It’s starting to feel like every product has an ‘AI-powered’ badge slapped on it. However, the SEC put its foot down earlier this year, charging US$400,000 for the false claims made by two companies.
“AI washing” is not only misleading, but it also undermines the perception of AI-first products and leads to disappointment among customers and investors.
Understanding the difference between AI-enabled and AI-native solutions helps clarify competitive edge, scalability, and market positioning. While AI-enabled solutions focus on enhancing existing products and may appeal to a broader customer base with a more familiar offering, it’s essential to understand the constraints to scale regarding incompatible data sources and legacy limitations.
Let’s decode the jargon, find out how to spot AI that delivers, and ensure you get what it says on the label.
AI-enabled solutions
Beginning as conventional technologies, AI-enabled solutions are those that later integrate AI to boost performance.
For example, HubSpot integrates AI to automate tasks like email scheduling and lead score predictions, enhancing its CRM functions. While Netflix uses AI to personalise show and movie recommendations, transitioning from a standard digital platform to one that leverages AI to analyse viewing habits for better suggestions.
What these companies have in common is, although not AI-native, they are digital-native. Both companies have accumulated vast amounts of user data over decades, fueling their AI engines. Netflix has viewing history, ratings, and metadata, while HubSpot has customer interactions, marketing data, and sales information. They have also invested heavily in AI talent.
When looking for AI-enabled companies to invest in, it’s crucial to ensure they have clear goals for their AI initiatives and are prepared to keep developing. Netflix invests 10 per cent of its revenue into its technology and development budget.
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What specific problem has your prospect investment identified that AI can solve? Are they continuously investing in their AI journey, or do they see it as a one-time project? AI initiatives must constantly evolve and adapt with their user base, so you must ensure your AI-enabled ventures have an agile culture to allow for rapid iterations.
AI-native solutions
Since AI-native solutions are built from the ground up using AI, they inherently offer more sophisticated capabilities. This means they have the elasticity to scale, deliver high performance with minimal resource consumption, and are designed for continuous AI advancement — they are positioned at the forefront to reap the benefits of rapidly evolving technology.
However, since these market disruptors often pioneer new fields, redefining industry standards, they come with a price tag and notable uncertainty.
Look at OpenAI’s GPT models. Its products are fundamentally AI, constantly advancing their ability to understand and generate text. Altogether, VCs have put in just over US$300 million at a valuation of US$27 billion – US$29 billion.
Similarly, Waymo is designed to utiliSe AI for navigating and making decisions, functioning as a fully integrated AI system rather than just a car with AI features. The autonomous ride-hailing service raised US$2.5 billion in its second round of funding.
Some of the smaller players looking to compete in the market often use third-party technology, like OpenAI, to address a specific lucrative use case. Labeled thin wrapper startups, these AI founders take existing technology and add their own unique value proposition — like Salesforce did with Oracle database.
The important part is to ensure your prospective startups keep listening to their audience, iterating their products, and confirming they solve a painful enough problem so that, over time, they can become thick wrappers with strong defensibility instead.
Wrapping up
In essence, most startups can’t compete with ChatGPT. Ninety percent of AI startups fail, most commonly due to a lack of market awareness, funding, or expertise. Jasper AI is an example of this, as its revenue and valuation crumpled after the source, OpenAI, released ChatGPT, a model that did precisely the same thing.
Also Read: One-third of Singaporeans never used AI tools in their workplaces: Survey finds
You must check whether your prospective AI-native startups solve a big enough problem, but more importantly, ask yourself: Do you believe in them? If you do, enquire about their business model. Is a focused strategy in place to meet achievable objectives? Have they got the right expertise? And what evidence do they have to show they can pivot if needed?
The fundamental nature of the space right now is that everyone is excited by AI, but we’re just coming to the tail of last year’s AI explosion, where many AI-enabled projects or AI-native startups that don’t have a strong enough use case or market won’t survive.
Only those that grow and meet revenue targets will retain their spot in the field. Startup ‘down rounds’ are often some of the first triggers that reduce investor confidence, and the loss of competitiveness or ability to meet growth targets is likely to impact employee and founder morale.
Choosing what companies to invest in requires careful consideration. But the results can be highly lucrative. Do you play safe and invest in renowned companies? Or is their market maturing? And what is their track record with implementing emerging technologies? Sometimes, after all your analysis, it’s about taking a leap of faith and trusting your gut.
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