Understanding AI Economics for Startups

New AI Sectors and Emerging Opportunities

By Kala K and Peter Adamson

New AI Sectors and Emerging Opportunities

In the last five (5) years, a small group of high value AI Startups have emerged mostly from the US and China. These companies are shaping totally new AI sectors (LLM, Autonomous Vehicles, Generative AI, Search Engine, Intelligent Robots, Chip, Application Deployment, Drug Discovery, etc) while transforming conventional ones and re-shaping entire economies with their line of AI technologies. For startups everywhere, this means we now have a number of highly effective and powerful lines of AI tools which can be applied to build radically innovative solutions and help re-wire some sectors to their next form.

But assessing AI’s value for startups has always been a painful exercise and even confusing to a certain degree. Productivity growth, workforce development, innovation and adaptability are some key aspects where entrepreneurs expect to see positive transformations with their AI investments. Though not all economic benefits of AI are universally applicable to all startups or businesses. The potential economic benefits of AI vary greatly depending on the industry, the specific business model, the type of product or service offered, the problems being addressed, execution and even physical conditions of markets.

Diagram 1 - Industry Distribution of Top 30 AI firms by Value Generation (by OxValue.AI )

Startups need to carefully identify where AI can provide a genuine competitive advantage and a clear return on investment within their specific context through phased experimentation and refinements. In our experience, short controlled pilots are effective in unveiling and validating conditions and environments in which businesses can derive optimal AI values. The process, if designed correctly, can further identify key misconceptions in AI utility, which is useful in making informed pivots to refine business models, product designs, operational approaches and strategies. These corrections will help uncover and address flawed strategies and missed opportunities.

In this blog, we share a summarised list of generic impact areas which can be used as a baseline to execute experiments, examine, evaluate and assess AI economics for startups.

2.0. The Eight Pillars of AI Economics

We identified eight (8) pillars to evaluate AI economics for a startup. These pillars are founded on the concepts of quality and speed in: i) finding and scaling market opportunities; ii) decision making; iii) generating high value IPs; iv) productivity growth; v) workforce development/shaping; and vi) high adaptability. Essentially in our use case, the metrics and goal is all about increasing output for each team member by multiple times with AI over a period of time (e.g. x5 to x10).

Diagram 1 - The Eight Pillars of AI Economics for Startups

2.1. Capital Efficiency and Scalability

AI does have a number of overheads in terms of cost and it should not be underestimated. This includes infrastructures, software, platform, frameworks, talent, data, operation, maintenance, cultural adaptation, change management and regulatory compliance. All of which are significant investments for a startup especially if you are involved in solving real world problems in sectors which require development of new AI based and agentic solutions (like when rewiring a sector).

However, these investments go a long way as it can significantly reduce scaling costs in ways we never imagined before. AI is rapidly changing the traditional startup growth model, where significant headcount increases were necessary for scaling the business in the past. Today's AI native startups can automate parts or whole core functions like sales, marketing, customer support, supply chain and software development, enabling them to achieve growth targets with leaner teams and far less resources. This means the assumption that growth requires heavy investment no longer stands for most sectors.

We have entered a period where it's becoming increasingly viable for startups in some sectors such as software and technology, to reach a $1 billion valuation with significantly smaller teams. Reasons why we see a steady rise of 40 person unicorns (e.g. Midjourney, RunwayML, Stability AI, etc) and increasing number of solo entrepreneurships in the market.

2.2. Competitive Advantage and Moat

The ability to leverage AI is becoming crucial in building competitive advantage, market leadership and business moat. Startups that don't integrate AI into their workflows and processes risk being left behind by those who can move faster and smarter with their superior AI aided business intelligence.

AI supercharges all existing data driven decisions, processes and tools. With AI, we are not just able to analyse large data sets quickly (which was anyway mostly done with just infrastructure and limitless distributed computing power brought by cloud partners), but extract new correlations and semantic meanings from datasets beyond relational tables. AI technologies such as Natural Language Processing (NLP), Computer Vision and multimodal systems are just some examples of such solutions that help us extract valuable information from various types of unstructured data. This increases our ability to observe, listen, process, learn and iterate faster while launching suitable action plans to capitalise on market opportunities, foresee risk and experiment new services safely.

2.3. Venture Capital and Funding

With a leaner setup, high level of automation and lesser overhead to manage, the startup investment requirements are shifting drastically. AI native startups are able to bootstrap longer and achieve substantial business traction with smaller founding teams before seeking any external funding. The reduced reliance on external funding puts startups in a position to demand better terms from their venture capitalists, potentially shifting the power dynamic in investor-founder relationships.

Similarly investors are increasingly seeking startups with the ability to maximize performance with a lean team through a well positioned and executed AI strategy, that not only automates but is able to form new pieces of intelligence from data assets, that can be monetised and productised. This is seen as a key indicator of viability and driver of growth with a lower burn rate.

Not surprisingly this led investors to move a substantial portion of global venture funding, mentorship and resources towards AI related companies in 2024, marking it as the leading sector for investment.

2.4. Cost Structure

AI development costs can vary depending on solution complexity, dev and technology strategies (e.g. cloud, hybrid, etc). For instance, a basic AI solution such as chatbots might cost between $15,000 to $70,000. Whereas an advanced industry based AI solution can potentially exceed $500,000. Factors influencing costs include development approach (e.g. in-house vs. outsourced), required resources, and the type of AI application.

Investment in AI hardware (GPUs, servers, storage) and software licenses alone can range from $50,000 to $150,000+ annually, depending on the scale. Cloud service subscriptions are also a significant ongoing cost contributor. Hiring experienced AI professionals, data scientists, and software developers requires competitive salaries, potentially ranging from $150,000 to $400,000.

While unavoidable, startups can still optimize costs by leveraging open source AI tools, cloud, using synthetic data for model training, leveraging pretrained models, strategic partnering, and adopting a phased investment approach with an MVP and effective feedback loop.

2.5. Continuous Improvement of Business Metrics

Many think AI is primarily about cutting costs through automation and replacing human jobs. This is true to a certain extent, but the freed resources now are shifted from routine simple tasks to areas where new values and radical innovations are created. While cost reduction can be a benefit, the true power of AI for startups often lies in creating new value propositions, enhancing existing products and services, improving customer experiences, nurturing workforce productivity, cultivating diverse business ecosystems, enabling personalization, and gaining deeper simulations from data to drive better decisions and policies.

Correctly put, AI is not replacing human jobs but transforming and modernising it. More employees can now focus on improving key metrics such as efficiency, quality, customer satisfaction, identifying profitable partnerships, innovation, digital transformation, workforce development, sustainability, marketing and revenue diversification.

2.6. Revenue Generation

AI native startups generate revenue through various models, through sale of AI powered software, hardware and services. Examples include AI driven predictions, content generation tools, recommendations, diagnostic tools, autonomous systems and other digital services.

Some startups offer AI based services via subscription or charging based on usage can create steady recurring revenue streams and align costs with customer activity. There are also AI native startups which leverage their unique datasets to generate revenue by selling proprietary data, insights, or analytics. For custom AI solutions, revenue can be structured through project based fees or long term contracts with ongoing maintenance and support payments. Adopting a mix of these revenue strategies is common and is highly feasible for AI native startups today.

2.7. Speed to adopt and value New Technologies

AI lowers barriers for new tech adoption by simplifying implementation through no code/low code AI platforms (e.g., Zapier) which allow startups to integrate technologies (e.g., chatbots, analytics, automation) without deep technical expertise. For instance, an industry SaaS startup can use AI powered analytics to process business data and derive insights without hiring a data science team. AI can automate tasks like testing, optimization and deployment. For instance, AI powered CI/CD pipelines can accelerate software updates, enabling faster iteration and feature releases.

Cloud based AI services (e.g., AWS SageMaker, Google Vertex AI) and open source frameworks (TensorFlow, PyTorch) reduce infrastructure costs, making advanced tools accessible to startups with limited budgets. Similarly, AI seamlessly integrates with other emerging tech such as IoT, blockchain and quantum computing, creating hybrid solutions for a variety of industry problems. For example, a logistics startup might combine AI, Autonomous Vehicles with IoT sensors to optimize supply chains in real time.

2.8. Faster Service and Product Iteration

AI catalyses product iteration in startups by accelerating data analysis, feedback cycles, enhancing collaboration, automating testing, streamlining development processes and enabling data informed pivots when needed. This allows startups to release new features and improvements to market more rapidly, staying highly competitive in fast paced industries. This also allows startups to drop initiatives that are deemed less relevant to markets.

For example, many startups use AI tools like Typeform (AI powered survey analysis) or Hotjar and Smartlook (behaviour analytics) to iterate faster.However, success depends on strategic integration, balancing automation with human creativity, and addressing ethical and technical challenges diligently. Startups that leverage AI effectively can outpace competitors by iterating on average 2 to 3x faster than those relying on traditional methods.

3.0. Two Possibilities Ahead

There are two varied outcome possibilities for AI native startups. The first path leads to a state where the business successfully reaps AI values continuously in every stage, racking more competitive edge as the business scales and grows. The second possibility is to generate moderate to no value at all, where cost per dollar revenue may remain the same or even increase and business is less competitive among peers. Neither is a dead end (though everyone hopes to be in the first class) - each path is a great signal to businesses for forming the next best action.

One thing to note is that, "AI is not a magic money making machine that instantly generates huge ROI.". Simply adopting AI will not lead to massive profits with little effort or upfront investments. Like in every other business setting, understanding the powers and limitations of the tools or technologies employed in achieving goals is critical. Mapping how such new technologies will affect organisations physically, environmentally and culturally in delivering services to customers and partners is key to success.

Smaller and phased experimentations with MVPs can help identify key advantages, scalability of solutions and changes required. In many cases, these on site experiments are far better bets for forecasting ROIs and gains as opposed to generic studies. AI is a double edged sword, requiring significant investment in development, infrastructure (cloud computing, specialized hardware), data acquisition, data cleaning, and skilled talent. ROI takes time to materialize and depends heavily on the specifics of application, integration into the business model and effective execution for the long haul.

Conclusion - Shift from ‘Build’ to ‘Adopt’

The economics of AI for startups are characterised by the potential for i) rapid and capital efficient scaling, ii) evolving funding landscapes that favour AI native approaches; iii) significant but potentially optimisable development and infrastructure costs; iv) ability to manage diverse revenue generation models powered by AI capabilities; and v) high productivity growth. Startups that strategically leverage AI to automate critical processes, build innovative products, and understand market trends faster through data analysis are well positioned to reap values of this evolving AI fuelled economic landscape.

By automating tasks, improving customer experiences, and supercharging innovation, AI becomes a force multiplier for startups. The key is to align AI initiatives with specific business goals (e.g. faster product iteration, scaling operations) and start with measurable pilots. When justified strategically, AI’s cost is an investment in sustainable growth and competitive edge, not necessarily a cost cutting bucket. To quantify AI’s impact, track KPIs like productivity growth, cost per acquisition, customer retention rate, and operational efficiency gains which usually secures buy-in from stakeholders.

Lastly, it’s absolutely fine to shift from "Build It" to "Adopt It". Make use of the increasingly composable architecture and modular AI tools provided by various cloud and AI vendors where possible (e.g. pretrained models, APIs ). Prioritise speed to market and you can always make a segway to develop your own technologies in later stages when there is more experience with what the market is comfortable with. For instance, in some markets you may need to balance innovation with compliance - where AI adoption might be constrained by evolving regulations (e.g. AI Act in the EU, GDPR) and ethical concerns (e.g. bias, transparency). In such markets, test business models and learn constraints before allocating investments for further IP and innovation generation.