Prompt

Is Agentic AI and AI code the end of the middle man? Will companies build their own apps instead of hiring 3rd parties? Is it worth it offering development services of Apps powered by AI?

Answer

Short answer: no, agentic AI is not the end of the middleman—but it is the end of undifferentiated middlemen. It changes who survives and why.

Let’s break this down clearly and realistically.

1. Will companies just build their own apps now?

Some will. Many won’t.

What will happen

Agentic AI + AI coding tools dramatically lower the barrier to:

• Prototypes
• Internal tools
• Simple CRUD apps
• One-off automations
• “Good enough” solutions for a single team

A product manager + a staff engineer + Copilot/Claude/Devin-style agents can now do work that once required a small dev shop.

So yes:

• Internal teams will build more
• Budgets for basic dev work will shrink
• “Build me an app” as a commodity is dying

What won’t happen

Most companies:

• Don’t want to maintain systems
• Don’t want to think about security, scaling, uptime, audits
• Don’t want to manage prompt drift, agent failures, or AI governance
• Don’t know how to turn “an app” into a reliable business system

AI writes code.
It does not own outcomes.

That’s where the middleman still lives.

2. Is agentic AI killing dev agencies and freelancers?

It’s killing a specific type:

❌ “We build apps”
❌ “We integrate AI”
❌ “We do custom software”
❌ “We automate your workflow”

Those are now table stakes.

Who will get wiped out

• Offshore dev shops selling hours
• Agencies reselling boilerplate + frameworks
• Freelancers competing on speed and price
• Teams whose only value was execution

If your pitch can be replicated by:

“Give this repo to an AI agent”

You’re exposed.

3. So… is it still worth offering AI-powered app development?

Yes—but only if you reposition aggressively.

The value has moved up the stack.

The new winning offers look like this:

1. Problem ownership, not code ownership

Clients don’t want an app.
They want:

• Faster decisions
• Lower risk
• Revenue protection
• Compliance
• Fewer humans in the loop

If your offer is:

“We deliver a system that reduces X cost by Y% and survives audits”

You’re safe.

2. System design + AI orchestration

Agentic AI introduces new failure modes:

• Agents hallucinating actions
• Silent errors
• Non-deterministic behavior
• Tool misuse
• Escalation loops

Most companies cannot design:

• Guardrails
• Human-in-the-loop escalation
• Observability for agents
• Cost controls
• Kill switches

That’s expert work.

3. AI as a managed capability

This is big.

Instead of:

“We build your app”

You offer:

“We run and continuously improve your AI system”

Examples:

• Agent performance monitoring
• Prompt/version management
• Model swaps
• Cost optimization
• Security reviews
• Drift detection

This becomes retainer revenue, not one-off projects.

4. The new “middleman” is actually more valuable

AI removes labor, not accountability.

Someone still must:

• Decide what should be automated
• Decide what should never be automated
• Own failures when AI acts incorrectly
• Explain decisions to regulators, executives, customers

That person/company is now:

• Architect
• Risk manager
• AI product owner
• Translator between business and machines

Most internal teams don’t want that liability.

5. Practical advice for you (based on your background)

Given:

• Your Azure AI certification
• Your interest in AI-driven due diligence and trading tools
• Your focus on lean, high-signal portfolio projects

Avoid this

• Generic “AI app development”
• Chatbot wrappers
• “Agent that does everything”
• Selling implementation hours

Lean into this

• Vertical-specific systems (finance, compliance, ops, research)
• Decision-support, not decision-making
• AI + rules + human checkpoints
• Auditable, explainable pipelines

Example positioning:

“We build AI systems that assist decision-making under regulatory and financial constraints.”

That’s not something Copilot replaces.

Final verdict

Agentic AI doesn’t eliminate the middleman.
It eliminates the unskilled middleman.

If you:

• Compete on execution → you lose
• Compete on speed → you lose
• Compete on cost → you lose

If you:

• Own outcomes
• Design resilient systems
• Manage AI risk and lifecycle
• Specialize deeply in a domain

👉 You become more valuable, not less.

Prompt

“Is Claude code the end of the middle man? Will companies build their own apps instead of hiring 3rd parties? Is it worth it to offer ai services based on Claude services?”

Answer

These are great strategic questions about the AI services landscape. Let me break down my thoughts:

Claude Code’s Impact on Development

Claude Code is powerful, but it’s not necessarily “the end of the middleman.” It’s more accurate to think of it as changing what kind of middleman provides value:

• What it enables: Companies can now build simple internal tools, prototypes, and straightforward applications faster without extensive dev teams
• What it doesn’t replace: Complex architecture decisions, system integration, security hardening, compliance requirements, scalability planning, ongoing maintenance, and domain expertise

Will Companies Build vs. Hire?

It depends on complexity:

• More in-house for simple tasks: Yes, companies will likely handle more basic automation and tooling internally
• Still outsource complex work: Enterprise-grade applications, systems requiring specialized knowledge, and mission-critical software will still need professional development teams
• Hybrid approach: Companies might prototype internally with Claude Code, then hire specialists to productionize

Is Offering AI Services Built on Claude Worth It?

Absolutely – but you need to provide clear value beyond raw API access:

High-value services:

• Domain-specific solutions (legal tech, medical, finance) where you understand the industry deeply
• Integration with existing enterprise systems
• Custom workflows and automation that require business process expertise
• Managed services with SLAs, security, and compliance
• Training and change management
• Ongoing optimization and monitoring

Less defensible:

• Generic chatbot implementations
• Simple wrapper applications without unique value
• Services easily replicated by clients themselves

The Bottom Line

The market is shifting from “build basic AI functionality” toward “apply AI strategically within complex business contexts.” If your services offer genuine expertise, integration capabilities, or solve specific industry problems, there’s absolutely a market. The commoditization of basic AI capabilities actually increases demand for specialists who can deploy it effectively.

Prompt

Is AI code the end of the middle man? Will companies build their own apps instead of hiring 3rd parties? Is it worth it to offer ai services based on AI services?

Answer

Is AI Code the End of the Middleman?

Not entirely, but it’s definitely shaking up the role of third-party developers and agencies—the “middlemen” in software outsourcing. AI code generation tools like GitHub Copilot, Cursor, or JetBrains’ AI assistants are automating routine tasks such as code snippets, refactoring, bug detection, and even testing, which traditionally padded outsourcing bills. This has led to productivity gains of 10-15% for teams using AI across the full software lifecycle, not just coding. In fact, vendors in AI software outsourcing are now leaning on these tools themselves to deliver faster and cheaper, creating a “self-reinforcing loop” where AI builds AI, cutting costs and errors for clients.

However, the middleman isn’t vanishing. A 2025 METR study found that early AI tools actually slowed experienced open-source developers by 19% on real tasks, as they grappled with integration and debugging AI outputs. AI excels at grunt work but falters on complex architecture, security vulnerabilities (which it can even introduce), or domain-specific needs. Outsourcing firms are adapting by specializing in AI integration, custom LLMs, and RAG systems, rather than raw coding. The job market reflects this: AI is hitting mid-level roles hardest, but demand for senior engineers who oversee AI (and fix its messes) is rising, with projections that AI could handle up to 40% of coding by late 2025—but 80% of programming still needs human oversight. In short, AI commoditizes the low-end middleman, but elevates the need for high-end expertise. Recent chatter on X echoes this nostalgia for “real” devs who code end-to-end, without AI crutches rewiring them into dependents.

Will Companies Build Their Own Apps Instead of Hiring Third Parties?

Increasingly, yes—for simpler apps and prototypes—but not across the board. AI democratizes app-building, letting non-technical teams (or small in-house devs) spin up MVPs with no-code/low-code platforms like Bubble or Adalo enhanced by generative AI. Tools like CloudApper’s AI-DevAgent or agentic systems (e.g., RepoAgent) automate full-stack tasks, potentially making devs 25x more efficient and slashing costs. Tech leaders like Meta’s Zuckerberg and Salesforce’s Benioff predict AI handling mid-level engineering by 2025, with Salesforce pausing hires after a 30% productivity spike. Smaller companies, especially, are ditching outsourcing for AI-driven internal builds to own their IP and iterate faster—think custom chatbots or recommendation engines yielding 15% cart size boosts in retail.

That said, for enterprise-scale, secure, or innovative apps, third parties remain essential. AI hallucinates bugs, ignores edge cases, and lacks strategic vision—humans are needed for the 80% of work involving ethics, compliance, and novel problem-solving. Startups might outsource complex integrations (e.g., HIPAA-compliant AI for healthcare), and even AI adopters like AstraZeneca partnered with firms for custom builds. The shift? More in-house for routine apps (reducing outsourcing by 20-30% in simple use cases), but a boom in specialized agencies for AI-augmented, high-stakes projects. By 2040, machines might write most code, but companies will still hire (or outsource) to direct it.

Is It Worth It to Offer AI Services Based on AI Services?

Absolutely—it’s a goldmine with low barriers and explosive growth. Reselling or building atop AI tools (e.g., white-labeling CustomGPT.ai chatbots, AI sales agents, or consulting on LLM integrations) taps into a market projected to balloon from $11B in 2025 to $91B by 2035, at 26% CAGR. Why? Demand is sky-high: 78% of sales teams using AI report revenue growth vs. 66% without, with 13-15% lifts and 10-20% ROI bumps. Small businesses are prime targets—AI resellers help them automate workflows, personalize marketing, or predict maintenance, yielding quick wins like 12% retention gains or $5K in downtime savings.

The model is scalable: No-code reselling means low overhead, recurring revenue from subscriptions, and easy upsells (e.g., from basic chatbots to full AI copilots). Top services to offer? S-tier: AI product recommendations, sales automation, and pricing optimization, which deliver 2-3% margin boosts and sell themselves via proven ROI. McKinsey’s 2025 survey shows AI driving EBIT in software engineering and sales, with 60% of execs citing ROI/efficiency gains. Risks? Saturation in generic tools, so differentiate with vertical expertise (e.g., AI for retail or finance). For solopreneurs or agencies, it’s worth it—start small, prove value, and ride the wave as 73% of execs plan AI business model overhauls.

Prompt

Is Google AI Studio the end of the middle man? Will companies build their own apps instead of hiring 3rd parties? Is it worth it to offer ai services based on Google AI services?

Response

Google AI Studio is not the end of the “middle man,” but it is arguably the end of the “lazy” middle man.

To give you a direct answer: Yes, it is absolutely still worth offering services based on Google’s AI stack, but the way you make money from it has shifted. The days of simply wrapping a prompt in a nice UI and selling it are disappearing.

Here is a breakdown of why this shift is happening and where the actual opportunity lies for you.

1. Is Google AI Studio the end of the middle man?

No, but it forces the middle man to “level up.”

Google AI Studio (and similar tools like OpenAI’s Playground) democratizes the prototyping phase. A marketing manager can now log in, write a prompt, and see if Gemini can write their blog posts. They don’t need you to test if the AI is capable.

However, Google AI Studio is largely a developer tool for prototyping, not a production environment.

• The “Middle Man” Gap: A business owner can generate a cool recipe app idea in AI Studio, but they cannot deploy it to the App Store, handle user authentication, manage secure database connections, or ensure the API costs don’t bankrupt them from a DDoS attack using just AI Studio.
• The New Role: You stop being the “gatekeeper of access” and become the “architect of reliability.” The middle man is now needed to turn a buggy, hallucinating prototype into a stable, secure business tool.

2. Will companies build their own apps instead of hiring 3rd parties?

They will build their own prototypes, but they will still hire for production.

We are seeing a trend called “Shadow AI”—similar to Shadow IT. Employees are building their own little tools and workflows using no-code/low-code solutions.

• The Trend: Small to mid-sized companies will likely build internal tools (e.g., a document summarizer for HR) themselves because tools like Google AI Studio make it free and easy to test.
• The Limitation: Once that internal tool becomes mission-critical, they hit a wall. They run into data privacy issues, context window limits, or integration needs (e.g., “How do I get this AI to automatically read my Salesforce data?”).
• Your Opportunity: The opportunity is no longer “I will build you an app from scratch.” It is “I see you built a prototype that sort of works; I will come in and make it enterprise-grade, secure, and integrated with your actual data.”

3. Is it worth it to offer AI services based on Google AI services?

Yes, specifically because of the “Gemini Ecosystem.”

Building on Google is currently a high-value play for three specific reasons that separate it from just “reselling ChatGPT”:

• The Context Window Advantage: Google’s Gemini 1.5 Pro has a massive context window (up to 2 million tokens). This allows you to build services that other models literally cannot support, such as uploading 10 years of financial PDFs and asking questions across all of them at once. “Middle men” who build specific document-analysis tools for lawyers/accountants using this feature are making a fortune right now.
• Integration with Workspace: If you offer B2B services, Google is king. Most businesses use Google Docs/Drive. Building apps that use Google’s ecosystem (Vertex AI) to seamlessly pull data from a client’s Google Drive is a massive value add that requires technical “middle man” expertise to set up securely.
• Multimodality: Google is exceptionally strong at native video and audio understanding. You could build a service that analyzes security camera footage or customer support audio logs directly. This requires engineering integration that most companies cannot do in-house.

The Verdict: How to Position Yourself

If you want to survive as a third-party developer/agency:

• Don’t sell: “I will build you an AI chatbot.” (Google AI Studio lets them do this for free).
• Do sell: “I will build a secure workflow that connects your proprietary customer data to Google Gemini to automate your support tickets without leaking data.”

The “middle man” is dead; long live the “integrator.”