AI-First Startups Are Rewriting the Software Operating Model

The most interesting AI-first startups are not merely adding intelligence to products. They are changing the cadence of engineering, the shape of teams, the definition of QA, the management layer, and the economics of software delivery.

Source: Razorpay FTX 2026 panel, moderated by Shashank Kumar, with Rishi Raj Bohrah of Emergent, Ganesh Gopalan of Gnani.ai, Sidu Ponnappa of realfast.ai, and Srinath Somasundaram of Deeptrace · Video ID DkFLQYKxtYs · Watch on YouTube.

Who Are the Panelists?

Shashank Kumar of Razorpay frames the problem from the perspective of a scaled company trying to keep transforming itself: what can a legacy or at-scale software organization learn from startups that were born AI-first? The panel brings together builders operating at different points of the new stack: Rishi Raj Bohrah at Emergent, Ganesh Gopalan at Gnani.ai, Sidu Ponnappa at realfast.ai, and Srinath Somasundaram at Deeptrace.

Their companies cover on-call agents, voice agents, AI-native services, developer workflows, and enterprise delivery. That spread makes the discussion useful: the common pattern is not one product category, but a new operating model for building and running software.

The Real AI-First Shift Is Operating Cadence

The surface-level definition of an AI-first startup is easy: engineers use Claude Code, Codex, Cursor, Copilot, and internal agents. The deeper definition is more uncomfortable. AI-first startups expect work to move through the company at a different tempo.

Rishi describes the internal posture bluntly: if engineers can be made more effective with the highest-tier coding tools, buy them the tools. In his company’s case, that means giving engineers the “$200 Ultra Max” versions of Claude Code, Codex, Cursor, or whatever lets them move fastest. The logic is not perk culture. It is leverage. A ten-engineer team supporting roughly twenty growth-startup-to-enterprise deployments would have been unrealistic a couple of years ago. With the right tooling and operating discipline, it becomes plausible.

AI-first does not mean “let the model code.” It means reorganizing the work so that humans hold the thread end-to-end while agents multiply execution capacity.

That end-to-end thread is the big change. The traditional software lifecycle fragments responsibility across PRDs, project managers, engineering tickets, QA, reviews, release managers, support, and observability. AI-first teams compress those stages. One person can prototype, validate requirements, write code, inspect the generated output, ship, observe failure, and loop back into fixes far faster than a traditional pod.

Dogfooding Is No Longer Optional

The panel repeatedly returns to dogfooding. If a startup is building AI agents for operations, support, on-call, voice workflows, or software delivery, those agents should be embedded in the startup’s own internal operations. That is how the product improves, and it is also how the team earns the right to move faster.

For agentic products, support itself becomes part of the product loop. Users will hit the last 10% of difficulty: ambiguous prompts, missing context, integration issues, strange failures, and edge cases. Instead of treating those as a human-only support burden, AI-first teams build agents that inspect failures, understand usage patterns, and reduce the unresolved delta over time. The 10% might become 5% over the next six months not because the product magically becomes perfect, but because the support ecosystem becomes agentic too.

That insight matters for product leaders. AI-first UX is not just a chat box. The interface may be chat, voice, or another natural-language surface, but the real product is the surrounding loop: context capture, fall detection, debugging, handoff, feedback, and progressive autonomy.

Voice AI Shows Why Testing Has to Become Agentic

Ganesh’s voice-agent perspective exposes the limits of traditional QA. Voice systems are not simple text systems with audio attached. They carry latency, speech recognition, vocoding, hallucination risk, emotional nuance, and India-specific communication patterns. A user may say “yes” while meaning “no.” Tone and context matter. The system cannot be judged only by transcribed words.

His technical direction is to collapse layers. Fewer layers mean lower latency and fewer places for hallucination to enter. But then the testing problem gets harder. How do you test an AI voice agent when the user can say anything?

The panel’s answer is direct: put an AI agent on the other side. One agent acts as the customer; the production agent responds; evaluation systems judge the interaction. Manually enumerated test cases cannot cover the combinatorial space of natural conversation. Agentic software needs agentic tests.

Services Move From Six-Month Delivery to Same-Day Iteration

Sidu Ponnappa’s realfast.ai lens connects AI-first startups to the future of IT services. Traditional services businesses sell projects, staff teams, and deliver on weekly or multi-month cycles. AI-native services change the delivery promise. Enterprise “five-minute delivery” has limits because regulated systems still need hardening, compliance, productionization, and accountability. But “next-day delivery” becomes a realistic ambition.

The shift changes what a services company owns. The customer may own the business hypothesis: if an operational metric moves from X to Y, the business should improve. The vendor should not pretend to own the full business outcome. But the AI-native services provider can own the output: moving the operational metric itself.

“We don’t own the outcome. We own the output.”

That nuance is important. It avoids fake accountability while making delivery much sharper. The provider must surface the metric, track it, form hypotheses, launch software against those hypotheses, and iterate. Requirements gathering may still involve sales and presales, but from requirements downward, the rhythm starts to resemble a consumer product company more than a traditional IT services firm.

The Playground Replaces the Low-Fidelity Mockup

The old early-stage requirements process relied on documents, wireframes, and low-fidelity design. The new version is a working playground. Instead of asking a customer to interpret a long PRD, a team can vibe-code a prototype and ask: is this what you want? Change this. Try that. Show me the workflow.

For prototypes, the panel is comfortable with high autonomy. Compliance and production hardening are not the goal. Alignment is the goal. The customer’s reaction to a working app is richer than their reaction to a document. The prototype becomes the requirements conversation.

But the panel is careful not to overclaim. For regulated environments and production systems, humans remain accountable. Many stories may run almost autonomously, but someone still has to own review, risk, and quality. The right boundary is not “agents never ship” or “agents always ship.” The right boundary depends on failure mode. Some teams already let agents patch failure points by inspecting logs, knowledge bases, and code. Starting from a blank regulated system is different.

Teams Shrink Toward Founder-Shaped Ownership

Shashank raises the natural implication: if Amazon once popularized the two-pizza team, does AI create a “two-slices team”? The panel’s answer is yes, but with context. Team size depends on project scale, but ownership can shrink dramatically.

The strongest people inside AI-first startups start to look founder-like. They understand the customer pain, define the problem, use tools dynamically, make tradeoffs, ship the solution, and drive adoption. Emergent’s team reportedly includes many former entrepreneurs, but even junior engineers who had started AI projects before joining can own end-to-end pieces. The hiring signal shifts from years of experience to demonstrated agency.

This creates a new management problem. If more individuals can hold larger threads, the old coordination layer looks heavier. Managers can use AI to give more automated feedback, support growth journeys, and keep larger spans of direct reports. But only if the managers themselves can operate in writing, reason clearly, and engage agents without needing humans to translate their intent.

The Management Layer Is the Bottleneck

The panel is less anxious about individual contributors than about second- and third-degree management. ICs can learn tools quickly. First-line managers can adapt if they stay close to the work. The harder question is where to find industry veterans who bring experience without becoming the bottleneck.

Sidu describes the anxiety sharply: outside certain tech-startup cultures, many managers do not communicate in writing, plan in writing, or think in writing. They are used to human mediation. When they engage with agents, they burn out because they cannot state and ask clearly. That becomes a structural problem. AI-first organizations need managers who can turn judgment into explicit instructions, context, evals, constraints, and operating loops.

That makes writing a core management skill again. Not writing as corporate documentation theater, but writing as operational precision. If a manager cannot describe the desired behavior, constraints, edge cases, risks, and success metrics, they cannot effectively manage agents—or the humans using them.

Hiring Moves From LeetCode to Judgment, Tokens, and Ambiguity

The interview loop changes too. The panel jokes about asking candidates how many tokens they used in their last program, but the joke points to a real cost model. Functionality is not the only dimension anymore. Token usage, latency, evaluation quality, retrieval strategy, and inference cost become engineering concerns.

Rishi frames critical thinking as the last bastion of human skill. Tools change weekly, sometimes daily. The durable skill is the ability to take an unscoped problem, figure out what matters for the use case, discover best practices, choose benchmarks, and get the job done. In a previous generation, great engineers knew how to Google, ask the right teammates, and synthesize. The new version is the same pattern with AI tools added to the workflow.

That means interviews should become less scoped, not more. Give candidates ambiguous problems. Watch how they clarify assumptions, choose tools, define evals, manage cost, and decide when the model is wrong. The answer matters less than the shape of their reasoning.

Key Lessons

• AI-first is an operating model, not a feature label. The cadence of work, ownership, QA, support, and management all change.
• Buy leverage aggressively. High-end coding tools are cheap relative to the output of a strong engineer using them well.
• Agentic products need agentic testing. Voice, chat, and open-ended workflows cannot be validated only through fixed test cases.
• Prototypes become requirements. Working playgrounds replace long documents for early alignment.
• Production still needs accountability. Autonomy is easier in playgrounds and failure-point fixes than in regulated, hardened systems.
• The biggest scaling risk is management. Managers who cannot write, specify, evaluate, and reason with agents become bottlenecks.
• Hiring should test ambiguity and judgment. Token cost, eval design, and dynamic tool use are now part of engineering competence.

Why This Matters for Diffie

For Anand and Diffie, this panel is almost a map of the market. AI-first software teams are compressing design, implementation, and iteration. That compression increases the need for browser-level verification. If one engineer can ship more UI surface area faster, the old QA process becomes the bottleneck. The more code AI helps generate, the more important it becomes to know what actually changed in the browser.

Diffie should position itself as infrastructure for the AI-first operating model, not as a conventional testing add-on. The customer pain is not “we need another QA tool.” The pain is “our engineering cadence is accelerating, but our confidence loop is still manual.” That makes the strongest wedge: AI-native teams need agentic browser testing that can behave like the synthetic customer on the other side of the interaction, inspect visual and behavioral regressions, and feed clear evidence back into the PR or release loop.

The playground idea is especially relevant. Diffie can help teams turn prototypes into reliable product conversations: capture browser states, compare flows, detect breakage, and let product and engineering teams argue from evidence rather than screenshots and memory. For UI-heavy teams using Cursor, Claude Code, or Codex, this is the missing eval layer.

The GTM implication is to sell to leaders who already feel the management bottleneck. Engineering heads at AI-first startups, founder-led teams, and product engineering orgs adopting coding agents will understand the problem faster than traditional QA buyers. The pitch should be concrete: keep your new AI-assisted shipping speed, but add a browser confidence loop that scales with it.