Is vibe coding really new?

While design technology has long been moving toward being more user-friendly, vibe coding marks a leap in sophistication and accessibility for non-technical users. With coding done through large language models, founders can potentially skip wireframes and mockups entirely, moving directly into functional prototypes that customers can use and provide feedback on.

This agentic capability enables what Bhatti called concurrent building and selling. “You don’t wait,” he said. “You can build and sell at the same time.” The autonomous nature of agentic AI systems handles complex development tasks independently while founders focus on customer engagement.

The term itself is new—“vibe coding” is credited to computer scientist Andrej Karpathy in early 2025 to describe how AI users were developing code.

Vibe coding tools work differently depending on what you’re trying to accomplish. Some platforms help experienced engineers code prototypes faster. Replit describes its approach differently—it enables non-technical founders and other “knowledge workers” to build complete applications, handle deployment and manage databases without the need for a developer.

The financial impact of vibe coding on startups

Vibe coding’s biggest impact may be on costs. Bhatti shared an example of using agentic AI in place of a development agency.

"One of our solopreneurs got a quote from a dev agency saying it would take upwards of half a million dollars to do something," Bhatti said.“He just wanted to see if this was even worth his while, and he was able to do it for a couple hundred, if not $1,000, just to test and learn from there.”

Financial advantages of vibe coding

• More chances to experiment: “What this does is it gives you more shots on goal,” Bhatti said. “As opposed to just three shots, you have 33.” Agentic AI amplifies this by working autonomously on multiple iterations without requiring constant human supervision.
• Different cost structure: Instead of hiring full-time developers, founders pay for specific projects and results rather than salaries and time.
• Longer runway: Lower development costs mean funding lasts longer—critical when racing to find product-market fit before cash runs out.

When to use vibe coding for your startup

Vibe coding excels in specific scenarios but has clear limitations. Understanding these distinctions helps founders make informed decisions about AI-assisted development strategies.

When vibe coding works well

• Early-stage validation: Build functional prototypes for customer testing before significant development investments.
• Specialized applications: Build custom tools for specific market segments when time to market is critical.
• Internal operations: Create custom tools for sales, marketing and operational processes.
• Investor demonstrations: Show functional prototypes instead of conceptual presentations.

This includes building new applications from scratch and connecting existing business systems, automating workflows within current software platforms and generating custom reports from established data sources.

“Vibe coding can get you very far—like 80 to 90% of the way there," Bhatti said. "At some point, you may want to implement or bring in more structured engineering processes as well." Agentic AI systems push this boundary further by handling more complex development tasks autonomously, but human-in-the-loop oversight remains essential for quality control and strategic direction.

The market pressure reality about vibe coding

Vibe coding creates a competitive reality: when development barriers lower for everyone, the advantage shifts from having more time to running more experiments in the same timeframe. As these tools become more accessible, competitive dynamics may shift accordingly:

• Accelerated competition: Lower development barriers enable more players to enter markets quickly.
• Evolving customer expectations: Users may start to expect quicker iterations and feature updates.
• Compressed validation cycles: Some investors may start to expect proof points that previously took years.

Preparing for investor conversations

As vibe coding tools mature, they may reshape investor expectations around validation timelines. If founders can build and test products faster, the logic suggests investors might eventually expect shorter paths to product-market fit demonstration.

Industry observers see this shift potentially accelerating. “I think the expectation now will be of investors to say that it's not just five years—it might be one year,” Bhatti said. “You should be able to know whether this works or not.”

This creates pressure across funding cycles: Many early-stage AI-powered companies are reaching later funding stages sooner, traditional development milestones are accelerating and investors increasingly expect working products and revenue generation earlier in the process.

While these shifts appear most pronounced in AI-focused companies, broader implications for startup funding cycles remain to be seen as the technology matures.

Common due diligence areas

Founders using vibe coding should expect investor questions about technical sustainability and enterprise readiness. While specific concerns vary by investor and industry, common areas of focus include:

• Security and compliance: How applications handle data protection and regulatory requirements
• Scalability planning: Transition strategies as user bases grow beyond prototype capacity
• Quality assurance: Oversight processes for AI-generated code and features
• Technical debt management: Plans for addressing limitations in rapid development approaches

Preparation and clear communication around these areas can help streamline due diligence conversations.

Positioning strategies for founders using agentic AI

• Strategic efficiency: A compelling founder presentation can position vibe coding as a customer discovery accelerator rather than simply a cost-cutting measure. This shows strategic thinking—that you understand technology’s real value lies in faster market feedback loops, not just cheaper development cycles.
• Learning velocity: Founders should highlight their ability to test and refine assumptions quickly, rather than just building features faster. This distinction matters to investors because it shows focus on finding product-market fit through systematic experimentation, not just shipping code efficiently.
• Competitive advantage: Strong pitches explain how vibe coding creates competitive moats—letting you respond to market changes and customize solutions for individual clients at scale. Investors want to see how the technology creates lasting differentiation, not just temporary speed.

Planning for scale beyond vibe coding

Successful vibe coding strategies require planning for transitions and scaling challenges. While AI-assisted development can take startups far, most will eventually need traditional development expertise for security, architecture and enterprise-scale requirements.

Capital considerations for agentic AI development

Vibe coding changes the sequence of development expenses. Traditional development frontloads costs through developer salaries, while AI-assisted development backloads them through usage-based technology costs:

• AI inference costs: The computational expenses for running AI models—these increase as more users interact with your application
• Observability costs: Monitoring tools that track application performance, user behavior and system health as you scale
• Transition planning: Budget for security audits and code optimization when moving to production scale
• Organizational shifts: Expense of training non-technical team members to use development tools and manage new workflows

The concurrent build-and-sell approach enabled by vibe coding and agentic AI may require more capital than many founders anticipate. When commercialization moves earlier in the development cycle, startups need greater working capital to support simultaneous product development and market entry activities.