AI Business Profitability: A Guide for Startups

Many startup founders, investors, and marketing leaders are wondering how to actually make money with artificial intelligence. The question of AI business profitability is on everyone’s mind, but finding clear answers can be tricky. It’s understandable to feel like you should have all the answers, especially as a founder.

However, AI is so new that figuring out how to make it profitable isn’t straightforward. Let’s explore this.

Table Of Contents:

• The Current State of AI Business Profitability
• Where AI Could Bring the Biggest Impact
  • Key Areas for AI Business Profitability Inside Companies
  • Automation’s Increasing Role
  • Accelerating The Workforce Adaptation
• A Deep Dive on Why Making Money With AI Isn’t Simple
  • Biggest Drivers For AI Model Building Companies
  • OpenAI Example
  • Who’s Seeing Profit Growth Then?
  • Obstacles To Profit and Emerging Options
  • Building For A Future Of Profitable AI: Key Considerations
• Conclusion

The Current State of AI Business Profitability

Many companies are pouring money into AI. However, they are struggling to see a direct return on that investment, at least in the near term. This doesn’t mean AI is a bad investment.

A Bank of America report suggests that large companies adopting AI might boost profit margins by 2% in five years. This equates to about $55 billion in yearly savings. However, that impact varies a great deal between industries and companies.

Where AI Could Bring the Biggest Impact

The impact of AI varies among industries. Some will gain far more value than others, and it’s often not in the way you might think. It is expected for generative AI to have financial impacts across many industries.

Generative AI will probably have the biggest financial impact on banking, high tech, and life sciences. For example, in banking, it could result in up to $340 billion annually in value if implemented broadly. McKinsey estimates suggest that, overall, generative AI can produce up to $4.4 trillion in global corporate profits yearly.

AI won’t only help specialized technology companies. Industries like retail and consumer packaged goods can find huge gains with AI solutions.

Companies could see $400-$660 billion added in yearly operating profits through AI. This McKinsey estimate demonstrates just how impactful AI can become for business owners.

Key Areas for AI Business Profitability Inside Companies

Most of the value from generative AI will be within four areas of business. These include customer operations, marketing and sales, software engineering, and R&D.

Let’s take a closer look at the impact on each:

• In customer service, AI acts like a “virtual expert.” AI gives answers and ideas to questions that customers have in real time.
• In drug discovery, AI could act as a helper, proposing potential molecules. AI models can do so while teams develop new medicine.
• For engineers writing computer code, AI can tidy up messy coding. AI can even suggest what coding should be added to help streamline operations.

Generative AI boosts workplace productivity gains. So long as employees adapt to working with this new technology, productivity is expected to improve across a variety of job functions.

Automation’s Increasing Role

New AI technology add to overall productivity improvements. Current AI has the ability to automate a large share of job activities.

Things like writing emails or responding to questions from clients are great examples. AI technology today, such as AI business chatbots, can automate parts of these jobs. Previously only humans could do it.

Prior models predicted AI might automate actions taking about half of workers’ time. However, now the estimation is much higher at about 60% to 70% of employee’s working time.

It used to seem very distant.

It’s largely because of generative AI’s capacity with language. Language is a basic requirement for jobs and AI now does many of those functions well.

Accelerating The Workforce Adaptation

It’s possible work could transform faster now. The capacity of the technology is growing faster than people thought it could. Models created several years ago are already updated.

One analysis estimates half of today’s jobs could be automated between 2030 and 2060. The central estimate is around the year 2045, earlier than once thought. An individual company may change how they use generative artificial intelligence faster.

Competitive pressures force businesses to advance fast to stay competitive.

A Deep Dive on Why Making Money With AI Isn’t Simple

The truth is that many large firms developing their AI models aren’t turning a profit at all. However, some startups are faring better than the larger, more recognizable firms.

OpenAI, a famous name with ChatGPT, hasn’t consistently been making profits. The New York Times said OpenAI expects to create $3.7 billion in revenue. But in doing this, their spending reached $5 billion for that.

So their loss was $1.3 billion for the period. Another company, Anthropic, has similar pressures. It’s spending near $2.7 billion a year.

What drives these costs for these popular businesses?

Biggest Drivers For AI Model Building Companies

Companies like OpenAI or Anthropic need computer power and human knowledge.

They must: Build or rent the “brains” and infrastructure. Servers and advanced chips to manage computing needs. Staff costs, including salaries for experts to push the AI industry ahead.

Companies are offering free use to drive adoption to gain brand traction. Companies aim to become the name people trust for these systems.

Very few companies can afford to make chips themselves and staff hundreds of employees with expertise. Thus, almost all of them rely on buying or renting “pieces”. They require the pieces for their AI parts to operate successfully.

This cost to them matters to figuring out how AI becomes profitable for organizations, even OpenAI. A strong dependency on renting these pieces makes up their cost.

OpenAI Example

To keep ChatGPT running and to improve it needs servers, and lots of them. Reports show these operational cost allocations.

About $4 billion helps maintain servers, keeping ChatGPT available and responsive. $3 billion is used to update their AI, giving AI “smarter” AI capabilities. This is the human-training part of the system.

$1.5 billion goes to salaries and operational costs. Even when considering these high expenses, they may only bring in around $3.4 billion for all of the trouble.

Who’s Seeing Profit Growth Then?

You’re probably wondering then where this money spent is ultimately ending up. Who are the true companies “making money?” It isn’t necessarily with firms making applications for daily users.

Rather, “behind-the-scenes” businesses provide foundations. These are chip manufacturers, data centers, and cloud computing firms.

Almost any firm dealing with “generative” needs tools for large language models. Every “generative AI” participant buys/rents capacity on powerful chips, and that’s where NVIDIA has taken leadership.

It leads with close to 80% share of the market on one crucial computer part (GPUs) according to many expert reports.

The launch of ChatGPT began “fever” to gain and compete. With increased needs for computer brains the part maker’s sales increased by 500% over two years period according to recent analysis. It translated directly to billions of dollars in AI chips.

Likewise, major “cloud services” suppliers seen rising computer resource usage from consumers. Their purchasers of AI models reported an over 17% gain in their service sales. The cloud business referenced buyers adding generative applications helped boost these services’ demand, contributing to increased demand overall.

Obstacles To Profit and Emerging Options

If your company wants to begin offering a service AI powered, it must first develop or access resources. For every firm starting up this presents significant startup funds to build infrastructure. Many of applications for generative applications cannot forecast if revenues will cover initial costs.

Some leaders expect losses now knowing profitability isn’t expected in 1-5 years. Some competitors do give hope. But some do create obstacles, lowering price standards.

Companies creating popular AI tools today see intense competition against established free model availability like Llama (by Meta). Meta presents quality model free of charge. Some might shift buying if another product seems on level, dropping profitability expectations within next 5 year timeline for existing generative AI service providers by reducing costs. Others will look to AI business plan generators to understand how they can make better plans.

Building For A Future Of Profitable AI: Key Considerations

Here are some key strategies to help you on the road to AI Profitability:

Conclusion

As more and more founders want to get involved in the business opportunity for artificial intelligence, many changes happen. Although it is still in the earlier growth phase, and artificial intelligence business profitability is still unknown, its presence will be felt. It is worth looking at AI profitability carefully when beginning any planning.

When building new business ideas and tools, it is important to remember the uncertainties in returns. Consider whether your organization will be using AI agents or even virtual assistants to get things done. Business leaders should carefully consider this topic.