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Microsoft Is Redesigning Copilot Because AI at Work Still Feels Clunky

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Microsoft
News

Microsoft Is Redesigning Copilot Because AI at Work Still Feels Clunky

Microsoft Is Redesigning Copilot Because AI at Work Still Feels Clunky

Microsoft is giving Copilot a cleaner design and faster responses while trying to make workplace AI feel less frustrating.

Microsoft is redesigning Copilot again, and honestly, that’s a move in the right direction.

Not because they’re packing it with some groundbreaking new feature, but because they seem to have finally realized that the biggest hurdle for workplace AI is friction. People don’t always enjoy using it, and that’s a massive problem for adoption.

The original pitch for Copilot was too good to be true: let AI handle the heavy lifting. Draft the emails, summarize the hour-long meetings, dig through the endless documents, and magically return everyone’s hours for the week.

But for several employees, the reality has been decidedly less magical. Using Copilot often meant adding an extra layer of management between you and the task you were trying to finish. Instead of making the work disappear, AI often turned into work itself.

That’s the core tension driving Microsoft’s latest overhaul.

They’re pushing for a cleaner, more streamlined interface across Microsoft 365. On paper, these sound like basic design tweaks, but they might be exactly what the tool has been missing. For the last two years, the AI industry has been obsessed with “more”- more parameters, smarter models, and more features. The bet was simple: if we make it powerful enough, people will naturally gravitate toward it.

That bet didn’t really pay off.

Businesses are realizing that employees don’t care how “impressive” a model- especially if it forces them to change their workflow or wait on a lagging interface. People prioritize convenience and speed over raw, forced complexity when it comes to their grind.

Microsoft’s pivot suggests they’re finally listening. The redesign is all about getting out of the user’s way- a subtle, necessary shift in how they’re framing the product. The next phase of workplace AI isn’t going to be won by the company with the most “intelligent” chatbot. It’s going to be won by the company that makes AI feel almost invisible.

That is the real challenge Microsoft is facing right now. Copilot doesn’t need to be more powerful, but more effortless.

Because right now, for a lot of us, AI still feels like just another person we have to manage. And the moment a productivity tool starts feeling like a second job, you’ve already lost the room.

Tiktok
News

TikTok’s Owner ByteDance Plans to Invest in AI Chips

TikTok’s Owner ByteDance Plans to Invest in AI Chips

ByteDance is the latest company to develop its own custom AI chips. Big Tech no longer wants to rent power as the global compute race intensifies.

ByteDance is reportedly developing its own AI chips. That might sound like the kind of technical industry news most people scroll past.

It really isn’t.

It is another sign that the AI race is shifting from apps and chatbots to something much more basic: who controls the computing power underneath everything.

Tech companies have mostly relied on firms like Nvidia, Intel, and AMD for chips. But AI changed the equation. Suddenly, everyone needs enormous amounts of computing power simultaneously. Training models, running AI assistants, generating videos, recommending content- all of it depends on infrastructure that’s becoming incredibly expensive and difficult to secure.

That’s why companies are starting to think differently.

Instead of endlessly buying hardware from someone else, many of the world’s biggest tech firms now want to build their own chips tailored to their specific AI needs. Apple did it with iPhones years ago. Google has its Tensor chips. Amazon built its own AI hardware for AWS. Now ByteDance is joining the same club.

ByteDance is working on custom CPU designs to support its growing AI ecosystem. That includes products beyond TikTok, such as AI tools and assistants, that the company has been aggressively expanding.

There’s also a bigger geopolitical angle hanging over this.

US restrictions on advanced chip exports have put pressure on Chinese tech companies to reduce dependence on foreign suppliers. So this isn’t just about saving money or improving performance. It’s also about long-term control.

What makes this interesting is how quickly chip-building has gone from niche strategy to industry trend.

Most people thought AI competition would be about which chatbot sounded smartest a few years ago. It increasingly looks like the companies with the most control over infrastructure may have the bigger advantage.

Because in AI, intelligence matters.

But access to computing power may matter even more.

CNN
News

CNN Is Suing Perplexity: A Never-Ending Battle Between Publishers and AI Firms

CNN Is Suing Perplexity: A Never-Ending Battle Between Publishers and AI Firms

CNN to sue Perplexity for using its content without permission or payment.

The media industry spent years worrying that Google and Facebook were swallowing ad revenue. Now publishers are facing a different fear: AI companies may absorb the content itself.

CNN has now filed a lawsuit against Perplexity, accusing the AI firm of unlawfully distributing its copyrighted pieces through AI responses. The case adds CNN to a growing list of publishers taking legal action against AI firms over how their systems collect and present news content.

Perplexity’s entire pitch is speed and convenience. Instead of showing users a page full of links, it gives direct answers generated from information gathered across the web. That’s exactly why publishers are worried.

If people get the summary without clicking the source, what happens to the business that funded the reporting in the first place?

CNN’s complaint argues that Perplexity benefits from journalism it didn’t create or pay for. The network also pointed out that quality reporting is expensive, time-consuming, and often dangerous to produce.

This lawsuit didn’t appear out of nowhere either.

Perplexity has already faced legal pressure from companies, including The New York Times, Dow Jones, Reddit, and other publishers, accusing the startup of scraping or reproducing content without proper authorization.

At the center of all these cases is a question the tech industry still hasn’t answered clearly: when AI summarizes information, where does fair use end and copying begin?

That question matters because AI search changes the internet’s flow of attention. Traditional search engines still pushed users toward websites. AI-generated answers risk keeping users inside the platform instead.

For publishers, that’s not a small shift. It’s existential.

AI companies simultaneously argue that these systems help people access information faster and more efficiently. Some media organizations have already signed licensing deals with AI firms rather than fighting them in court.

But CNN’s lawsuit shows many publishers are no longer willing to wait and see how this plays out.

The AI boom has often been framed as a battle between tech giants building smarter systems.

Increasingly, it also looks like a battle over who owns the value created by human knowledge in the first place.

Google
News

Google Adds New Controls for Search Personalization, History, and More.

Google Adds New Controls for Search Personalization, History, and More.

Google seems to be on a revamp spree. Just recently, it introduced updated icons for its Google apps section on Chrome while discussing new workspace icon designs.

But the changes aren’t just aesthetic- or all about the ‘vibe.’

The tech powerhouse is also rolling out new features that directly impact users’ search behavior. It can be said to be a ripple effect of all the structural changes it has been making, especially to cement itself as a pioneer of AI-led search, if we want to be specific.

The first step was doubling down on AI-powered search, and the second was introducing AI Mode. Users ask questions ‘naturally’ and let AI do the heavy lifting. The goal is to deepen research while decreasing response times. Because who has the time to actually ‘look for’ answers any longer?

That happened merely a week ago.

Now that Google has set the stage, it’s diving deeper into the internal restructuring. The revamp is more about controls and transparency.

Users could previously access their recent and past activities under the ‘Web & App’ activity section, while handling customized recommendations within ‘Search Personalization.’ But that’s no longer the case.

To grant users more control, it’s creating separate, independent controls-

First, the “Search Services History” will determine whether (and if) Google can store user activity across apps and services. That includes search queries across Flights, Maps, Translate, News, and AI-powered responses.

There’s another sub-setting included: Saved Media.

Users will be able to enable (and disable) the setting depending on whether they want Google to save all their media files from videos and audios to images included across their Google Lens sessions, voice searches, and Search Live. If users choose to retain their media, they’ll additionally have the option to remove saved media files from the history section.

Second, “Personalized Recommendations” that will enable (or disable) whether Google can leverage a user’s activity and history to tailor suggestions and search results.

These changes actively target the ongoing discussions surrounding user consent and data privacy. All the while promoting interactive AI features that have little to do with appearance and more to do with how businesses have become more sensitive towards user privacy.

Users want personalization. They want access to their past activity. But not at the cost of their personal space.

And that’s what Google is conquering: the gap between what’s promised and how it’s delivered.

NVIDIA
News

NVIDIA Invests in Taiwan, Citing It as the ‘Epicenter’ of AI Revolution

NVIDIA Invests in Taiwan, Citing It as the ‘Epicenter’ of AI Revolution

NVIDIA’s CEO calls Taiwan the heart of the AI revolution. Behind the statement is a massive shift in tech power.

The AI conversation usually lands in familiar places: Silicon Valley, OpenAI, Google, Microsoft.

Jensen Huang wants to redirect the attention.

The NVIDIA CEO said this week that Taiwan is the “epicentre” of the AI revolution and predicted the island will remain one of the world’s most significant tech manufacturing hubs for decades to come. He made the remarks while unveiling NVIDIA’s planned Taiwan headquarters, which is expected to break ground this year and become operational by 2030.

On the surface, it sounds like a ‘smart’ corporate move during a launch event. But Huang’s argument for choosing Taiwan is difficult to dismiss.

Nearly every major AI breakthrough eventually runs into one unavoidable requirement: chips. And Taiwan plays a critical role in making those chips- especially through manufacturers like TSMC. NVIDIA itself reportedly plans to increase annual spending in Taiwan to around $150 billion, a massive jump from previous years.

That says something important about where AI truly power sits. And also challenges Silicon Valley’s foothold as the nucleus of AI advancement.

Consumers see chatbots, AI search, and image generators. Behind all of that is a supply chain built on factories, advanced packaging, semiconductors, and infrastructure. A surprising amount of it connects back to Taiwan. That’s where Taiwan beats Silicon Valley by a whole lot.

And the timing also matters.

Taiwan occupies a sensitive position as tensions between China and the West continue to rise. A strategy is no longer a regular investment announcement when one of the world’s most valuable companies commits a figure like $150 billion. It’s a long-term bet.

Huang is a Taiwan-born and often speaks about the island’s importance for tech innovation. But the message cut deeper this time: AI may look like software on the surface, but it remains deeply tied to physical manufacturing.

The AI race is often framed as a competition over intelligence.

Increasingly, it looks like a competition over who builds the world that powers that intelligence.

Microsoft
News

Microsoft Cancels Claude Licenses: Is AI Not the Answer?

Microsoft Cancels Claude Licenses: Is AI Not the Answer?

Microsoft is, by reports, canceling Claude Code licenses across its most prominent divisions. Is this to promote GitHub Co-Pilot or the signaling of a deeper problem?

Microsoft has decided to end the Claude Code licenses inside its experiences and devices group, a.k.a the teams behind Windows, Microsoft 365, Outlook, Teams, and Surface.

For Anthropic, this has to be nothing but bad news. Microsoft, which has been heavily investing in OpenAI and other AI tools, has always been optimistic about the tech. Trying this new thing out in their divisions could be part of a larger experiment to see what is working.

But we suspect there is something bigger here at play. The cost of running an AI system is not efficient, as these tools are developing into smarter versions of themselves- they are becoming more and more energy inefficient.

The tokens that companies use cost a lot of money because they require a lot of money. And the usage is drying up.

Claude’s usage windows have been reducing at a steady pace, sometimes getting over in one or two prompts or actions.

Uber, as every outlet has reported, has faced similar problems. The token budget Uber thought it needed was blown away in just 4 months. Of course, programming or doing any real work is complex. It requires experience, and thinking, and clearly thinking does not come cheap, even when organizations think it does.

Computing, thinking, and intelligence, and the ability to synthesize information, are a scarce resource. And AI might find it difficult to replicate it across multiple instances. It can do certain tasks very well, but it needs many tokens to do it.

That is difficult en masse.

However, this raises a question: where is AI heading, not in terms of if it will get better or more intelligent, but rather, how much energy would it need?

AI data centers cannot be called efficient. They are guzzling energy, and with everyone using it, that rate may be exponential. Every hard problem, every doc generated, every code written, every long-chain task it performs, the tech eats energy on a large scale.

The hard problem here isn’t managing costs, but scaling down energy costs. The question is how? We are currently using the LLMs and agents to solve problems by making them amazing at guessing what comes next, and that guesswork is draining every compute token dry.

It’s time we move beyond discussing alternatives and put research into finding them. Or this might end up as a cost that cannot be recovered.