This is Hamamoto from TIMEWELL.
AI Is Rewriting the Rules for Startups
Artificial intelligence is advancing at a pace that is fundamentally changing the front lines of business. For startups in particular, it is forcing a radical rethink of development methods and go-to-market strategy.
At TechCrunch Disrupt 2025, held October 27–29, OpenAI's Head of Startups Mark Manara sat down with AI editor Russell Brandham to discuss how AI is dramatically accelerating product development and contributing to company growth across both consumer and enterprise markets.
The conversation zeroed in on OpenAI's dual-track strategy serving both consumer and enterprise customers, and on the concrete steps the company is taking to meet the needs of startups and large enterprises alike. Companies once dismissed as "GPT wrappers" are now creating genuine differentiated value, compressing development cycles from weeks to days, cutting system-wide costs, improving response times, and fundamentally evolving how they operate. This article draws on that conversation to unpack OpenAI's startup support strategy, the realities of technological innovation, and the new business opportunities AI is opening up.
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OpenAI and Startups: B2B Investment and Next-Generation AI Strategy
The TechCrunch Disrupt 2025 session made clear that OpenAI is far more than a chatbot provider — it is investing heavily in B2B solutions and startup support. Mark Manara emphasized that while OpenAI operates large-scale consumer chat products, it is pouring significant resources into B2B development so that companies have the support they need to embed AI into their own products.
OpenAI's global teams work continuously to improve product performance and refine API delivery to meet the diverse demands of startups in the field. In the B2B market, this gives companies a powerful engine for scaling AI-powered businesses. On the consumer side, the same discipline — rapid product improvement and tight feedback loops — is the key to a better user experience.
The session explored how startups use OpenAI's models and APIs to build original products, and how they can develop faster and more cost-effectively on top of that foundation. Companies like Cursor, Perplexity, Bridge, and Harvey are challenging the market on OpenAI's technology, and their success creates what Manara called "momentum across the whole industry."
Today's startups face the imperative to compress traditional development cycles dramatically. As Manara pointed out, companies are increasingly shipping new features in tight sprints measured in days — or even a single day — rather than weeks. Cursor, for example, ships feature improvements in as little as 24 hours, delivering an intuitive experience that keeps users coming back.
This is made possible by building on OpenAI's APIs with models of different sizes, making it easy to deploy the right model for each job. Startups place AI models at the core of their products, incorporate performance and usability feedback aggressively, and apply increasingly scientific approaches to development to raise the bar.
Manara also noted that his startup support team includes former founders, former VCs, and operators from many industries — people who can move the needle not just on technical development, but on market analysis, product marketing, and actual sales strategy. Companies run technical validation against real-world cases to determine which model fits which task, fine-tuning tool call accuracy, API response times, and user interface improvements at every level. OpenAI is at the front line of solving the challenges startups face through technology.
Changing the Development Cycle: Fast, Flexible Innovation Through AI
Behind OpenAI's technical innovation is a relentless pursuit of more capable, versatile model designs — and this is accelerating startup development cycles at an unprecedented pace.
The session examined the specific engineering challenges startups face day-to-day: the accuracy of tool calls in agentic tasks, code generation quality and style requirements, and more. Manara explained that when AI calls external tools to complete a task — and whether it makes the right call at the right moment — has a major impact on the performance of a product. A coding assistant that triggers tool calls repeatedly as the user works, for example, can degrade the experience, which means the model's "personality" and behavioral patterns also need careful tuning.
Developer-facing coding tools now offer not only complex automatic code generation but granular personalization of how the tool behaves — going far beyond productivity gains to dramatically improve team-wide efficiency and enable faster time to market.
The session also surfaced fine-tuning as a critical topic. OpenAI continues to advance its fine-tuning capabilities through supervised fine-tuning and reinforcement learning, drawing out optimal model performance for specific domains and tasks. In complex fields like insurance code selection in healthcare, adding reinforcement fine-tuning on top of a standard model has been shown to dramatically improve accuracy and reliability. This reflects OpenAI's strategy of providing diverse model variants tailored to different purposes, making it possible to choose the optimal tool for each task.
Beyond algorithmic and model improvements, infrastructure optimization is also advancing. The session highlighted how caching technology is being used to improve response times while simultaneously lowering token costs — enabling faster, more economical services for end users and improving business model stability for companies building on the platform.
OpenAI also actively provides technical support to developers. Extracting maximum value from fine-tuning requires specialized expertise and dataset analysis, and OpenAI works closely with partner companies throughout that process to propose the right approach.
New Possibilities for Startups: AI Integration Across Every Business Function
In the final section of the session, Manara explored how OpenAI's technology is impacting startup operations and go-to-market strategy across the board. He drew on examples from sales and marketing, customer support, and even internal finance and accounting — illustrating how AI is transforming traditional business workflows in each.
In sales, AI-powered tools are automating the complex, time-consuming processes of lead research and information gathering, dramatically increasing per-employee productivity. This means even small startups can respond to market changes as quickly as large enterprises, giving them a powerful competitive weapon.
Manara also cited the compression of the product development cycle itself as one of the most significant transformations AI is enabling. Features that once took weeks to implement — or mobile apps that took weeks to launch — can now be completed in a day or a week with the latest AI tools. The result is a positive feedback loop: companies can respond to market shifts and customer demands faster, and products evolve at an accelerating pace.
A concrete example: an email management assistant whose founder Richard Hollingsworth argues that the "perfect email assistant" is not one that is simply knowledgeable, but one that deeply understands the individual user's characteristics and email habits, then makes optimal suggestions. This illustrates how AI within a company can go far beyond automation — contributing to genuinely personalized relationships with users.
The session also highlighted AI use cases in areas not traditionally associated with AI: accounting, finance, and data analysis. Finance teams are increasingly asking AI to analyze complex datasets and generate revenue forecast reports, freeing specialists to focus on higher-level strategic decisions. Automated customer support and chatbot integrations allow companies to respond to customers around the clock, simultaneously improving service quality and reducing operating costs.
The wave of AI integration is spreading across every department of the modern startup, improving efficiency and productivity organization-wide. Even companies once dismissed as "GPT wrappers" are now creating unique value and establishing themselves in their markets — a trend that will accelerate across the industry.
The key insight is this: it is no longer enough to focus on the model's performance in isolation. What matters is how you embed AI into your business processes and optimize it for your specific context. OpenAI's work represents the trajectory of the current wave of innovation — and it will continue to be a major source of new business opportunities for startups for years to come.
Reference: https://www.youtube.com/watch?v=2VroIM26s84