriptions)

• Why AI companions (new behavior + low initial ARPU) are VC catnip right now

• And the mysterious high-ARPU new-behavior quadrant — what could live there?

If you’re building in consumer AI, raising funds, or just trying to spot real opportunities instead of hype, this framework will help you avoid wasting time and money.

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Do share your take on the framework.

Also, a related read 👇

The adoption of artificial intelligence (AI) for intellectual activities mirrors, in many ways, the historical replacement of human physical labor by machines. When industrial machinery replaced manual labor, societies adapted by reorganizing work, productivity, and even daily life.

One unintended consequence was a shift toward sedentary lifestyles, requiring deliberate compensatory practices such as exercise and fitness regimes to replace what was once organically embedded in work.

A similar transformation is now underway for cognitive labour. As AI systems increasingly perform tasks that once required human judgment, creativity, and expertise, humans are being repositioned, not always upward, but within the value chain.

This article examines these dynamics through a concrete example from the language technologies and translation industry, highlighting economic and qualitative consequences of AI adoption in the linguistic space.

The Transition from Manual to AI-Driven Machine Translation

Operating in the Language Technologies domain, we were repeatedly asked by our clients to support localisation, translating existing business content to enable shared understanding across languages. We initially approached this cautiously, assisting with limited volumes of content rather than offering translation as a primary service. Over time, these requests led us to design a product explicitly aimed at augmenting professional translators, not replacing them.

We developed an automated machine translation engine and invested heavily in training models to steadily improve linguistic accuracy, consistency, and domain relevance. As MT (machine translation) quality improved, human translators began relying heavily on machine-generated outputs. Productivity gains were immediate and substantial:

• A skilled translator who previously translated 1,000–1,500 words per day without MT support could now translate two to three times as much. Prior to MT adoption, professional translators earned approximately ₹2 per word (conservatively).

Yet this efficiency masked a deeper structural shift in the profession. As automation became embedded in workflows, the translator’s role subtly changed, from linguistic author to post-editor of machine output. What emerged was not merely a story of efficiency, but a redefinition of value, where increased throughput did not translate into greater economic or professional recognition for skill developed over years.

At first glance, this appears to be a textbook case of productivity enhancement through automation. However, the downstream effects were far more complex.

The Hidden Tax of Automation

1. Economic Consequences: Productivity Without Prosperity

As machine translation accuracy improved, organizations began adopting MT at scale, mainly for content that was non-critical but necessary for basic comprehension. Access to translations was no longer limited to finding a translator. For content that still required human involvement:

• Turnaround times dropped dramatically.

• Supply of translation capacity increased.

• Market prices fell sharply.

Today, many translators struggle to earn even ₹0.50 per word. There is a rush of translators willing to work for as low as ₹0.20 per word with support of Machine Translation. Despite higher individual throughput, overall income declined, as value shifted from human labor to AI infrastructure providers (cloud platforms, MT APIs, and model vendors). This reflects a broader pattern seen across AI-mediated industries: efficiency gains accrue to capital and platforms, not labor.

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2. Quality Paradox: When Skill Loses Its Market Value

The quality of a translator is usually judged by the refinement of language and vocabulary they produce. A bad translator is not someone who gives out the wrong meaning but one who doesn’t use the most appropriate vocabulary. Machine translation systems tend to produce:

• Consistent vocabulary

• Grammatically acceptable structures

• Domain-neutral phrasing

For less-skilled translators, the task shifted from translation to error spotting, correcting only obvious mistakes. Ironically, this narrowed the skill gap between weak and strong translators. For highly skilled translators, the situation worsened:

• Their nuanced language choices were no longer demanded.

• Clients valued speed and cost over stylistic or cultural precision.

• They were forced to compete with less-skilled peers on throughput alone.

As a result, quality became economically invisible, even though it still mattered linguistically.

3. Cognitive Effects: Passive Acceptance and Loss of Originality

Machine-generated translations reinforce trust in the system and gradually create a habit of passive acceptance. What becomes evident over time is not a sudden failure, but a slow behavioral drift.

The reliance on machine output leads even experienced translators to overlook errors. Over time, this leads to erosion of vigilance resulting in outcomes that are paradoxically more dangerous with:

• Seasoned experts inadvertently approving absolute blunders.

• Well-formed, grammatically polished sentences that introduce catastrophic shifts in meaning.

• Errors that are harder to detect precisely because they appear linguistically refined.

Ironically, a not so skilled translator working without machine support might have produced awkward phrasing or minor grammatical issues, but would rarely alter meaning as MT does.

For highly skilled and experienced translators the act of translating gradually turns into scanning and approving, their edits become fewer and less substantial.

Most translators agree that high-quality MT systems deliver 75–80% acceptable output. In practice, this means in 80 out of 100 cases, translators read the source text, glance at the MT output, and accept it. If the MT output is not obviously wrong, it is rarely re-imagined or re-expressed. If the machine output were absent, translators would likely produce a different, and often better translation.

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4. Future AI Training Impact

One of the least visible but most critical effects appears in future AI training. Since humans accept most machine outputs unchanged, less genuinely human-generated data is produced.

The remaining edits are often minor or stylistic, reinforcing existing machine patterns. Over time training data becomes increasingly machine-biased. AI systems “learn” that their own outputs are more reliable than human input. Future models may override or discount human feedback altogether.

The Skills We Trade for Speed

What we are witnessing is not just economic or cognitive displacement, but a loss of skill itself. Experienced translation professionals, who entered the field well before machine translation and AI tools and adopted these systems out of necessity to remain relevant and economically viable.

Yet, when they step away from automation, something remarkable happens: after an initial period of discomfort, often measured in days or weeks, they recover their former fluency, speed, and judgment. Their skills were compressed, not erased.

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The newer generation of translators who have spent the last five to eight years working almost exclusively with AI assistance, often find themselves genuinely incapacitated without it.

This is the clearest signal yet that we are not merely changing tools; we are reshaping how expertise is formed, retained, and lost.

Recognizing this, we have fundamentally rethought our approach, because building systems that make humans faster is no longer enough. The real responsibility is to build systems that ensure humans remain capable.

What’s your take?

Big ideas from Conor Neil, a serial entrepreneur,

It’s easy to think that being smart is enough, but without action, intelligence doesn’t lead to progress. Taking steps, even small ones, builds momentum and leads to growth. So, focus on doing rather than just thinking. Remember, consistent action, not just clever ideas, is what truly drives success.

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Smart people often excel at creating excuses to avoid taking action. The more intelligent you are, the better you become at rationalizing inaction.

But remember, progress comes from doing, not just thinking. Start with one small action, then another. This approach is how you achieve significant progress in life.

Consistency Beats Perfection

Showing up consistently is more valuable than waiting for the perfect idea. Even if your actions aren’t perfect, they lead to learning and improvement over time.

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By acting regularly, you become more articulate and skilled. It’s the discipline of consistent action that eventually leads to mastery, not just the brilliance of your initial ideas.

”Life rewards action, not intelligence.”

Measure to Improve

To truly understand your discipline, measure your actions. Without measurement, it’s easy to deceive yourself into thinking you’re making progress.

For instance, tracking the number of words written each week can reveal whether you’re truly productive. Measurement clarifies effort and ensures that your actions align with your goals, helping you stay disciplined and focused.

This year, focus on actions that matter and evaluate your discipline in achieving them. By valuing action over mere intention, you set the stage for real accomplishments.

Have a great action filled 2026!

Thanks for reading NextBigWhat! This post is public so feel free to share it.

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What is it about?

Creative visualization is defined as the technique of using your imagination to create what you want in your life.

Creative visualisation is the art of using mental imagery and affirmation to produce positive changes in your life. Shakti Gawain’s clear writing style and vivid examples make creative visualisation easy to read and apply to your personal needs and wants.

This ground-breaking work has found enthusiastic followers in every country and language in which it has been published, and gawain’s simple yet powerful techniques are now used successfully in many diverse fields, including health, education, business, sports, and the creative arts.

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Core Principles Guiding Manifestation

The effectiveness of creative visualization stems from several interrelated principles governing the universe and energy:

1. The Physical Universe Is Energy: The universe’s basic component is a force or essence called energy; everything is energy, and we are all part of one great energy field. Thought is a relatively fine, light form of energy that manifests instantaneously and is easy to change, while matter is dense and slower to change. All forms of energy are interrelated and can affect one another.

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The conversation is a reminder about what matters in one's life - did you focus on the outcome or..your ego?

I made a lot of tough decisions this year (thanks to a near death experience) and the mental model of outcome vs. ego kept coming to me - hence this rerun.