In the race to adopt free AI, some companies are missing the fine print. — Reuters
DeepSeek recently knocked ChatGPT off its throne as the top-rated free app in Apple’s App Store, so you know what that means: Business leaders’ DMs lit up with, “Have you tried this yet?”
DeepSeek isn’t just free for public use. DeepSeek’s developers claimed the technology took just two months and cost under US$6mil (RM26.53mil) to develop. This means it would cost companies that want to build apps and tech with DeepSeek a fraction of what it would cost to use ChatGPT or similar. US developers like OpenAI, Google, Meta, and more have all spent billions of dollars training their AIs, so DeepSeek’s low-cost promise of ChatGPT o1 model capabilities does seem too good to pass up.
But not all that glitters is gold.
If the tech is free, you are the product
Here’s what many business leaders are missing: When it comes to enterprise AI adoption, “free” often comes with a price tag that won’t show up on your P&L statement right away. And while I’m all for democratising access to transformative technologies, the current rush to adopt free AI tools without proper evaluation frameworks is setting off alarm bells for those of us who have seen similar patterns play out before.
The elephant in the room: Data privacy
DeepSeek’s privacy policy states that user data – including keystrokes, chat histories, and uploaded files – are stored on servers located in China. For many businesses, this should be an immediate red flag. We’ve seen this movie before with TikTok, and – spoiler alert – it doesn’t end with “and everyone’s sensitive corporate data lived happily ever after.” The real cost here isn’t measured in dollars but in potential risk to intellectual property and corporate security.
This isn’t just about one company or one country. The broader issue is how businesses evaluate and implement AI tools in general. When technology moves this fast, it’s tempting to jump on the latest trend without considering the full implementation costs. Remember, even ChatGPT hasn’t been without security issues – for instance, it was banned in Italy until OpenAI made adjustments to comply with EU privacy regulations. I’ve watched companies rush to adopt free AI tools only to realise months later that they’re spending more on training, integration, and security measures than they would have with a properly vetted, enterprise-grade solution.
Speaking of which: Though DeepSeek claimed development cost them under US$6mil (RM26.53mil), new reports suggest it actually spent as much as US$500mil (RM2.21bil) – and investigations are ongoing. In fact, one US senator recently proposed a law banning and criminalising DeepSeek.
Moving past the price tag
The rush to adopt AI tools and solutions also often means overlooking crucial questions about data governance. It’s reminiscent of the Covid-19 pandemic, when companies had to rapidly shift to a work-from-home model, even if their systems weren’t ready to properly handle it – and companies are still dealing with the fallout.
It’s important to ask questions and think critically. Who owns the outputs of your AI interactions? How is your company’s data being used to train these models? Because these aren’t just compliance questions – they’re fundamental business considerations.
This is exactly why forward-thinking companies succeed where trigger-happy companies fail. Companies with eyes on the future are turning to strategic partners for AI implementation. Take Crayon, for instance. While some companies are racing to adopt the latest free AI tools, it’s demonstrating how proper AI implementation can truly transform businesses – not through quick fixes, but through carefully planned integration. For example, it developed an AI-powered image recognition app that helped a Coca-Cola bottler identify 220,000 spare parts. After a successful proof of concept, now it’s rolling that tech out to all bottling plants.
Proper AI development, strategy, and implementation can cut IT spending, reduce costs derived from tech debt by 40 percent, improve business outcomes, scale value, and more – but it requires thinking beyond the sticker price. It’s about understanding how these tools integrate with your existing systems, what security measures need to be in place, and how to train your team effectively.
Responsible AI adoption
The challenge of building trust in new technologies isn’t unique to AI, which means we can learn from other sectors and apply those lessons. Tara Mind, a mental health benefits platform, demonstrates how even cutting-edge treatments can be successfully introduced to companies through careful vetting, clear protocols, and evidence-based approaches. And just as companies like Tara Mind are pioneering careful protocols around innovative mental health treatments, businesses need similar frameworks for evaluating and implementing new AI tools.
Lasting, successful enterprise AI adoption requires a comprehensive strategy that accounts for hidden costs and long-term implications, including safety and security. This means investing in proper vetting processes, security measures, and training programs. It also means understanding that while the tool might be free, the infrastructure needed to use it safely and effectively often isn’t.
Want to make better decisions about AI adoption? Start by asking better questions. Instead of “How much does it cost?” ask “What’s the total cost of ownership?” Instead of “What can this tool do?” ask “How will this tool integrate with our existing systems and security protocols?” These are the questions that separate successful AI implementations from costly mistakes.
Beyond the free AI hype
Tools like DeepSeek won’t be the last to promise revolutionary capabilities at seemingly unbeatable prices. But as business leaders, we need to look beyond the allure of free and focus on sustainable, secure implementation. The most successful companies aren’t jumping on every new AI trend – they’re building rigorous frameworks for evaluating and implementing AI tools in ways that protect their data, their customers, and their future.
Remember: The goal isn’t to avoid new AI tools altogether – new tools are great. But it’s crucial to adopt them strategically and responsibly. Remember that uploading sensitive information into any free AI model out there isn’t without considerable risk. In the long run, paying for the right solution often costs less than dealing with the hidden prices of “free”. – Inc./Tribune News Service
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