Udit Akhouri

Category: Uncategorized

  • AI is No Longer a Moat

    AI is No Longer a Moat

    First principles – Value lies in outcomes, not in methods.

    When user are buying a product, they are buying the results – not the technology. Users don’t buy AI, they buy saved time, reduced friction, higher revenue, lower costs, fewer headaches, etc.

    Methods matters only to us, builders. Outcomes, matters to the real users.

    If I go a restaurant to eat sushi, I don’t care if a machine prepared it and saved the restaurant owner five bucks on each plate. I just care about the good taste of the sushi.

    Initially, it might be possible that I get attracted towards a restaurant which is the first to use a machine to make it, but over time, as it becomes more and more common, the initial hype dies.

    Thus once a method becomes ubiquitous, it stops being a differentiator.

    That’s where AI sits today.

    The Shift Happened Quietly

    A year ago, adding ‘AI-powered’ to your landing page felt like a moat. It signalled novelty. Intelligence. Future.

    Today, every product is AI-powered. The good ones, the bad ones, the exceptional ones that VCs back, all of them.

    Analytics tools, supply chain systems, healthcare autonomy, customer support, CRM, design – anything you can imagine, is AI-powered.

    The same can be seen in the trend of YC-backed startups as well –

    Source – https://www.reddit.com/r/ycombinator/comments/1fbb9m0/the_rise_of_ai_companies_in_yc/

    But the real problem is when everything claims AI, nothing differentiates.

    Users have stopped really caring.

    Some are even skeptical now. For many users, ‘AI’ just means unreliable, unfinished, hallucination-prone MVPs or another thin wrapper over ChatGPT. Instead of trust, it sometimes, creates doubts.

    The novelty phase is over. AI has become infrastructure.

    What Made This Click for Me

    When building SuperDocs, I shipped the product in two days. No heavy landing page. No elaborate positioning.

    • You land on the site.
    • You paste your GitHub repo
    • Docs get generated.

    That’s it.

    Nowhere did we say: ‘AI-powered documentation generator.’

    Instead, the message was simple: ‘Generate documentation in minutes.’

    And users didn’t ask:

    ‘Does it use AI?’

    They cared about one thing:

    ‘Does this save me time?’

    And it did.

    That was the realisation. AI wasn’t the product. The outcome was.

    Builders Are Marketing the Wrong Thing

    Most of the AI products in today’s market position themselves like –

    ‘Old solution + AI’

    Ex.-

    • Documentation tool with AI
    • Customer support with AI
    • Analytics with AI
    • Marketing tools with AI

    But if ten competitors say the same thing, no one stands out.

    Saying ‘we use AI’ is equivalent to saying:

    ‘We use databases.’

    ‘We run on cloud.’

    ‘We use APIs.’

    These are implementation details that users don’t care about.

    The real question is:

    Why you? Why now? Why better?

    Differentiator Must be Visible in Outcomes

    Real differentiation sounds like:

    • Generate docs in one click
    • Reduce support tickets by 60%
    • Cut onboarding time in half
    • Automate reports in seconds
    • Save teams 10 hours per week

    Now the user understands value immediately.

    The conversation shifts from:

    ‘What tech do you use?’

    to

    ‘What problem do you eliminate?’

    And that’s the only thing users ever cared about.

    When Should AI be Marketed?

    Okay, it’s not that should completely ditch AI from your marketing plan. There are cases where AI is still worth highlighting.

    But only when it creates a defensible advantage, not when it’s a wrapper.

    For example:

    • Custom models trained on proprietary industry data
    • Ultra-low latency inference giving real-time advantage
    • Domain-specific intelligence competitors cannot replicate
    • Unique performance benchmarks
    • Workflow intelligence unavailable elsewhere

    Example positioning:

    ‘Hotel management platform powered by models trained on millions of hotel data points.’

    Now, AI is the moat, not just the tool.

    But if you’re calling an API or building a thin layer over general models, AI is not your differentiation.

    Your product experience is.

    AI has Become Electricity

    Nobody markets products as”

    ‘Powered by electricity.’

    Electricity is assumed. Invisible. Expected.

    AI is heading in the same direction.

    Soon every tool will use AI in some capacity. The winners won’t be those shouting about it. They’ll be the ones who make it invisible.

    The best technology disappears into experience.

    What Builders Should Do Now

    Stop asking:

    ‘How do we show we use AI?’

    Start asking:

    ‘What outcome improves because of AI?’

    our messaging should translate technology into impact:

    Not:

    • AI powered workflows

    But:

    • Finish workflows in 30 seconds

    Not:

    • AI-driven automation

    But:

    • Eliminate manual work entirely

    Not:

    • Intelligent recommendations

    But:

    • Increase conversions by 20%

    Users don’t want intelligence.
    They want results.

    Final Though

    AI itself is not magical to users.

    The magic is when life becomes easier.

    If your product saves time, reduce efforts, or removes complexity, users will care. Whether AI is involved or not becomes irrelevant.

    So, the next time you write your landing page or pitch or product, try removing the words ‘AI-powered’.

    If the value still stands, you’re building the right thing.

    If it doesn’t, you’re probably selling the method, not the outcome.

    And outcomes are what endure.

  • The Modern Developer’s Dilemma: Speed Without Shared Context

    The Modern Developer’s Dilemma: Speed Without Shared Context

    AI Summary : Agent-assisted coding dramatically increases individual developer velocity, but it optimizes for local task completion rather than global system coherence. As more code is generated by agents, shared human context erodes: architectural intent becomes implicit, debugging shifts from causal reasoning to probabilistic trial-and-error, and teams lose durable mental models of their systems. This creates a structural coordination problem that compounds over time, especially as less-experienced developers ship increasingly complex systems. The real opportunity is not faster code generation, but tooling and workflows that preserve developer memory, shared context, and alignment between human reasoning and machine execution.

    Agent-assisted coding optimizes for local velocity, not global coherence. The result is codebases that no human fully understands and teams that cannot reason together effectively.

    I saw this firsthand while building Exthalpy.

    My team was small. Four developers. Initially, they barely used AI. Output was slow, but everyone understood what was being built and why. The system had friction, but it had coherence.

    I pushed the team to adopt AI-assisted coding aggressively. Velocity spiked. Features landed faster than expected. On paper, it looked like a pure win.

    In practice, context collapsed.

    One developer would ship a large change using an agent. The rest of the team would immediately lose the mental model of what had changed, how it worked, and why decisions were made. Most of the code was no longer written by humans. It was assembled by agents optimizing for task completion, not for shared understanding. Even a single day of absence was enough for the system to drift. The team could build a lot in 24 hours, but when we tried to reason about a specific behavior, nobody had durable context. The knowledge lived in private prompts and transient chats, not in shared artifacts.

    This is not a tooling problem. It is a coordination failure.

    Agentic development shifts cognition from developers into models. Locally, this is rational. You maximize throughput and reduce cognitive load. Globally, coherence erodes. Engineers stop forming deep internal representations of the system. Architectural intent becomes implicit. Interfaces drift. Invariants weaken. The codebase becomes legible primarily to machines.

    Agent-assisted coding changes how developers internalize systems. When most logic is produced through agent-assisted coding workflows, human context decays faster than teams realize.

    I saw the same pattern in my own workflow. In one project, I spent three hours debugging a Supabase issue manually. It was slow, but the context stuck. I understood the failure mode deeply. In another project, built rapidly through vibe coding with minimal documentation, that understanding evaporated within weeks. The code remained. The mental model did not.

    Speed erased memory.

    For solo developers and small teams embracing agentic workflows, this becomes a hidden bottleneck. You can move fast, but you cannot reliably compound understanding. Debugging becomes probabilistic. Collaboration becomes fragile. Scaling becomes risky because no one can reason confidently about system behavior.

    This is structural, not temporary.

    The number of inexperienced developers is increasing rapidly. Agentic tools allow them to ship systems far beyond their underlying understanding. As more software is produced this way, technical and coordination debt compound non-linearly. Better agents will increase output, but they will not automatically restore shared human context. They may accelerate its erosion.

    The core failure is that developer cognition has no durable memory layer.

    Today, context lives in scattered fragments: chat histories, local experiments, partial docs, forgotten mental notes. None of it compounds across time, projects, or teams. We have optimized heavily for generating code and almost not at all for preserving the reasoning that produced it.

    If developers could store working context in a persistent, queryable local “mind palace” — decisions, constraints, failures, architectural intent — they could compound understanding instead of leaking it. For teams, this memory layer would need to synchronize across contributors and environments, preserving shared cognition as systems evolve.

    The opportunity is not to make agents faster at writing code. It is to make humans better at retaining and transmitting understanding while machines operate at scale. The winning developer stack will optimize for coherence, memory, and alignment between human reasoning and machine execution.

    Speed without shared context is not leverage. It is latent fragility.