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OneTrust and Snowflake Partner Up to Make Consent Signals More Actionable

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Snowflake
News

OneTrust and Snowflake Partner Up to Make Consent Signals More Actionable

OneTrust and Snowflake Partner Up to Make Consent Signals More Actionable

Snowflake and OneTrust are baking consent into data sharing as AI makes privacy mistakes far more dangerous for brands.

For a long time, “user consent” in marketing basically meant one thing: annoying cookie banners that everyone clicked through without reading.

That system was always shaky, but AI is exposing just how messy it really is.

Snowflake and OneTrust just announced a partnership that allows companies to carry user consent signals directly into Snowflake’s data collaboration environment. Sounds technical. Because it is, but the bigger story here is much simpler: companies are starting to panic about what happens when AI trains on data it was never supposed to touch.

And that panic is justified.

Before AI exploded, ineffectual data governance was primarily a compliance headache. Maybe regulators fined you. Maybe legal got involved. Maybe consumers got angry for a few days online. But gen AI completely changes the scale of the problem.

Once questionable data enters an AI system, pulling it back out is not easy. In some cases, companies may have to retrain or even roll back entire models. That is expensive, messy, and terrible for trust. OneTrust’s strategy chief, Ojas Rege, basically admitted as much, saying rollback may be the “only remedy” in certain situations.

So now the industry is trying to solve a problem it probably should have addressed years ago: ensuring consent remains attached to the data wherever it goes.

That matters because modern marketing data travels across several points. Between brands, ad platforms, analytics systems, clean rooms, AI tools, and external partners, information is constantly floating. Somewhere along the way, the original permissions often become vague or disconnected.

AI makes that vagueness dangerous.

And honestly, this feels like the start of a much larger shift. Companies spent the last two years obsessing over how much data they could collect for AI. Now they are realizing the more important question is whether they are actually allowed to use it.

That changes the conversation entirely.

Because in the AI era, ineffective consent management is no longer just sloppy marketing. It is a business risk.

Substack
News

Substack Loses Brownie Points as Writers Move to Ghost and Beehiiv

Substack Loses Brownie Points as Writers Move to Ghost and Beehiiv

More creators are leaving Substack for Ghost and Beehiiv as frustrations grow over growth and platform control.

Substack felt like the future of media- for a little while.

Writers could leave collapsing newsrooms, build direct audiences, charge subscriptions, and finally “own” their work. It looked clean, independent, even rebellious. But now a growing number of creators are realizing something uncomfortable: they may not have owned as much as they thought.

According to a new report from The Verge, more writers are leaving Substack for rivals like Ghost and Beehiiv, frustrated by rising costs, platform dependence, and Substack’s increasing shift toward becoming a social network.

And honestly, this feels like a very familiar internet story.

Platforms usually begin by empowering creators. Then they grow. Then they optimize for engagement. Then, creators slowly realize the platform’s priorities are no longer actually aligned with theirs.

Substack’s biggest issue is what many writers now call the “Substack tax.”

The company takes a 10 percent cut of subscription revenue, which sounds manageable until newsletters scale. And for large publications, that can turn into tens or even hundreds of thousands of dollars a year.

That is why its competitors are suddenly gaining momentum. They charge flatter fees, offer more customization, and offer creators a stronger sense of ownership over their audience and brand.

Because that is the real tension underneath all of this: creators no longer merely want monetization. They want control.

And Substack has begun to feel like it wants creators inside its ecosystem rather than building independent media businesses externally. The company has leaned heavily into all social nitty-gritty creators were running away from- algorithmic discovery, Notes, video features, and even social-style engagement systems. That helps Substack grow as a platform, but not every writer wants to become a part-time content creator feeding another recommendation engine.

There is also something bigger happening here. The internet is moving away from giant centralized platforms again. Slowly but noticeably.

Writers watched what happened to creators on Facebook, YouTube, Instagram, and even Twitter. Algorithms changed. The reach collapsed. Businesses disappeared overnight. So now many newsletter publishers are asking a smarter question earlier: if your audience lives on someone else’s platform, do you really own it at all?

Substack helped revive independent publishing. That part is real.

But creators increasingly seem to be treating it less like a permanent home and more like a launchpad they eventually plan to leave.

NVIDIA
News

NVIDIA is Financing the Entire Gold Rush, Invests Billions in IREN

NVIDIA is Financing the Entire Gold Rush, Invests Billions in IREN

NVIDIA’s $2.1 billion IREN deal shows the AI boom is no longer just about chips- it’s now a massive infrastructure and finance race.

NVIDIA has crossed an invisible line in the AI boom. It is no longer just the company selling shovels during a gold rush. Increasingly, it is also financing the mines.

NVIDIA announced an investment deal of up to $2.1 billion into data centre operator IREN as part of a broader partnership to deploy as much as 5 gigawatts of AI infrastructure. That is an extraordinary number.

For context, 5 gigawatts is the scale of infrastructure usually associated with national energy planning, not a single technology partnership.

And that’s precisely the point.

The AI industry is entering a new phase where software innovation is no longer the only bottleneck. It is electricity, cooling, fibre optics, land, financing, and raw compute capacity. NVIDIA understands this better than anyone, which is why the company is rapidly evolving from chipmaker into infrastructure kingmaker.

The most interesting part of the IREN deal is not even the money. NVIDIA secured rights to buy up to 30 million IREN shares at $70/piece across five years. In other words, NVIDIA is embedding itself financially inside the ecosystem it powers. The company increasingly profits not only when customers buy GPUs, but when the entire AI infrastructure economy expands.

That is a dangerous level of gravity for one company to possess.

The AI market already revolves around NVIDIA’s chips.

Now the company is moving deeper into datacentres, cloud infrastructure, optics, and even factory construction. Just this week, NVIDIA also committed billions toward expanding fibre-optic manufacturing with Corning. Meanwhile, companies like CoreWeave, IREN, and Nebius are effectively becoming extensions of NVIDIA’s ecosystem.

It looks less like a healthy technology market and more like the emergence of an AI industrial complex.

Of course, investors love it because demand still appears endless. Big tech is projected to spend more than $700 billion on AI infrastructure this year alone. But history has punished industries that assume demand curves only move upward.

The irony is that NVIDIA may now be too important for the AI economy’s stability. When one company supplies the chips, funds the infrastructure, shapes the architecture, and influences the financing, the entire market inherits the same concentration risk.

And concentration risk has a long history of looking brilliant right before it becomes terrifying.

AI
News

Big Tech’s AI Obsession Is Now Distorting the Global RAM Market

Big Tech’s AI Obsession Is Now Distorting the Global RAM Market

Big Tech firms are scrambling for RAM to fuel AI growth- and the rest of the tech industry may end up paying the price.

The AI boom has officially entered its “resource panic” phase.

Not software. Not models. Not chatbots. RAM.

According to a recent report covered by The Verge, major tech companies are now offering unusually generous deals and incentives to secure memory chips for AI infrastructure. Translation? The businesses building AI systems are getting nervous that there will not be enough memory to go around.

And honestly, this says a lot about where the AI industry is actually heading right now.

For all the futuristic marketing surrounding artificial intelligence, the entire framework still depends on very physical, limited hardware. AI models are memory-hungry monsters. Training them takes enormous amounts of DRAM and high-bandwidth memory, and running them at scale takes even more.

Every chatbot response, AI-generated image, or automated workflow sits on top of warehouses full of servers burning through memory at exponential rates.

The problem is that only a handful of companies really control the global RAM market- mainly Samsung, SK Hynix, and Micron. So, everybody else starts feeling the squeeze when trillion-dollar tech giants start aggressively locking in supply.

That “everybody else” includes consumers.

Laptop prices rise. Gaming hardware gets more expensive. Smartphone manufacturers start cutting corners or increasing prices. The AI race you never asked to participate in quietly affects the price of your next device.

It’s interesting how quickly the industry has shifted from optimism to spearheaded competition. AI companies were talking about possibilities over a year ago. Now they are fighting over infrastructure like countries fighting over oil.

And that changes the conversation completely.

Because this is no longer just a software revolution but an industrial one. Those rich enough to secure the raw material before their competitors do will be the ultimate winners.

The AI boom is starting to become more of a global resource grab.

And RAM is one of the first battlegrounds.

DeepSeek
News

China’s DeepSeek Proves It’s Ready to Compete in the Big Leagues

China’s DeepSeek Proves It’s Ready to Compete in the Big Leagues

DeepSeek’s possible $45 billion valuation signals China’s AI race is no longer about survival- it’s now about scale and, most crucially, dominance.

For a while, Silicon Valley treated China’s AI ambitions like an imitation game. Fast followers. Cheap replicas. Strong domestically, but still trailing the American frontier. DeepSeek’s explosive rise is beginning to destroy that narrative.

The Chinese AI startup is reportedly nearing a valuation between $45 billion and $50 billion as it enters its first major fundraising round, with China’s powerful state-backed semiconductor fund expected to lead the investment.

That number matters.

Not just because it is enormous, but because of what it represents: China is no longer simply trying to survive US tech restrictions. It is building an alternative AI ecosystem with serious momentum behind it.

DeepSeek became globally relevant after shocking the industry with powerful large language models developed at a fraction of the cost associated with American rivals. That alone rattled investors. The assumption had been that frontier AI required near-infinite capital, Nvidia dependency, and hyperscaler-level infrastructure.

DeepSeek challenged that belief.

Now Beijing appears ready to push even harder.

The involvement of China’s “Big Fund” changes the story from startup success to national strategy. AI in China is being treated more like critical infrastructure- similar to energy, defense, or telecom.

The competitive environment in China differs from that in the West.

American AI firms continue to be driven by venture capital expectations and quarterly market pressure. Meanwhile, Chinese AI companies are backed by state-aligned industrial policy and long-term financing

.

The West has honestly underestimated the severity of this combination.

What makes DeepSeek particularly interesting is that it has evolved during pressure, not abundance. US export restrictions on advanced chips were supposed to slow China’s AI progress. Instead, companies like DeepSeek began adapting models for domestic hardware, accelerating China’s push toward technological self-reliance.

That doesn’t mean China has overtaken OpenAI or Anthropic. The top American labs still dominate at the bleeding edge. But the conversation has changed. AI is no longer a one-country race.

It is becoming a geopolitical arms race with two entirely different systems competing to shape the future- one fueled by venture capital, the other by state power.

And DeepSeek may be the clearest sign yet that China intends to stay in that fight for the long haul.

Quantum
News

Quantum Computing’s Biggest Bet Yet is on Manufacturing, Not Physics.

Quantum Computing’s Biggest Bet Yet is on Manufacturing, Not Physics.

Quantum Motion’s $160 million raise signals a shift in quantum computing: the race is no longer merely about science, but scalable production.

For years, quantum computing has existed in a strange limbo between scientific breakthrough and an expensive science fair project. The promises have always sounded revolutionary, i.e., machines capable of solving problems impossible for today’s computers.

However, the industry itself falls into the well-known trap- burning cash while chasing scale and relevance.

A London startup called “Quantum Motion” is now trying to resolve the problem from a completely different angle: through ordinary silicon chips, rather than exotic hardware. And investors are paying attention.

Quantum Motion announced it had raised $160 million to build quantum computers using standard silicon transistor manufacturing techniques this week. That matters because the company is essentially betting that the future of quantum computing will not belong to whoever builds the cleverest qubit in a lab, but to whoever figures out how to manufacture millions of them cheaply and reliably.

That is a very semiconductor-style way of thinking.

Most major quantum players, such as IBM and Google, have focused on superconducting systems or other highly specialized approaches. They work, but scaling them into commercially viable machines remains brutally difficult.

Quantum Motion’s logic only sounds simple in theory: take the same transistor architecture already used across phones and laptops and modify it enough to behave like quantum bits (or qubits).

The keyword here is “just enough.”

That mindset could become the industry’s defining shift. The quantum sector is slowly realizing that physics alone is no longer the bottleneck. Manufacturing is.

History shows this repeatedly: transformative tech exists only when they are reproducible at scale. Think about transistors. It changed the world because companies learned how to cheaply mass-produce it.

Quantum computing may now be approaching the same inflection point.

Quantum Motion claims it could eventually build useful quantum systems for as little as $10-20 million, still absurdly expensive by consumer standards, but dramatically cheaper than many current experimental systems. Whether that vision works remains uncertain.

Quantum computing is still filled with timelines that collapse under real-world pressures. But the bigger story is psychological.

Investors are no longer funding quantum companies purely because the science sounds futuristic. They are funding companies that seem like they might actually manufacture something real.

And honestly, that is probably the first genuinely mature sign this industry has shown in years.