🦾How You Can Leverage:

Oh, YOU didn't restructure your entire company to lay the groundwork for a $1 trillion IPO this week?

Thought we all did?

OpenAI did. Microsoft somehow won by losing equity. Amazon axed 14,000 due to AI (and there’s more on the chopping block). Google hit 650 million users.

And that’s just the start.

This week wasn't about flashy model releases. Nope. This was about partnerships, billion dollar deals, and corporate chess moves that'll shape the AI industry for years.

What'd you miss?

Here's what matters for AI updates this week.

Why build when you can buy?

Apple will reportedly pay Google $1 billion a year for a specialized version of Gemini AI with 1.2 trillion parameters, according to Bloomberg's Mark Gurman. The decision came down to cost, with Google's offer beating Anthropic's $1.5 billion annual price tag.

The new Gemini model will reportedly launch on Apple's private cloud servers in spring 2026, handling complex Siri tasks while Apple's own models continue running on devices for personal data. Apple doesn't plan to publicize Google's involvement, keeping the partnership mostly behind the scenes.

Here's the thing though. These two tech titans are already frenemies. Google pays Apple between $15 to $20 billion a year to remain the default search engine on Apple devices, a deal that has drawn antitrust scrutiny from the US Department of Justice.

Apple CEO Tim Cook hasn't ruled out acquiring an AI company, signaling that bigger moves may still be coming.

What it means: If you can't innovate fast enough, rent the innovation. Instead of building AI from scratch, partner with the leaders and focus on what you do best. Apple's betting on integration over innovation, and your company can follow the same playbook.

The White House just crushed any hopes of an AI bailout.

David Sacks, the White House AI and crypto czar, announced there would be no federal bailout for AI companies. The statement came after OpenAI made some government backing comments and as Michael Burry, the famed investor who predicted the 2008 housing crash, made headlines by acquiring over a billion dollars in put options against Nvidia and Palantir.

That sent global stocks tumbling. Nvidia dropped about 7%, and Palantir dropped 11%.

Sacks noted that The US currently has at least five major frontier AI model companies, so if one fails, others are ready to step in and fill the gap.

What it means: No safety net means the market decides winners and losers. Bet on multiple AI providers because if your vendor goes under, the government isn't swooping in to save them.

Smart money plays both sides.

Anthropic may soon be valued at over $350 billion as Google reportedly entered talks to deepen its current investment. That's twice as much as earlier this year, highlighting the straight up silly growth in the sector.

Why would Google stick billions more dollars into a technical competitor? If Anthropic ends up eating market share from Microsoft or OpenAI, Google still profits. It makes sense to fund one of your bigger competitors, especially on the coding side where Gemini 2.5 Flash and Flash Thinking have gotten extremely popular.

What it means: Hedge your bets like Google does. If you're building AI into your products, don't marry one provider. The landscape is shifting too fast, and the companies funding both sides understand something crucial about managing risk.

If Moonshot AI's benchmarks are to be believed, we could have a game changer.

Moonshot AI released a new open weight model called Kimi K2 Thinking, and its posted benchmark scores surpassed OpenAI's proprietary GPT-5 on several key tests. The most attention grabbing stat is Kimi K2 Thinking's 60.2% on BrowseComp, beating GPT-5's 54.9% and Claude Sonnet 4.5 Thinking at 24%.

Kimi K2 is released under a modified MIT license on Hugging Face that allows full commercial use, but requires prominent attribution only if a product serves over 100 million monthly active users.

We'll see how well it sticks because we've seen open source models from China get released with very high internal benchmarks that waver when third parties test them. Some original reporting claimed it only cost $4.5 million to train, but don't believe that. We heard similar Pinocchio tales from DeepSeek back in January until a semianalysis report debunked those training cost claims.

What it means: Open source AI is catching up to expensive proprietary models faster than anyone expected. Don't overpay for proprietary solutions when open source alternatives are hitting similar performance levels.

Fresh off their relationship change with OpenAI, Microsoft is now leaning into superintelligence on its own terms.

Microsoft is formally launching a superintelligence program aimed at building safer human centered frontier AI models. They're calling it humanist superintelligence. Microsoft AI chief Mustafa Suleyman will lead the new division with chief scientist Karén Simonyan and core Microsoft AI researchers shifting focus to this initiative.

Suleyman says the goal is superintelligence designed to be subservient to humans and to keep people at the top of the food chain. He cautioned that results will take time, noting it will likely be a good year or two before the team produces frontier models.

The move follows Microsoft's prior contractual limits with OpenAI that barred direct pursuit of AGI. With the new OpenAI restructure, Microsoft is now openly competing in the push toward AGI and beyond. Suleyman confirmed the team will explore recursive self improvement while recognizing the risks.

What it means: Microsoft is no longer waiting for OpenAI to deliver AGI. The AI race just got more competitive, which means better tools for you sooner and better pricing as more players compete.

Revenue growing. Opportunities growing. Job losses also growing.

IBM will lay off a low single digit percentage of its roughly 270,000 global employees this quarter. Even at 1%, that's 2,700 people, but it's probably more. Several thousand employees already received notices giving them 30 days to find a new role inside IBM or face termination.

The pivot is driven by rising demand in AI. CFO Jim Kavanaugh told analysts that roughly 80% of AI consulting and software clients in the last six months were new to IBM. Bookings reached almost $10 billion in the third quarter, up $2 billion from the prior quarter.

What it means: AI isn't just changing how we work. It's eliminating entire job categories while creating new ones. If your role doesn't involve AI consulting, implementation, or strategy, start learning those skills now.

What's a casual $600 billion among friends?

Meta confirmed it plans to invest $600 billion in US infrastructure and jobs by 2028. That's one of the largest private buildouts tied to AI and data centers in the country. CEO Mark Zuckerberg was caught on a hot mic back in September at a White House dinner throwing this figure out, but Meta just made it official with new details.

The company frames the spend as a national competitiveness play, saying data centers are crucial to maintaining America's tech edge and delivering the next wave of AI products. Meta links the investment to a push for personal superintelligence, a term for AI systems that surpass human cognitive abilities.

What it means: The AI infrastructure buildout is the biggest investment opportunity since the internet boom. If you're in construction, data centers, power generation, or semiconductors, position yourself to capture this wave.

Yeah, you read that right. Space.

Google's Project Suncatcher is exploring AI data centers in space to ease local opposition to new data centers while opening a new way to scale AI. The idea is to fly small satellites really close together so they can share data quickly, staying within about a kilometer of each other.

Google is stress testing its latest cloud TPU chips with high energy particle blasts to mimic space radiation. Early tests have been promising, showing the chips held up longer than expected before errors appeared. Google's goal is for each chip to last at least five years in space, which is the minimum needed to make sense fiscally.

The first two test satellites could launch by early 2027. Google hopes that by the mid 2030s, launch prices could drop enough to make space data centers cost competitive with those on the ground.

What it means: When Earth can't handle your compute needs, you literally go to space. The constraints aren't software anymore, they're physics and infrastructure. Understanding where the bottlenecks are helps you predict where the next breakthroughs will happen.

Should the government back AI like it backed banks?

OpenAI CFO Sarah Friar sparked controversy at a Wall Street Journal panel when she said she hoped the federal government would play a role in supporting AI investments. After fumbling for a word, she used backstop, which some interpreted as a request for federal guarantees.

After the media blew it up, Friar walked back the wording on LinkedIn, writing that OpenAI is not seeking a government backstop for infrastructure commitments. Then CEO Sam Altman reinforced the clarification on Twitter, stating that OpenAI neither has nor wants government guarantees for its roughly $1 trillion of data center plans.

The comments landed as AI linked stocks fell sharply. Nvidia was down 7% over the week, wiping out over $400 billion in market value. Microsoft dropped 4%. Palantir fell 13%.

Here's what it boils down to. After the financial crisis, the US government introduced a backstop for the banking system. A lot of people are wondering if AI deserves the same treatment given how strategically important it is.

What it means: OpenAI wants taxpayer protection for their trillion dollar bets but doesn't want to admit it publicly. Watch what companies do, not what they say. The fact that this conversation even happened tells you how risky these massive AI infrastructure commitments really are.