Building a Generative AI Startup: Tips and Strategies

Planning to Pioneering the Future of AI? Explore this comprehensive guide on building a generative AI startup that can help!

Generative AI is shifting the paradigm of the tech world. The technology has grabbed global attention due to its ability to produce content out of thin air. And, within a year, names like GPT 4, Midjourney, and Bard AI have dominated the market. 

This rapid growth of popular generative AI tools inspired a momentum worldwide. Now more and more startups are interested in building generative AIs to serve individuals and businesses alike.

A report published on Statista provides statistical evidence to support this claim. The report suggests that the Generative AI market size is expected to grow at a CAGR of 24.40% between 2023-2030. 

Additionally, the rising popularity of artificial intelligence’s potential is attracting more and more businesses to adopt them. Thus, it is an excellent opportunity for experts who are planning to build generative AI startups.

But as the new tech world order is dominated by names like Google and Microsoft, it creates a necessity to plan the startup well. This blog helps you with the same. In our take on building a generative AI startup, we are focusing on challenges and tips to overcome them. 

First, some critical insights that might help you understand the massiveness of the generative AI industry better.

Crunching the Data: Why Should You Build a Generative AI Startup? 

Data is the real key to understanding the growing market of generative AI. So, we are listing down some of the top generative AI statistics from reliable sources like McKinsey, Statista, and Salesforce. Let’s have a look!

• Mckinsey believes generative AI could add a $2.6 trillion to $4.4 trillion value to the global economy by multiplying the impact of AI by 15 to 40%.
• 75% of generative AI use cases are expected to fall across areas like customer operations, marketing, and sales, R&D, and software engineering.
• Statista suggests that in 2023, the generative AI market size is projected to reach $44.89 billion.
• Salesforce says 73% of the Indian population surveyed uses generative AI followed by Australia with 49%, the US with 45%, and the UK with 29%.
• 52% of Gen Z entrust generative AI with informed-decision making.

How to Build a Generative AI Startup?

Building generative AI startups is a continuous process that requires a regular flow of efforts. When you have a clear plan in mind and the ability to afford calculated and uncalculated risks, you can kickstart the process. 

Here’s a step-wise guide on generative AI startups that can help!

1. Understand the Generative AI market Well

There is plenty of helpful data to use as a reference if you want to understand- What is generative AI’s future in the global market. Finding the right guide on generative AI’s market position is not very difficult nowadays. 

There are plenty of quantitative big data sources like McKinsey, Salesforce, and Statista that publish reliable generative AI reports and regularly update them. Some questions that these reports can answer are-

• Which markets are rapidly increasing the adoption of generative AI?
• What generative AI features are gaining traction?
• What generative AI models are offering better functionality?
• What are the generative AI target groups that can use it?

2. Pick the Niche You Want to Target

The next step requires you to pick a niche for building generative AI startups. The niche decides what services your generative AI will provide. There are several niches existing in the generative AI market for which artificial intelligence companies in the USA and from other corners of the world are building solutions. For instance, image, audio, video, or text generation. 

Additionally, according to the niche, the size of data required will vary. You will also have to find experts who have a history of contributing to building generative AI startups of the same niche. There are some questions that you should ask yourself while picking the niche-

• What content niches fall under your expertise?
• What are some user groups you are planning to target?
• What industries can be served using your AI?
• What are some leading names in your niche?
• What niches can be used to target larger user groups?

3. Find the Right Tech and Design Experts to Hire

Now you have the responsibility of finding the right design and tech experts who can design and build applications of generative AI. It is critical that your early-stage staff has extensive experience in building such solutions. 

For instance, predictive AI and generative AI go hand-in-hand. Predictive AI finds and predicts patterns while generative AI generates results. So you will have to find experts that can cater to both tech requirements.

Experts with diversified experience can help you fulfill client requirements with high-quality standards. This can play a critical role in the earlier stage to leave a good impression on client’s minds. 

Some factors that should affect your choice of experts are-

• Educational background in design and tech
• Past projects developed or designed
• Familiarity with technologies and tools
• The engagement that experts brought on past projects
• Understanding of target markets

4. Build a Minimum Viable Product (MVP)

The best way to know your market well is to build applications of generative AI yourself and deploy them into the market. Building an MVP is quite cheap in the modern market thanks to the existence of alternatives like low-code development tools and several open-source frameworks. 

By building an MVP generative AI, you get to test the waters. Your MVP will help you measure the responsiveness of target users. An MVP will help you figure out facts like-

• What are some user groups that engage with technologies?
• How many users are actually interested in your niche?
• What are some revenue opportunities for a generative AI in your niche?
• What more features can be integrated into your final product?
• What are some gaps that can be further explored by deploying a final product?

5. Invest in Marketing

A good marketing strategy is the key to success for any organization. A generative AI marketing strategy will require you to build a website, and social media presence, and invest in advertisements. 

Finalize a logo and a brand name that represents your organization. Use it in your advertisements that you will show on targeted platforms. Several IT companies prefer onboarding social media apps like LinkedIn, Instagram, TikTok, Facebook, and more to promote their artificial intelligence companies.

Additionally, hiring digital marketing companies and affiliate marketing experts is also a great way to boost your startup’s visibility. 

6. Explore Ethical Obligations and Regulations

Every business is supposed to work under regulatory obligations set by the respective governments of their regions. That is why it becomes a necessity to explore every single regulation in the market. 

Find legal experts, if required to create a work infrastructure that meets all legal requirements. Legalities are also involved in creating partnerships with clients. So, having an expert helping you with legalities can be beneficial in making legally binding development contracts.

Potential Challenges Generative AI Technology Startups Face

The generative AI technology market is still at a nascent stage. So, there are several challenges that remain unexplored even until now. Let’s have a look at a few of them

• Enough training data

The major challenge faced in building generative AI applications is the lack of enough training data. The size of the data decides how accurate or high-quality results will be generated by the trained generative AI. 

Take Open AI’s GPT 4 as an example. 570 GB of training data was used to train GPT 4. This data was extracted from sources like web texts, articles, Wikipedia, books, and more. 

• Affordability

Building generative AI solutions is a costly affair. That makes it necessary to have enough budget to build, market, and deploy these tools. A major challenge that startups might face is not having enough budget to sustain long enough to attract good ROIs. 

• Tech Giants

Another critical challenge that startups face is competing with tech giants like Google and Microsoft. Tech giants have greater promotional budgets and established market reputations. Thus, it gets hard for new startups to build generative AI applications that are unique in nature and can thrive independently.

• Ethical obligations

AI requires a massive amount of data which is often extracted from several existing works by real people out there. For instance, AI art generators are trained using massive amounts of artworks by famous artists. This gives AI an unfair advantage over those who work manually and take hours, days, or weeks to prepare a unique artwork. 

• Monetization

Monetizing the generative AI models is a tough task but critical given the fact that managing an AI tool is an expensive endeavor. There can be questions about the ownership of the content used to train AI, exploring the right pricing model for different markets, strategizing free trials, and countering free alternatives by tech giants.

The major hurdle here is the benefits Of Generative AI that tech giants are delivering for free. Take OpenAI’s GPT-3.5 as an example. The tool is accessible for free for everyone making it a tough alternative to counter. 

Popular Generative AI Startups in 2023-24 that can Inspire

• Midjourney

A San Francisco, California-based research lab was founded in 2021. The lab launched one of the most successful applications of generative AI in 2022- Midjourney. The platform uses the capabilities of generative AI to produce high-quality graphics based on AI prompts given by users. 

Its abilities made the tool a viral name and became an inspiration for building generative AI startups around the world. The startup witnessed significant growth in its revenue as well. As Dealroom.co states, the Midjourney revenue grew by 1432% between 2022-2023.

• OpenAI

The AI research and development company was launched in December 2015. Its headquarters are in San Francisco, California and since its inception, it has attracted a massive revenue stream. 

Between 2022 and 2023 itself, the revenue generated by OpenAI has multiplied by 1900% making it one of the most successful examples to build a generative AI startup. Open AI has leveraged the benefits Of generative AI in several ways. 

The OpenAI API supports several popular AI models out there including GPT-3 & GPT 4, DALL.E, Whisper, etc.

• Character.ai

Another successful startup that can inspire you to build high-quality generative AI solutions is Character.ai. It is an example of one of the most creative AI chatbots out there. The chatbot lets you create characters and communicate with them. 

The generative AI was developed by ex-developers of Google’s LaMDA Noam Shazeer, and Daniel De Freitas. Its beta model was launched in September 2022 and within such a short span of time, it is showing a 720% rise in its revenue compared to 2022.

Wrapping up

As you launch and navigate through the ethereal ecosystem of generative AI, you will find there are giants with a stronghold on the industry. But it is also true that startups like OpenAI and Midjourney have come up with ideas that significantly caused a threat to these strongholds and countered success that was dominated once by tech giants like Google and Microsoft.

Now as the world is still exploring the benefits of generative AI, there are opportunities to tackle and gaps to fill if you have the perfect idea, the right amount of resources, and the capability to take risks. 

Lastly, we hope that our analysis of the journey of building generative AI startups will be an informative addition to your journey of building your organization.