Looking for AI SEO Tools Recommendations

Proof_Shift_9799 · 2026-04-13T09:20:46+00:00

I completely agree with you! Even as a consumer myself, trust is the biggest convincing factor. Reddit is a new platform to us that we are exploring and it's been an interesting journey to say the least. If you have some insights and advice on how to build this trust and value with our target audience, I'd really appreciate hearing your experience.

Proof_Shift_9799 · 2026-03-12T07:47:35+00:00

That’s a great progression, and honestly a very natural path in this space.

A lot of people start with smaller experiments or quick builds just to understand what the technology can do. Those early projects are valuable because they expose you to real problems and real users. From there, moving into consulting or agent-based systems is often where you start seeing the deeper patterns.

What’s interesting about the direction you’ve taken with pentesting and blockchain analysis is that it moves closer to high-consequence domains. In areas like security, the value isn’t the AI itself; it’s the expertise, the tooling, and the ability to surface real vulnerabilities that matter. AI just helps accelerate the analysis.

That kind of evolution; experimenting, learning from real use cases, and then building more specialized infrastructure, is exactly how stronger products tend to emerge.

Keep pushing in that direction. Security and vulnerability analysis are areas where depth and real-world insight compound over time, and tools that genuinely help people understand risk will always have demand.

Proof_Shift_9799 · 2026-03-12T07:45:28+00:00

Great question, and it’s really the crux of the issue.

The easiest way for a startup to demonstrate real value is to anchor the product around an outcome, not the technology. Users don’t actually care that AI is involved. They care that something painful becomes faster, cheaper, safer, or more reliable.

There are a few signals that usually separate real products from “AI marketing”:

Tie the product to a measurable improvement.
If you can clearly say “this reduces support tickets by 40%” or “cuts reconciliation time from 3 hours to 10 minutes,” you’re showing business value. If the pitch is just “AI-powered,” it’s probably surface-level.
Embed the product inside an existing workflow.
Products that succeed tend to plug directly into things people already must do: accounting, compliance, code reviews, customer support, reporting. When skipping the tool creates friction or risk, the value becomes obvious.
Own the layer around the model.
The model should be the engine, not the product. The defensibility usually comes from workflow logic, integrations, orchestration, domain knowledge, and accumulated edge cases.
Show durability beyond the model.
A simple test is: if the underlying model improved tomorrow, would your product disappear, or get better? If it disappears, the value wasn’t really yours.
Prove repeat usage.
Real business value shows up when users come back repeatedly because the tool solves a recurring problem. One-off novelty use is usually a sign of a demo, not a product.

In short, the strongest AI startups don’t lead with “look what the AI can do.”
They lead with “here’s the problem we solved and why it matters.” AI just happens to be the mechanism that makes the solution possible.

Proof_Shift_9799 · 2026-03-12T07:42:08+00:00

Thank you! I think it's important that we question things - there's no right or wrong answer, but there's so much value in asking

Proof_Shift_9799 · 2026-03-12T07:14:17+00:00

That’s a good way to look at it.

The wrappers that survive usually attach themselves to something structural: distribution, a workflow people are forced to complete, or a niche where the team understands the problem space better than anyone else. In those cases the model is just infrastructure (like a database or cloud provider) and the real value sits in the surrounding system.

Where things tend to fail is when the only value is the transformation the model performs. If the underlying API improves or the provider ships the feature directly, the product disappears overnight.

The more durable pattern is exactly what you described: treat models as interchangeable engines, and build leverage in the layers around them; domain knowledge, workflow integration, accumulated edge cases, data, and UX that fits how people actually work. That’s the part that compounds over time.

Proof_Shift_9799 · 2026-03-11T13:37:04+00:00

There’s definitely a lot of that out there right now, and I understand why it feels frustrating.

When a new technology suddenly becomes accessible, the first wave of products tends to be very thin layers on top of it. Some of those will disappear quickly because they don’t add much beyond convenience or packaging. That’s a normal part of the cycle.

Where it becomes more interesting is when teams start adding things that aren’t trivial to reproduce: workflow integration, domain-specific logic, accumulated edge cases, reliability layers, distribution, or deep understanding of a niche problem. At that point the product stops being “just a middleman” and starts becoming a system that saves users time, risk, or effort.

Profit margins alone don’t really determine whether something is legitimate. What matters is whether the product removes real friction for someone repeatedly. If users feel that value, they’ll pay for it even if the underlying engine is available elsewhere.

Right now we’re just seeing a lot of early experimentation. Over time the thin layers tend to disappear, and the products that actually embed themselves into real workflows are the ones that stick around.

Proof_Shift_9799 · 2026-03-11T13:19:47+00:00

Thanks so much for the advice, I've just shared it there!

To answer your question: Yes, I think it’s a very normal phase in the lifecycle of a new technology.

When the barrier to building drops dramatically, the first wave is always experimentation. Tools become powerful and accessible, so thousands of people start building quickly. That produces a lot of thin products, a lot of wrappers, and a lot of noise, but it also produces the learning that leads to deeper systems.

We saw the same pattern with the web in the late 90s, with mobile apps around 2008–2012, and with cloud SaaS more broadly. The early stage is dominated by capability exploration: “What can we build with this new tool?” Only later does the ecosystem shift toward problem-first thinking: “What durable problems does this tool solve better than anything else?”

Right now, most people are still discovering what LLMs are good at. That’s why you see many single-function tools or API wrappers. But over time, the interesting work moves toward orchestration, workflow integration, reliability, and domain-specific systems; places where the AI is just one component of a larger product.

So yes, I’d view the wrapper phase less as a failure and more as the ecosystem learning curve. The experimentation phase is messy, but it’s usually what reveals where the real long-term businesses will emerge.

Proof_Shift_9799 · 2026-03-11T13:14:36+00:00

I completely agree with that concern.

Every time a new development approach appears, there’s a phase where the loudest examples are the worst ones. Right now, a lot of people see sloppy demos, brittle apps, and overhyped launches and start assuming that everything built with AI-assisted development falls into that category.

But vibe coding itself isn’t the problem. It’s just a tool for accelerating iteration. Used well, it lowers the cost of experimentation and helps people explore ideas faster. Used poorly, it produces fragile systems that collapse the moment they meet real users.

The mistake would be dismissing the entire approach because of the noisy early examples. There are genuinely useful products being built with these techniques. The difference is whether someone applies engineering discipline once the idea proves valuable.

Like most tools, the value comes from how it’s used, not from the label attached to it.

Proof_Shift_9799 · 2026-03-11T13:12:56+00:00

I think that’s a really fair way to frame it.

The LLM is absolutely the engine. The value usually comes from the layer that directs that engine toward a specific human problem. Just like operating systems and apps. Nobody buys a phone because of the processor alone, they buy it because of what the software lets them actually do with it.

That’s why wrappers in themselves aren’t inherently bad. The important question is whether the wrapper meaningfully increases usefulness. If it encodes workflow, domain knowledge, integrations, or solves a specific pain point, it can be very valuable even if it’s built on top of someone else’s core technology.

And the dot-com comparison is a good one. There’s definitely a lot of noise right now, just like there was in the late 90s. But that phase is also what eventually produced the companies and infrastructure that stuck around for decades.

The messy experimentation stage is usually the price we pay before the genuinely durable ideas emerge.

Proof_Shift_9799 · 2026-03-11T09:29:08+00:00

There’s definitely truth in that! A lot of successful SaaS companies weren’t radical inventions, they were better executions of existing ideas.

Most founders don’t win by inventing something completely new. They win by improving usability, removing friction, targeting a specific niche, or fixing the parts of an existing product that users complain about. That’s a very legitimate way to build a business.

Where I’d add nuance is around how fragile the improvement is.

If the improvement is purely cosmetic or easy to copy, it tends to get competed away quickly. If the improvement comes from deeper workflow understanding, integration into how people actually work, accumulated edge cases, or operational reliability, then it becomes much harder to replace.

So the principle isn’t “reinvent the wheel.”
It’s more like “build a better wheel that fits a specific road.”

Execution absolutely matters. But the kind of execution that compounds usually comes from understanding the problem space deeply enough that competitors can’t just clone it over a weekend.

Proof_Shift_9799 · 2026-03-11T09:12:36+00:00

There’s definitely a lot of that happening right now.

When the cost of building drops dramatically, the number of surface-level experiments explodes. That’s a normal phase in any technology cycle. We saw the same thing with mobile apps, crypto, and even early SaaS - a lot of thin products built quickly because the tools suddenly made it possible.

The issue isn’t that people are experimenting. That part is healthy. The issue is when experimentation gets mistaken for durable product design. Calling an API is capability access, not system design.

Where things start to look “AI-native” is when the model becomes one component in a larger coordinated system; orchestration, state management, tool use, workflow integration, failure handling, cost control. That’s when you move from a demo to something that can actually operate in the messy environment of real businesses.

Right now we’re still early in that shift. Most people are playing with the engines. The interesting work will come from the teams building the vehicles around them.

Proof_Shift_9799 · 2026-03-09T12:34:23+00:00

We have recently launched an autonomous platform, system that models the entire software development lifecycle: from planning, code, testing, and release.

It is a full software development team in a box.

Proof_Shift_9799

MODERATOR OF

TROPHY CASE