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The boom of AI investments

Perplexity AI, the startup positioning itself as a competitor to Google Search (NASDAQ: GOOGL), recently raised $500 million in funding, bringing its valuation to $9 billion. This highlights a broader trend in the artificial intelligence (AI) industry: the overwhelming appetite for AI investments, even as many of these companies remain unprofitable.

Despite their operational costs—which include training AI models, running resource-intensive systems, and competing in a talent war—startups like Perplexity AI continue to attract investor dollars. In North America, AI funding has steadily increased quarter over quarter while overall venture capital investment has declined, which means AI companies are receiving a larger percentage of the investment dollars that are up for grabs.

In my opinion, the question for this industry is beginning to become: how much longer will this phenomenon last? At the moment, the future of this trend looks uncertain due to these businesses having net negative business models. These high costs and a lack of profitability are raising eyebrows among investors. Even the biggest names in the industry, such as OpenAI, are not expecting to achieve profitability until the end of the decade. As a result, investors are now exploring the “boring” corners of the AI ecosystem, such as infrastructure and data centers, which are always in high demand since they are essential for supporting AI operations.

This shift indicates that the investment frenzy may eventually cool unless companies find ways to turn their technological advancements into sustainable business models. But for now, the clock is ticking, and each day that passes brings us closer to the moment when the investment pendulum begins to swing away from AI.

OpenAI’s launches telephone and text message access 

Meanwhile, OpenAI introduced two new features that allow users to interact with ChatGPT via voice calls and text messages.

Currently, anyone without an OpenAI account or subscription can call ChatGPT by dialing 1-800-242-8478 (1-800-CHATGPT). This enables users to engage with the generative AI chatbot in a conversational format, similar to a regular phone call. Users can simply speak on their phones instead of typing questions on a screen. However, this voice-call version lacks multi-modal capabilities, meaning users cannot send images or documents for analysis.

Users can reach ChatGPT through WhatsApp at the same number on the text-message front. Like its voice counterpart, the text-message version is limited and does not currently allow image uploads or document analysis.

When I first heard about these additions, I wondered, “Who would want this?” ChatGPT already offers a comprehensive experience through its app and desktop platforms.

OpenAI’s Chief Product Officer, Kevin Weil, said: “[Our goal at] OpenAI is to make artificial general intelligence beneficial to all of humanity, and part of that is making it as accessible as possible to as many people as we can. Today, we’re taking the next step and bringing ChatGPT to your telephone.”

After giving it further thought, I began to see the value in features like these. The more inroads that there are to onboard a user to a product or service, the better it will be for the company in terms of adoption. In this case, in particular, voice calls or text messaging might serve as a practical alternative in a pinch, especially for those without constant internet access. However, I think what will be even more valuable is how these features might inspire new use cases. With the ability to call or text ChatGPT directly, users could develop creative, task-specific applications that solve a user’s pain point without needing to use or own a smartphone or an entire computer setup with a ChatGPT subscription.

Apple Intelligence under fire for misleading summaries

Apple’s (NASDAQ: AAPL) notification summary feature, powered by the artificial intelligence that Apple calls “Apple Intelligence,” has come under scrutiny for its inaccuracies. This feature condenses multiple notifications into a single summary, allowing users to review notifications in a single message that groups them rather than receiving a constant stream of alerts—the problem is it’s not always accurate.

Although users were aware of this issue in the past, a recent incident flagged by the BBC showed the significance of this feature’s flaw. Apple’s AI miscategorized a BBC news article, creating a misleading summary. For news organizations bound by strict ethical and integrity standards, errors like this are highly problematic. Misrepresenting credible sources can damage trust in these instances and give the impression that the publication is unreliable.

In response, the BBC reached out to Apple, urging the company to address the issue, but at the time of writing, it remains unclear if any changes have been made.

This scenario underscores a broader challenge with generative AI: reliability. Early iterations of AI were notorious for “hallucinations,” where systems generated false or inaccurate information. While AI models have indefinitely improved since then, this incident is a reminder that AI still requires human oversight to prevent potentially harmful or misleading outcomes.

In order for artificial intelligence (AI) to work right within the law and thrive in the face of growing challenges, it needs to integrate an enterprise blockchain system that ensures data input quality and ownership—allowing it to keep data safe while also guaranteeing the immutability of data. Check out CoinGeek’s coverage on this emerging tech to learn more why Enterprise blockchain will be the backbone of AI. 

Watch: Micropayments are what are going to allow people to trust AI

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