DEV Community: Richie Shammah

Shipping Faster Is Not the Same as Growing Faster

Richie Shammah — Wed, 12 Aug 2026 02:24:38 +0000

AI has made it easier to build products. It hasn't made it easier to build something people want, it has changed the economics of building software.

A small team can now prototype an idea, generate a working interface, connect APIs and put a product in front of users far faster than before.

That sounds like an obvious advantage.
And it is.
But there's a problem.

The ability to build faster can create the illusion that you're growing faster.

A team can ship five features in a week and still be no closer to finding customers.

It can launch version 10 and still not understand why version 1 didn't get traction.

It can have a faster development cycle while having a painfully slow learning cycle.

That's where many early-stage startups get stuck.

The new bottleneck isn't always building

For years, one of the biggest startup problems was simply getting something built.

Today, especially with AI-assisted development, that bottleneck is shrinking.

The harder question is becoming:
What should we build next, and why?

That's a very different problem.

Y Combinator's advice to ship early was never simply about producing more features. The purpose of an early release was to get something into users' hands, learn what matters, and change direction when necessary.

That distinction matters.
Shipping is an activity.

Learning is the objective.

When shipping becomes a trap

Imagine two AI startups.

Startup A releases a new feature every three days.

Startup B releases less frequently, but every release answers a specific question:

Will users pay for this?
Do they use this workflow repeatedly?
Does this solve a painful problem?
Does it improve retention?
Would users recommend it?
Does this attract the right customer?

Startup A looks faster from the outside.

Startup B may actually be progressing faster.

Why?

Because Startup B is reducing uncertainty.

That's what early-stage growth is really about.

AI makes this distinction even more important

The easier it becomes to build, the easier it becomes to overbuild.

A founder can see an idea on Monday, build it on Tuesday, add ten features on Wednesday and launch on Thursday.

But none of those actions answer the most important question:
Does anyone actually want this?

Current startup thinking around product-market fit increasingly emphasises behavioural signals such as returning, paying and referring users rather than simply shipping more product.

That changes what "moving fast" should mean.

It shouldn't mean:
How many features can we ship?

It should mean:
How quickly can we turn uncertainty into evidence?

*Think about growth as a learning loop
*
A healthier early-stage cycle looks something like this:

Hypothesis → Build → Release → Observe → Learn → Adjust

Not:
Idea → Build → Feature → Feature → Feature → Launch → Hope

The first loop creates evidence.
The second creates activity.

And activity can look a lot like progress when you're inside the company.

The metric founders should watch differently

Instead of celebrating only:
Features shipped
Releases completed
Development speed
Product updates

Start asking:
How quickly did we get the product in front of the right users?
How quickly did we receive meaningful feedback?
How many assumptions did we validate?
What did users repeatedly ask for?
What did we stop building because the evidence was weak?
Did usage, retention or referrals improve?

These questions turn product development into a growth system.

The uncomfortable part
Sometimes the fastest thing a startup can do is stop building.

If users aren't engaging with the core product, adding another feature may only hide the real problem.

Maybe the positioning is wrong.
Maybe the target customer is wrong.
Maybe the problem isn't painful enough.
Maybe the product works, but distribution doesn't
Or maybe the startup is solving a problem that customers simply don't care enough about.

More code won't necessarily fix any of those.

This is why product-market fit cannot be declared simply because a product is improving. YC's Michael Seibel has argued that founders often mistake product optimisation for having actually found product-market fit.

So, what should AI startups optimise for?

Not simply shipping velocity.
Learning velocity.

A startup that can build quickly and learn quickly has a genuine advantage.

But a startup that can only build quickly may simply reach the wrong destination sooner.

AI has made the first part easier.

The next competitive advantage will come from knowing what deserves to be built, what should be stopped, and what the market is telling you between releases.

Final thought

The best early-stage startups aren't necessarily the ones shipping the most.

They're the ones learning the fastest.

Because growth doesn't come from how quickly you can add another feature.

It comes from how quickly you can discover what makes people stay.

Visibility Starts Before Your Product Does.

Richie Shammah — Tue, 04 Aug 2026 03:17:57 +0000

At the Idea Stage, visibility isn't about chasing views or followers.

It's about building credibility before asking people to trust your product.

The founders who consistently share insights, document their journey, and educate their audience don't just launch to strangers—they launch to people who already believe in their thinking.

Build trust before launch, and growth becomes much easier after it.

The AI Positioning Crisis Nobody Is Talking About

Richie Shammah — Mon, 03 Aug 2026 02:40:26 +0000

The easier it becomes to build AI products, the harder it becomes to build memorable companies.

Every week, dozens of AI startups launch promising to transform how we work.
Many have impressive technology.
Many solve real problems.
Yet after visiting five AI startup websites, something strange happens.
You can't remember any of them.
Not because the products are similar.
Because the messaging is.
"The AI Copilot for..."
"AI-powered productivity."
"The future of work."
"Revolutionizing the way teams..."
These phrases have become so common that they no longer help customers understand why one product is different from another.
AI startups don't just have a product race.
They're entering a positioning race.
The easier AI becomes to build, the harder it becomes to stand out.
The rapid growth of foundation models, APIs and AI development tools has dramatically reduced the time it takes to launch a product.
That's great for innovation.
But it also means customers are seeing more AI products than ever before.
When everyone can build quickly, technology alone becomes a weaker differentiator.
The companies that stand out are the ones people immediately understand and remember.
The real competition isn't features.
Imagine you're looking for an AI meeting assistant.
You open five different websites.
One says:
"Your AI copilot for meetings."
The next promises:
"Smarter meetings with AI."
Another claims:
"Revolutionizing meeting productivity."
The products behind those websites may be completely different.
The messaging isn't.
If customers can't quickly understand why your product exists—or why it's different—they're more likely to move on.
Positioning answers one simple question.
Why should someone remember you instead of the ten alternatives they saw today?
That's not a branding exercise.

It's a growth strategy.
Clear positioning improves every part of a startup's go-to-market motion—from homepage conversion and product demos to sales conversations and referrals.
Companies that communicate clearly stand out.
Perplexity didn't simply present itself as another AI chatbot.
It became known for delivering answers with cited sources, giving users a specific reason to trust and recommend it.
Cursor didn't try to become an AI tool for everyone.
It focused on developers and built its product and messaging around that audience.
Lovable consistently showcases what users create with its platform rather than leading with technical AI terminology.
The emphasis stays on outcomes instead of algorithms.
These companies have different products.
What they share is clarity.
A simple positioning test
Before publishing your homepage or launching your product, ask three questions.

Can someone explain what your product does in one sentence? If not, your messaging is too complex.
Can they explain why it's different? If not, you're competing on features instead of positioning.
Will they remember your product tomorrow? If the answer is no, neither your customers nor your marketing will do the work you expect.

Final thought
AI is making software easier to build.
It isn't making companies easier to remember.
As more startups enter the market, the winners won't necessarily be those with the largest feature lists or the most sophisticated models.
They'll be the ones people can describe without opening the website again.
In a crowded AI market, being memorable is becoming just as important as being technically impressive.

Why X is still one of the strongest platforms for founders in 2026.

Richie Shammah — Fri, 31 Jul 2026 03:45:23 +0000

X is still one of the strongest platforms for founders in 2026.

Not because every post goes viral.

Because the platform still rewards the things founders need most:

• Direct access to customers.
• Conversations instead of just broadcasts.
• Distribution through replies, not just followers.
• Searchable ideas that continue getting discovered.
• A community that actively discusses products, launches, and growth.

Most founders don't need millions of impressions.

They need the right 100 people to see what they're building.

That's still one of X's biggest strengths.

The founders who treat X as a place to contribute not just promote, are usually the ones who benefit the most.

Distribution follows participation.

AI Startup Growth Audit #001: Can PureBox Become the Future of Email Productivity?

Richie Shammah — Mon, 27 Jul 2026 04:12:06 +0000

Every AI startup doesn't need a revolutionary product—it needs a clear positioning and a scalable distribution strategy.

For this edition of my AI Startup Growth Audit, I analyzed PureBox, an AI-powered Gmail assistant built around one simple but powerful promise: nothing moves until you approve.

In this audit, I break down:
• Product & pricing
• Market positioning
• Distribution strategy
• Growth opportunities
• Founder visibility
• Final growth score

Overall Growth Score: 86/100

The product is strong. The next challenge is building awareness and becoming the default choice for AI-powered inbox management.

Which AI startup should I analyze next? 👇

The Distribution Gap Killing Early-Stage AI Startups.

Richie Shammah — Mon, 27 Jul 2026 02:35:05 +0000

Why shipping faster isn’t enough when nobody discovers your product.

Every week, dozens of AI startups launch on Product Hunt, Hacker News, Reddit and X.

Most disappear within days.

Not because the product is bad.

Not because the founders aren't talented.

They disappear because they confuse launching with distribution.

Building has become dramatically easier in the AI era. Getting noticed hasn't.

I call this the Distribution Gap:

The distance between building a valuable product and consistently putting it in front of the people who need it.

Why this happens

AI has made product creation cheaper

Models, APIs and no-code tools have lowered the barrier to shipping.

The bottleneck has shifted.

Everyone launches in the same places

Product Hunt.

Reddit.

LinkedIn.

Show HN.

Launch videos.

When everyone follows the same playbook, attention becomes scarce.

Distribution isn't treated like product development

Founders measure:

commits

features

bugs

uptime

But rarely measure:

audience growth

founder visibility

referral loops

community engagement

content reach

The Pattern in Practice

The Distribution Gap isn't theoretical. You can see it in how AI startups approach growth.

Some products gain traction because they treat distribution as part of product development. Others struggle because they assume a launch alone will create lasting demand.

Two examples illustrate this difference.

Example 1, Perplexity AI

Distribution wasn't just marketing, it became part of the product.

While many AI startups focused on building better chat interfaces, Perplexity AI positioned itself around one clear promise: delivering answers backed by cited sources.

That positioning made it easier for users to explain why they preferred it over alternatives.

But the company's growth wasn't driven by product improvements alone.

Its founders and team consistently shared product updates, demonstrated new features publicly, engaged with users on X, and encouraged people to compare results with competing AI tools.

Each product improvement became a reason for users to return, share screenshots, and recommend the platform to others.

The lesson isn't that Perplexity had the best technology.

It's that they reduced the gap between building and being discovered.

Example 2, Lovable

Building in public accelerated adoption.

Lovable entered a crowded market of AI coding assistants.

Instead of relying solely on paid marketing or launch-day attention, the team consistently showcased what users were creating with the product.

Social feeds became filled with short demonstrations, user-generated projects, founder updates, and community discussions.

This created a feedback loop:

Users built projects.

They shared them online.

Those posts attracted new users.

New users built more projects.

Rather than treating users as customers, Lovable turned them into part of its distribution system.

The product spread because people enjoyed showing what they had built.

The lesson is that distribution becomes far more effective when the product naturally encourages sharing.

These companies differ in their products, audiences and strategies.

But they share one important characteristic:

Distribution wasn't treated as a campaign.

It was treated as an ongoing capability.

That's the mindset many early-stage AI startups still overlook.

A simple framework: The Richie 4D Framework

Discoverability
Can people find you?

2.Differentiation

Do people instantly understand why you're different?

Distribution

Do you have repeatable channels that aren't dependent on launch day?

Defensibility

Will users continue recommending you after the initial excitement?

My Observation

After analysing lots of AI startuplaunches, I've noticed that founders often spend months refining product features but only begin thinking about distribution a few weeks before launch. By then, they're trying to build awareness from scratch in an increasingly crowded market.

The startups that maintain momentum tend to start much earlier. They build audiences, educate potential users, gather feedback publicly and create anticipation well before the product is available.

In other words, they don't launch into an audience, they launch with one.

Conclusion:

AI has dramatically reduced the time it takes to build software.

It hasn't reduced the time it takes to earn attention.

In fact, the easier it becomes to launch, the harder it becomes to stand out.

The startups that win over the next decade won't simply build faster.

They'll distribute better.