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It’s March 7th, the big deadline day for the Digital Markets Act (DMA). The law came into effect on Thursday, so all the big tech companies operating gatekeeper services must have updates in place to accommodate the requirements. For Apple, it’s iPhone sideloading support via iOS 17.4. For Meta, that means WhatsApp and Facebook Messenger interoperability (interop, as Meta calls it) chats.

How about users? I’m a European who will be able to take advantage of all these DMA-related benefits. I already know I don’t want sideloading on iPhone (or Android, for that matter). But interoperability seems like the dumbest requirement of the DMA, a feature I don’t want to take advantage of in WhatsApp or any competing instant messaging app that might be labeled a gatekeeper. 

Meta’s explanation of how WhatsApp interop will work is also the best explanation for the unnecessary interoperability requirement. Why go through all this trouble to fix something that wasn’t broken in the first place? 

What is interoperability?

Meta explained in a detailed blog post all the work behind making WhatsApp and Facebook Messenger compatible with competing chat apps that ask to be supported. 

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That’s what interop hinges on. First, a WhatsApp rival must want their app to work with Meta’s chat platforms. Even if that’s achieved, it’s up to the WhatsApp/Messenger user to choose whether to enable the functionality. 

Meta says it wants to preserve end-to-end WhatsApp encryption after interop support arrives. It’ll push WhatsApp and Messenger’s Signal encryption protocol for third-party chat apps. Other alternatives can be accepted if they’re at least as good as Signal. 

How will it work?

Meta has been working for two years to implement the changes required by the DMA. But things will not just work out of the box starting Thursday. A competing service must ask for interop support and then wait at least three months for Meta to deploy it.

It might take longer than that for WhatsApp and Messenger to support that service. Rinse and repeat for each additional chat app that wants to work with WhatsApp. 

That’s a lot of work right there, both for Meta and WhatsApp competitors. I can’t see how any of this benefits the user. The interop chat experience isn’t worth it to me. Here’s what you’ll get in the first year. Because yes, the DMA has specific requirements in place for what features interop chats should offer: 

Interoperability is a technical challenge – even when focused on the basic functionalities as required by the DMA. In year one, the requirement is for 1:1 text messaging between individual users and the sharing of images, voice messages, videos, and other attached files between individual end users. In the future, requirements expand to group functionality and calling.

Thankfully, the DMA also focuses on privacy and security. That’s why WhatsApp and Messenger will focus on ensuring that chats remain end-to-end encrypted. I’ll note that Messenger end-to-end encryption started rolling out months ago, and it might not be available in all markets.

Meta’s blog does a great job explaining what’s going on under the hood with interop chats between WhatsApp and third-party apps. It underlines all the massive work and resources Meta is deploying for this.

I’m actually kind of in awe of Meta’s willingness to comply with these DMA provisions. All this effort makes me wonder what Meta can gain from the whole interoperability thing. Maybe the endgame is converting even more users to WhatsApp and Messenger, but I digress. After all, it’s not like Meta could avoid complying with the DMA.

I’ll also say that Meta doesn’t seem to restrict interoperabiltiy to the European Union, as Apple does with iPhone sideloading. Or, at least, restrictions aren’t the focus of this blog, though the title clarifies it’s about chats in Europe: “Making messaging interoperability with third parties safe for users in Europe.”

The obvious warning

While Meta also explains how encryption and user authentication will work, it acknowledges that it’s not in full control. Therefore, it can’t promise the user the same level of security and privacy for Whatsapp interop chats as Whatsapp-to-Whatsapp chats: 

It’s important to note that the E2EE promise Meta provides to users of our messaging services requires us to control both the sending and receiving clients. This allows us to ensure that only the sender and the intended recipient(s) can see what has been sent, and that no one can listen to your conversation without both parties knowing. 

While we have built a secure solution for interop that uses the Signal Protocol encryption to protect messages in transit, without ownership of both clients (endpoints) we cannot guarantee what a third-party provider does with sent or received messages, and we therefore cannot make the same promise.

[…] users need to know that our security and privacy promise, as well as the feature set, won’t exactly match what we offer in WhatsApp chats.

If you care about WhatsApp interoperability should read the entire blog post at this link. Then promptly disable the feature once WhatsApp informs you that interop support is ready.

When you picture the tech industry, you probably think of things that don’t exist in physical space, such as the apps and internet browser on your phone. But the infrastructure required to store all this information – the physical datacentres housed in business parks and city outskirts – consume massive amounts of energy. Despite its name, the infrastructure used by the “cloud” accounts for more global greenhouse emissions than commercial flights. In 2018, for instance, the 5bn YouTube hits for the viral song Despacito used the same amount of energy it would take to heat 40,000 US homes annually.

This is a hugely environmentally destructive side to the tech industry. While it has played a big role in reaching net zero, giving us smart meters and efficient solar, it’s critical that we turn the spotlight on its environmental footprint. Large language models such as ChatGPT are some of the most energy-guzzling technologies of all. Research suggests, for instance, that about 700,000 litres of water could have been used to cool the machines that trained ChatGPT-3 at Microsoft’s data facilities. It is hardly news that the tech bubble’s self-glorification has obscured the uglier sides of this industry, from its proclivity for tax avoidance to its invasion of privacy and exploitation of our attention span. The industry’s environmental impact is a key issue, yet the companies that produce such models have stayed remarkably quiet about the amount of energy they consume – probably because they don’t want to spark our concern.

Google’s global datacentre and Meta’s ambitious plans for a new AI Research SuperCluster (RSC) further underscore the industry’s energy-intensive nature, raising concerns that these facilities could significantly increase energy consumption. Additionally, as these companies aim to reduce their reliance on fossil fuels, they may opt to base their datacentres in regions with cheaper electricity, such as the southern US, potentially exacerbating water consumption issues in drier parts of the world. Before making big announcements, tech companies should be transparent about the resource use required for their expansion plans.

Furthermore, while minerals such as lithium and cobalt are most commonly associated with batteries in the motor sector, they are also crucial for the batteries used in datacentres. The extraction process often involves significant water usage and can lead to pollution, undermining water security. The extraction of these minerals are also often linked to human rights violations and poor labour standards. Trying to achieve one climate goal of limiting our dependence on fossil fuels can compromise another goal, of ensuring everyone has a safe and accessible water supply.

Moreover, when significant energy resources are allocated to tech-related endeavours, it can lead to energy shortages for essential needs such as residential power supply. Recent data from the UK shows that the country’s outdated electricity network is holding back affordable housing projects. This will only get worse as households move away from using fossil fuels and rely more on electricity, putting even more pressure on the National Grid. In Bicester, for instance, plans to build 7,000 new homes were paused because the electricity network didn’t have enough capacity.

In an era where we expect businesses to do more than just make profits for their shareholders, governments need to evaluate the organisations they fund and partner with, based on whether their actions will result in concrete successes for people and the planet. In other words, policy needs to be designed not to pick sectors or technologies as “winners”, but to pick the willing by providing support that is conditional on companies moving in the right direction. Making disclosure of environmental practices and impacts a condition for government support could ensure greater transparency and accountability. Similar measures could promote corporate accountability in global mineral supply chains, enforcing greater human rights compliance.

In navigating the intersection of technological advancement and environmental sustainability, policymakers are facing the challenge of cultivating less extractive business models. This is not just about adopting a piecemeal approach; it’s about taking a comprehensive systematic view, empowering governments to build the needed planning and implementation capacity. Such an approach should eschew outdated top-down methods in favour of flexible strategies that integrate knowledge at all levels, from local to global. Only by adopting a holistic perspective can we effectively mitigate the significant environmental impacts of the tech industry.

Ultimately, despite the unprecedented wave of innovation since the 1990s, we have consistently overlooked the repercussions of these advances on the climate crisis. As climate scientists anticipate that global heating will exceed the 1.5C target, it’s time we approach today’s grand challenges systemically, so that the solution to one problem does not exacerbate another.

OpenAI introduced ChatGPT in November 2022, sparking a tremendous amount of interest in artificial intelligence. ChatGPT gained so much attention that generative AI (GenAI) became a dominant theme in the tech world in 2023.

Microsoft backed OpenAI at the start of 2023 by pledging a multimillion-dollar, multiyear investment to accelerate OpenAI’s development of its AI technology.

Google made its GenAI move in March 2023 with Bard. In February 2024, Google rebranded Bard as Gemini when it debuted an improved version of the AI chatbot.

ChatGPT and Gemini are largely responsible for the considerable buzz around GenAI, which uses data from machine learning models to answer questions and create images, text and videos. OpenAI and Google are continuously improving the large language models (LLMs) behind ChatGPT and Gemini to give them a greater ability to generate human-like text.

GenAI is still rapidly evolving, and models don’t always return correct answers. Despite the common occurrence of AI hallucinations — wrong answers generated by AI — in both ChatGPT and Gemini, the tools are being adopted by businesses and consumers seeking to automate time-consuming tasks.

What is Google Gemini?

Gemini is Google’s GenAI model that was built by the Google DeepMind AI research library. The Gemini AI model powered Google’s Bard GenAI tool that launched in March 2023. Google rebranded Bard as Gemini in February 2024, several months after launching Gemini Advanced based on its new Ultra 1.0 LLM foundation. In May 2024, Google first offered users of Gemini Advanced access to the newer Gemini 1.5 Pro model.

Gemini is designed to retrieve information as a simple answer, similar to the way smart assistants like Alexa and Siri work. It uses LLMs to reply to prompts with information it has already learned or can retrieve from other Google services.

Google Gemini is multimodal — it understands audio, video and computer code as well as text. Google has paused Gemini’s image generation feature because of inaccuracies, however. Google’s statement disclosing the pause pledged to re-release an improved image generation feature soon.

Gemini’s capabilities are integrated into Google’s search engine and available in Google Workspace apps such as Docs, Gmail, Sheets, Slides and Meet. Gemini for Google Workspace is the new name for Duet AI for Google Workspace, which was Google’s answer to the Microsoft Copilot AI assistant. Google Gemini is available through an app on Android phones and in the Google app on iOS.

Gemini Advanced is part of the Google One AI Premium plan subscription service that costs $19.99 per month in the United States. Google One AI Premium also includes 2 TB of storage.

Gemini Advanced is a more powerful AI version than Gemini Pro, which remains available for free. Gemini Advanced with Gemini Pro 1.5 provides a large context window of 1 million tokens, enabling analysis of larger data sets.

Google suggests Gemini Pro and its AI capabilities is the better choice for development, research and creation tasks, and if you’re looking for a free chatbot. It brings AI to simple tasks for personal use. For those willing to pay the subscription fee, Google recommends Gemini Advanced for professional applications, more demanding workflows, enhanced performance and more cutting-edge capabilities. Google Advanced will also include early access to new features.

Gemini Nano, another part of the Google Gemini family, is used in devices such as Google’s Pixel 8 Pro smartphones.

What are the main differences between Gemini and ChatGPT?

ChatGPT and Google Gemini have become increasingly similar. Both have a free service, a nearly identically priced subscription service, and similar interfaces and use cases. The differences are largely under the hood — in their language models.

They’re also used for many similar functions, and work by users typing in a query to get a response. Both raise privacy concerns about how user data can be used. However, they differ in their training models, data sources, user experiences and how they store data.

Training models

ChatGPT is built on OpenAI’s GPT-3.5 or GPT-4. Gemini has three sizes: Gemini Pro for a wide range of tasks, Gemini Ultra for highly complex tasks, and Gemini Nano for mobile devices. Gemini Pro 1.5, which powers the subscription Gemini Advanced version, is faster and more advanced than the model used for the free Gemini service.

Data sources

The main difference between ChatGPT and Gemini is the data sources used to train their LLMs. GPT-4o uses predefined data that goes up to October 2023. Gemini draws on data pulled from the internet in real time. It is tuned to select data chosen from sources that fit specific topics such as coding or the latest scientific research.

User experience

ChatGPT users can log onto the free ChatGPT with any email account. ChatGPT also includes an API that developers can use to integrate OpenAI LLMs into third-party software. It lacks a Save button, but users can copy and paste answers from ChatGPT into another application. It does have an Archive button that can list previous responses in ChatGPT’s left-hand pane for quick retrieval.

Because ChatGPT is text-based, it can’t include images, videos, charts or links in its answers. It also lacks the ability to search the internet.

Because of OpenAI’s close partnership with Microsoft, ChatGPT can be used through Windows apps such as Word, Excel, PowerPoint and Outlook. Also, Microsoft’s Copilot AI assistants use the GPT-4 language model.

Gemini Pro’s interface gives users a chance to like or dislike a response, opt to modify the size or tone of the response, share or fact-check the response, or export it to Google Docs or Gmail. Gemini also has a “review other drafts” option that shows alternate versions of its answer. Gemini also lets users upload images, but its ability to create images is on hold until Google improves that feature.

Data storage and privacy

Both ChatGPT and Google Gemini store user data.

ChatGPT stores all prompts and queries entered. Users can review previous conversations through its archive feature. Although users can delete responses and conversations, the chatbot might continue to use these responses in its LLM for training. This raises privacy concerns when users enter personal data or proprietary information. OpenAI also discloses that ChatGPT gathers geolocation data, network activity, contact details such as email addresses and phone numbers, and device information.

According to OpenAI’s privacy policy, it collects any personal information a user provides. This includes account information such as name, contact information, payment card information and transaction history. OpenAI also might disclose geolocation data to third parties such as vendors and service providers, and to law enforcement agencies if required to do so by law.

OpenAI said the user retains ownership rights of input data and owns the output, but it “may use Content to provide, maintain, develop, and improve our Services, comply with applicable law, enforce our terms and policies, and keep our Services safe.”

Gemini stores conversations in a user’s Google account for 18 months, but users can change the retention period to three months or 36 months in their activity settings. Gemini conversations can also appear in searches, raising privacy concerns.

Google discloses that it collects conversations, location, feedback and usage information. The Google Privacy Policy claims Google uses collected data to develop, provide, maintain and improve services, and to provide personal services such as content and ads. Customers can delete information from their account using My Google Activity, or by deleting Google products or their Google accounts.

Google said it will share information to third parties with user consent and law enforcement when required.

Which chatbot is better?

There is a bit of a GenAI arms race going on now, with OpenAI and Google making updates to their models. Google has been especially aggressive, perhaps because ChatGPT came out first and Gemini must play catch-up. With each new version of the LLMs, Google and OpenAI make significant gains over their previous versions.

Generally, ChatGPT is considered the best option for text-based tasks while Gemini is the best choice for multimedia content. However, there are other considerations, as noted in earlier sections of this article. Users can try the free versions to determine which works better for them.

There have been several in-depth reviews about the chatbots worth noting:

• Researchers from Carnegie Mellon University and BerriAI benchmarked Gemini Pro against GPT-3 and GPT-4 on 10 diverse language tasks with the goal of providing an impartial in-depth analysis. They found Gemini’s strengths included performance on long, complex reasoning chains and translating into non-English languages. On the downside, it struggled with mathematical reasoning — especially with large numbers — showed bias on multiple choice questions and aggressive content filtering blocked many responses. In summary, the researchers concluded Gemini Pro did not match GPT-3 and GPT-4, but “exhibits strengths in handling complexity and reasoning depth.”
• Ethan Mollick, an associate professor who studies AI at the Wharton School of the University of Pennsylvania, performed what he called “tasting notes” of Gemini Advanced vs. GPT-4. Mollick concluded that Gemini Advanced is the first advanced AI model that can compete with GPT-4. He said each has its strengths and weaknesses — for example, GPT-4 uses code in a more sophisticated way and is better at hard verbal tasks while Gemini is better at explanations and search. But both “are weird and inconsistent and hallucinate more than you would like.”
• Bernard Marr, a futurist and author of Generative AI in Practice, pointed out in a Forbes article that ChatGPT is designed to be more conversational while Gemini processes information and automates tasks more efficiently. Marr’s conclusion after using ChatGPT and Gemini is that ChatGPT-4 is the more powerful chat interface but “Gemini is closing the gap …”.
• Neither ChatGPT nor Gemini are perfect, and their developers admit that. Both generate hallucinations and even warn users of that in their responses.
• Both of the chatbots include a disclaimer on the bottom of their prompt screens. Gemini’s reads: “Gemini may display inaccurate info, including about people, so double-check its responses.” ChatGPT advises: “ChatGPT can make mistakes. Consider checking important information.”
• The Gemini FAQ on Google’s website offers this valuable advice that can apply to all AI tools:
  • Gemini can’t replace important people in your life, like family, friends, teachers or doctors.
  • Gemini can’t do your work for you.
  • Gemini can’t make important life decisions for you.

Generative AI alternatives

GenAI is a fast-moving technology. Besides the updates to ChatGPT and Google Gemini, other companies are working on AI projects. These include AI21 Labs’ Wordtune, Anthropic’s Claude, Glean, Jasper, Open Assistant and Writesonic’s Chatsonic. China’s Baidu search engine uses AI with an application called Ernie Bot. Many productivity applications and SaaS products also incorporate GenAI assistants.

Comparison of ChatGPT vs. Gemini responses

We asked ChatGPT 3.5 and Google Gemini Pro the same requests and prompts to see how their responses would compare. The results are as follows:

Idea generation

Prompt: What are the five hottest IT trends an IT professional should know about?

Thoughts: ChatGPT’s answers were more general while Gemini drilled down into specific areas — for example, generative AI vs. AI/ML and cybersecurity mesh vs. cyber security. ChatGPT’s inability to reference data past January 2022 limits its effectiveness when looking for trending information. Gemini snuck in a few extras under “Bonus trends.”

Creating content

Prompt: Write a two-paragraph summary explaining cyber-resiliency challenges.

Thoughts: Both did a good job of explaining and summarizing a complex issue in two paragraphs, but Gemini included more specifics about the challenges and what can be done about them.

Planning

Prompt: What are the best cloud computing conferences to attend?

Thoughts: ChatGPT listed more conferences, but its list was a bit dated as several of its conferences have been renamed. Gemini offered greater detail and broke its list into specific areas of expertise.

Developer assistance

Prompt: List 10 frequently used SQL queries for querying a PostgreSQL database.

Thoughts: The lists were similar, although they used different terms in some cases. A nice feature was the code embedded in the responses. We shortened Gemini’s response to fit on one page, but its longer version included embedded code.

Dave Raffo is an independent IT analyst and journalist. He previously worked as a senior analyst at The Futurum Group and Evaluator Group, covering integrated systems, software-defined storage, container storage, public cloud storage and as-a-service offerings. He previously worked at TechTarget from 2007 to 2021 as executive news director and editorial director for its storage coverage, and he was a technology journalist for 30 years.