NVIDIA's $100B Open AI Investment: What the Deal Really Means

When NVIDIA CEO Jensen Huang casually mentioned that his company might make "the largest investment we've ever made" in Open AI, it sent shockwaves through Silicon Valley. This wasn't just another funding announcement. This was a signal that the race to build AI infrastructure had entered a new phase.

But here's where it gets complicated. The Wall Street Journal reported that the original $100 billion deal between the two companies had stalled. Then Huang pushed back, calling those claims "nonsense" and reaffirming NVIDIA's commitment. So what's actually happening? Is the deal dead, restructured, or just hitting normal negotiation bumps?

The truth is messier and more interesting than headlines suggest. Understanding this investment requires zooming out to see why two of tech's most powerful companies need each other, what they're actually building together, and what happens when AI infrastructure becomes the bottleneck holding back the entire industry.

TL; DR

• $100B partnership: NVIDIA and Open AI announced plans to build 10 gigawatts of AI data centers, with phase one launching in late 2026
• Deal uncertainty: Wall Street Journal reported negotiations stalled, but Huang denied this, suggesting the deal is being restructured rather than cancelled
• Not all at once: The investment won't be the full $100 billion in a single funding round—Huang clarified it would be spread across multiple phases
• Strategic necessity: Both companies benefit enormously—NVIDIA gets a guaranteed buyer for advanced chips, Open AI gets the computational infrastructure to compete with larger rivals
• Industry-wide impact: How this deal plays out affects AI development across the entire ecosystem, from startups to Big Tech competitors

The Context: Why This Matters Right Now

You need to understand something fundamental about AI economics. Training and running large language models is absurdly expensive. We're talking about computational demands that dwarf anything that existed five years ago.

Open AI's Chat GPT costs roughly $700,000 per day to operate at its current scale. That's not a typo. Scale that across every API call, every research project, every enterprise deployment, and you're looking at infrastructure costs that would bankrupt traditional software companies in months.

NVIDIA's GPU chips are basically the only game in town for this workload right now. Their H100 and newer Blackwell chips are purpose-built for the parallel processing that AI models require. AMD's trying to catch up. Intel's trying. But NVIDIA controls somewhere around 80-90% of the high-end AI chip market.

This creates a problem for Open AI. They're in a competitive arms race with Google, Meta, and increasingly, Chinese AI labs. They need cutting-edge GPUs. But those chips are in scarce supply, expensive as hell, and NVIDIA can basically set the price.

Meanwhile, NVIDIA's facing a different problem. Their stock price has gone ballistic—it went from around

A $100 billion guaranteed purchase order from Open AI essentially locks in demand and validates that AI compute will remain a critical bottleneck for years to come.

The Original Deal: What Was Actually Announced

Let's go back to September 2024. NVIDIA and Open AI made a joint announcement that, frankly, seemed almost too perfect for both companies.

The headline: NVIDIA would invest up to $100 billion in a partnership to build 10 gigawatts of AI data center capacity. For context, 10 gigawatts is roughly equivalent to the entire electricity consumption of a mid-sized country. This wasn't hyperbole—this was an unprecedented commitment.

The structure was interesting. Rather than Open AI needing to raise money directly, NVIDIA would essentially fund the infrastructure itself. Open AI would handle the operational and technical side. The companies set a target date of the second half of 2026 for the first phase to go live.

On paper, this solved multiple problems simultaneously. Open AI got guaranteed access to cutting-edge chips without having to negotiate directly with NVIDIA for every order. NVIDIA got a locked-in customer with massive volume commitments. And both companies could claim they were building the future of AI responsibly by planning infrastructure that could support safe scaling.

But—and this is a crucial but—the deal was never finalized. No binding contracts were signed. No money changed hands. It remained what's called a "letter of intent," which in corporate terms means "we're pretty interested but haven't actually committed yet."

The Stall: What Wall Street Journal Actually Reported

In early January 2025, the Wall Street Journal reported something that made investors nervous. The deal hadn't just stalled—it had barely progressed beyond "early stages" of negotiations.

Huang, they reported, had privately highlighted that the agreement was nonbinding. More troublingly, sources said Huang had criticized Open AI's business approach as lacking discipline. That's corporate speak for "they're not running things the way we'd want our $100 billion to be managed."

This raised legitimate questions. Was Huang having second thoughts? Did NVIDIA's board push back on the scale of the commitment? Was Open AI being difficult to work with as a partner?

The Journal's reporting suggested friction points. One likely issue: how the money would be structured and controlled. NVIDIA wanted assurances about how the infrastructure would be managed and utilized. Open AI presumably wanted independence in how they'd use the resources. These are the kinds of governance questions that can torpedo deals.

Another factor that probably mattered: NVIDIA's stock had already priced in massive future growth. Committing $100 billion to a single customer, even one as prestigious as Open AI, might have looked like overconcentration of risk. What if Open AI's business model changed? What if their AI models became less useful? What if they couldn't efficiently deploy that much capacity?

The timing also mattered. By January 2025, some of the hype around AI had cooled slightly. Companies were asking harder questions about ROI on AI projects. The narrative that AI would generate unlimited revenue growth was being questioned more seriously.

Huang's Response: Reading Between the Lines

When Huang spoke to reporters in Taipei and called the Wall Street Journal report "nonsense," he did three important things:

First, he reaffirmed that NVIDIA's commitment to Open AI was genuine. "I believe in Open AI. The work that they do is incredible. They're one of the most consequential companies of our time." That's not the kind of language you use if you're backing away from a deal. He was defending both Open AI and the partnership.

Second, he clarified the financial commitment. According to CNBC's reporting, Huang said this funding round wouldn't come anywhere near the full

Third, by pushing back publicly, Huang was managing expectations and resetting the narrative. He was signaling that this deal is still alive, still moving forward, but on a different timeline and with a different structure than initially announced.

This is actually smart negotiating. The initial announcement created momentum and press coverage. But it also created inflated expectations. By saying the investment wouldn't be $100 billion in this round, Huang was essentially resetting the valuation conversation while keeping the deal intact.

What Likely Happened Behind Closed Doors

We don't have perfect visibility into negotiations, but based on how these deals typically work, here's probably what went down:

Both sides agreed the partnership made sense. But once they got into the weeds, reality set in. NVIDIA probably wanted more control over how the infrastructure would be built and who would operate it. Open AI wanted to build something they owned and controlled, not something where NVIDIA had leverage over their operations.

The "discipline" comment Huang allegedly made hints at this. He might have felt Open AI was too focused on pushing cutting-edge capabilities and not focused enough on operational efficiency and financial sustainability. From NVIDIA's perspective, if they're investing $100 billion, they want to know their customer won't just blow through it on R&D without generating returns.

Open AI's perspective is different. They're not a traditional utility company that needs to show quarterly profits on every project. They're a research organization with commercial operations. They might feel that short-term financial discipline would compromise their ability to do breakthrough work.

So they probably went back to the drawing board. The new structure likely involves:

• Staged investment phases instead of a lump sum, with performance gates and reviews
• Clearer governance on how decisions get made about infrastructure deployment
• More defined success metrics for what success looks like
• Potential involvement of other partners or investors to reduce NVIDIA's risk concentration
• Revised timeline with the first phase being smaller and more near-term

None of this kills the deal. It actually makes it more likely to succeed. Thoughtful negotiations beat rushed deals every time.

The Economics: Why the Numbers Matter

Let's dig into what these numbers actually mean in real-world terms.

10 gigawatts of capacity is genuinely massive. To put it in perspective, a large power plant generates around 1 gigawatt. So this is equivalent to ten large power plants dedicated entirely to AI infrastructure.

The cost structure matters. A typical AI data center costs around

This means the

Here's where it gets interesting though. The chips themselves are the constraining factor. If NVIDIA provides $100 billion worth of chips, that's the real bottleneck. The rest of the infrastructure is expensive but solvable with traditional financing.

For NVIDIA,

For Open AI, obtaining

The Competitive Dynamics: Why Everyone's Watching

This deal matters way beyond just NVIDIA and Open AI. It affects the entire AI landscape.

Google is watching closely. They build their own AI chips (the TPU line) and operate their own data centers. An NVIDIA-Open AI mega-infrastructure essentially makes Open AI's models potentially more capable and cheaper to run than Google's own. That's not good news for Google's market position.

Meta is watching too. They're huge NVIDIA customers, buying chips for their AI research and LLa MA model development. If Open AI gets preferred pricing or priority access through this deal, Meta might find themselves squeezed out of critical chip inventory.

AMD has to be watching this with particular anxiety. They're trying to break into the AI chip market with their MI300 series. But if NVIDIA locks Open AI into a multi-year exclusive or preferred-vendor relationship, AMD's chances of gaining market share just got harder.

China's watching from a geopolitical perspective. A $100 billion commitment to AI infrastructure in the US means the US is investing heavily in maintaining its lead in AI capability. There are immediate strategic implications for semiconductor export controls, AI regulation, and the broader tech competition between nations.

And startups? They're watching because this shapes what infrastructure will be available and at what cost. If the best chips go to Open AI and other mega-players, smaller AI companies get pushed further down the queue. This could actually accelerate consolidation in the AI space.

What Happens to Open AI's Business Model

Here's something that doesn't get enough attention: this infrastructure buildout fundamentally changes how Open AI makes money.

Currently, Open AI's primary revenue comes from API access to their models. Developers pay per token. It's a software-like model with high margins once infrastructure costs are covered.

But if Open AI builds dedicated infrastructure through this deal, they're becoming a cloud infrastructure provider, not just an AI model company. That's a different business entirely.

Cloud infrastructure is lower margin and higher complexity. You have to manage capacity, customer relationships, service level agreements, technical support. It's not as scalable as pure software.

On the flip side, controlling your own infrastructure gives you enormous advantages. You're not beholden to cloud providers' pricing. You can optimize the hardware for your specific use cases. You can offer better prices to customers because you have lower costs. You can improve margins by running the infrastructure at higher utilization rates.

So Open AI is essentially taking a bet that becoming vertically integrated—owning the infrastructure from chips to applications—is better than remaining a pure software company. This is a strategic shift, not just an infrastructure upgrade.

The Timing: Why 2026 Matters

The original target date of the second half of 2026 for phase one to go live is significant.

That's roughly 18-20 months away. In tech, that's not that far out. Major infrastructure projects typically take 3-5 years from conception to operation. Getting the first phase live in 18 months would be aggressive.

What this probably means is that phase one is relatively modest in scale. Maybe 0.5-1 gigawatt of initial capacity, located at a single site or cluster of nearby sites. This allows for rapid deployment and learning.

Phases two and three would then scale out from there, deploying the remaining capacity across multiple sites and geographies. This staged approach actually makes sense because it allows both companies to test the partnership operationally before scaling aggressively.

2026 is also interesting from a competitive perspective. By then, we'll probably be three generations deep into large language models. GPT-5 will likely exist. New competitors might have emerged. The architecture of AI models might have shifted.

So Open AI is essentially saying: "We need cutting-edge infrastructure ready by 2026 because that's when the really expensive phase of AI compute begins." That's a vote of confidence in continued AI scaling.

The Technical Challenge: Building at This Scale

Building 10 gigawatts of AI capacity is not just about ordering GPUs and plugging them in. The technical challenges are immense.

Power delivery is the first issue. Getting 10 gigawatts of electricity to data centers reliably requires upgrades to electrical grids. You might need new power plants, transmission lines, and substations. Most current data center sites can't support this kind of power density.

Cooling is the second issue. GPUs generate enormous amounts of heat. Cooling costs can be 30-40% of total operating expenses. At 10 gigawatts, you're generating about 10,000 megawatts of heat (chips are maybe 50-60% efficient). That's equivalent to a small city's worth of thermal energy that needs to go somewhere. You'd need water resources, advanced cooling systems, maybe even novel cooling technologies like immersion cooling.

Networking is the third issue. Connecting thousands of GPUs so they can work together as a single coherent system requires ultra-low-latency networking. Typical internet latency is measured in milliseconds. You need microseconds. This requires custom networking hardware, often with optical interconnects, and very careful topology design.

Reliability is the fourth issue. At this scale, hardware failures aren't rare—they're constant. You might have multiple GPUs failing every day across 10 gigawatts of capacity. You need systems to automatically route work around failures, replace hardware on the fly, and maintain service continuity. This is harder than it sounds.

Land is the fifth issue. You need physical space for all this equipment. 10 gigawatts might require 200,000+ square feet of facility space (accounting for cooling, power infrastructure, support systems). Finding suitable locations with adequate land, power, water, and regulatory approval is a years-long process.

All of this explains why getting phase one operational by late 2026 is legitimately ambitious. It's technically possible, but it requires everything to go right.

Market Implications: Chip Pricing and Supply

If this deal goes through, it'll have immediate market impacts on GPU pricing and availability.

Currently, NVIDIA's chips are scarce and expensive. Customers have to wait months for orders. Demand vastly exceeds supply. This is great for NVIDIA's margins—they can charge premium prices because they're the only supplier with cutting-edge products.

But if $100 billion worth of chips are locked into this Open AI deal, that's less chips available for everyone else. Competitors will find it even harder to get allocation. Startups and smaller companies will be further squeezed. Prices might actually go up because supply gets even tighter.

NVIDIA's growth, paradoxically, might be limited by demand constraints rather than production constraints. They can't grow faster than they can manufacture chips. And if most new production goes to a few mega-deals like Open AI, the addressable market for other customers shrinks.

AMD and other competitors might actually benefit from this in the long term. If NVIDIA's chips become even more scarce and expensive, customers desperate for alternative solutions might finally adopt AMD at scale. But in the short term, this deal just increases NVIDIA's market dominance.

Risk Factors: What Could Still Go Wrong

Despite Huang's reaffirmations, there are legitimate risks that could still derail this:

Regulatory Risk: Governments increasingly scrutinize major AI infrastructure projects, especially those involving foreign nationals or strategic technology transfer. US regulators might require security reviews. Chinese regulators might have concerns about who controls the infrastructure and how it's used.

Business Model Risk: Open AI's path to profitability remains unclear. If they build this massive infrastructure and can't generate sufficient revenue to justify it, the partnership could strain. Investors in Open AI might balk at the infrastructure bet.

Technology Risk: What if there's a breakthrough in AI chip efficiency that makes current chips obsolete before they're even deployed? What if a better AI architecture doesn't need massive data center compute? These seem unlikely but aren't impossible.

Geopolitical Risk: US-China tensions, especially around semiconductors, could force policy changes that affect where infrastructure can be built or who can access it. Export controls could change. National security reviews could delay deployment.

Market Risk: What if AI demand doesn't grow as expected? What if enterprises adopt smaller, more specialized AI models instead of massive foundation models? Open AI's bet on scale could be wrong.

Alternative Scenarios: How This Could Play Out

Scenario One: The deal gets restructured significantly. NVIDIA commits to providing $25-30 billion in chips over the first phase (through 2026). Open AI raises separate funding for infrastructure buildout. Phase one goes live in late 2026 with 1-2 gigawatts of capacity. Both companies claim victory. More phases might follow, or they might not.

Scenario Two: The deal stalls longer. Negotiations drag through 2025. By the time both sides agree on terms, a year has passed. First deployment gets pushed to 2027 or 2028. This reduces the headline impact but doesn't kill the partnership.

Scenario Three: A third party gets involved. Microsoft, Google, or a new investor joins to help structure the financing and reduce NVIDIA's risk. This keeps the deal alive but changes its character—it becomes less of an exclusive partnership and more of a consortium approach.

Scenario Four: The deal gets restructured into something different. Instead of direct investment, it becomes a chip supply agreement with other investors funding infrastructure. NVIDIA supplies chips, someone else raises infrastructure capital. This is probably more realistic than the original "we'll invest $100B" announcement.

What This Means for the AI Ecosystem

If this deal ultimately succeeds, even in restructured form, it has ripple effects:

For startups: The infrastructure is more expensive and consolidated than ever. Startups building new AI models will struggle to get affordable compute. This could push them toward smaller models and specialized AI rather than competing on scale. It might actually slow innovation because capital is a bigger constraint.

For enterprises: Access to cutting-edge AI models might come through Open AI's infrastructure rather than through cloud providers. This could change how enterprises deploy AI. Microsoft, which is heavily invested in Open AI, might gain advantages in enterprise AI.

For open source: With so much money flowing to proprietary AI infrastructure, open source AI models might lag in capability because they don't have equivalent compute resources. This could centralize AI power in a few hands.

For developers: If Open AI has unlimited compute, they can run more training experiments, build bigger models, and push the frontier faster than competitors. The gap between Open AI and other AI labs could widen.

For NVIDIA: Success on this deal validates their strategy of being the enabling layer for AI. It might lock them into dominance for the next 5-10 years. Failure or significant restructuring might open the door for competitors.

The Broader Context: NVIDIA's Strategic Position

Why would NVIDIA make this investment when they're already selling every chip they produce?

The answer is about defending their future. NVIDIA's dominant position in AI chips is not guaranteed to last forever.

Google's developing better TPUs optimized for their workloads. AMD's catching up with better chips. Intel's trying hard. There's potential that in 5 years, custom ASICs (application-specific integrated circuits) become viable and the market fragments.

By investing $100 billion in Open AI, NVIDIA is essentially guaranteeing demand. They're also building a relationship with one of the most important AI companies. When Open AI's engineers encounter chip limitations, NVIDIA gets a seat at the table to help solve it.

It's also a financial play. NVIDIA's stock is expensive. Their PE ratio is high. Their growth rate, while impressive, needs to be sustained for the valuation to make sense. Committing to a massive $100 billion customer commitment helps prove that growth is sustainable.

Finally, it's about optionality. By partnering deeply with Open AI on infrastructure, NVIDIA gets insights into where AI is heading. They learn about challenges early. They can adjust their product roadmap based on real-world infrastructure experience.

The Financial Models: Does the Math Work?

Let's do some rough math on whether this makes financial sense for both parties.

For NVIDIA:

At 70% gross margins (typical for NVIDIA's high-end chips), that's roughly

For Open AI: Getting

The payoff for Open AI is compute capacity that lets them build bigger models, serve more customers, and potentially become significantly more profitable. If they can generate $50 billion in annual revenue from operating this infrastructure (a big if), then the ROI over 5 years is excellent.

Both sides' math seems to work. Which is probably why, despite negotiations being contentious, the deal is still alive.

What Happens If This Succeeds

If NVIDIA and Open AI actually build 10 gigawatts of capacity and deploy it successfully, the trajectory of AI changes.

Open AI becomes not just an AI model company, but the largest AI infrastructure provider in the world. They'd have the most valuable computational asset on the planet. Every other AI company would need to decide: rent compute from Open AI, or build competing infrastructure?

NVIDIA becomes more entrenched. They're not just selling chips—they're partnering with the customer to shape entire systems. Their influence extends beyond just hardware into infrastructure design and operations.

The AI market could actually slow down. With massive infrastructure advantages, it's harder for new competitors to emerge. The competitive dynamics shift toward entrenched players who own infrastructure.

Or it could accelerate. More compute available means more ambitious projects become possible. The models built on this infrastructure could be significantly more capable, pushing the entire field forward.

The most likely outcome is something in between. This infrastructure enables bigger, better models. But it also raises barriers to entry for new competitors. The AI field matures from "we're building cool demos" to "we're building critical infrastructure that society depends on."

What Huang's Comments Actually Mean

When Huang said this could be "the largest investment we've ever made," what did he actually mean?

It's important not to read this as a done deal. It's a reaffirmation of intent, but with important caveats. He said it could be the largest investment, not will be. He said this funding round wouldn't be the full $100 billion. He's managing expectations while keeping enthusiasm alive.

What he's really saying is: "We're serious about this. We believe in Open AI. But we're also being cautious and negotiating carefully."

That's actually the right approach. A $100 billion bet on a single customer is huge. Smart negotiating means taking time to get the terms right, even if it delays announcement of the deal closing.

For investors in NVIDIA, this is positive but not a slam dunk. It shows demand confidence. But it also shows that the original deal structure is being rethought, which might mean reduced commitments.

For Open AI stakeholders, it's positive that NVIDIA remains committed. But the fact that negotiations are drawn out suggests some real friction that will take time to resolve.

Looking Ahead: Timeline and Next Milestones

Based on typical venture and strategic investment timelines, here's probably what we'll see:

Next 6 months (Spring 2025): Detailed negotiations continue. NVIDIA and Open AI work through governance, staged investment terms, technical specifications, and operational responsibilities. You might see smaller announcements about partnerships with cloud providers or infrastructure vendors.

6-12 months out (Summer-Fall 2025): Expect a formal announcement of revised deal terms. This would include updated investment amounts, revised timelines, and governance details. It might not be as flashy as the original $100 billion announcement, but it'll reconfirm commitment.

12-18 months out (Late 2025-Early 2026): Infrastructure construction begins. You'll hear about site selections, power company partnerships, construction contracts. Real-world implementation starts replacing corporate announcements.

18-24 months out (Mid-2026): First phase nears completion. Testing and optimization happen. You'll see technical announcements about infrastructure capabilities, networking speeds, cooling efficiency.

2+ years out (Late 2026 onwards): First phase goes live. This is when the real test happens—can this infrastructure actually deliver on the promises? Do models built on it work as expected? This is when theory becomes reality.

Each of these milestones is a decision point. If things go wrong—costs balloon, technology disappoints, regulatory issues arise—you might see the deal restructure again or scale down.

The Bigger Picture: What This Signals About AI's Future

Beyond just NVIDIA and Open AI, this deal tells us something important about where AI is heading.

The fact that two companies are willing to commit $100 billion to infrastructure signals that the market believes AI scaling will continue for many years. If they thought AI was reaching diminishing returns or hitting plateaus, they wouldn't be committing this much capital.

It also signals that AI infrastructure is becoming a core competitive advantage. No longer is it enough to have good algorithms. You need the massive compute to train and run them. This shift toward infrastructure-as-competitive-advantage changes the industry.

Finally, it signals that compute is still the bottleneck. If it weren't, NVIDIA wouldn't be needed. But compute remains so scarce and expensive that two major companies are willing to make massive capital commitments just to ensure access. That tells you everything about where we are in AI development.

Practical Implications for Developers and Teams

What does this mean if you're building AI applications or making infrastructure decisions?

Short term (next 12-18 months), probably not much changes. Open AI's current pricing and availability don't shift immediately because this new infrastructure isn't live yet. You can still use Chat GPT API at existing prices.

Medium term (18-36 months), expect significant changes. Once the infrastructure is operational, Open AI could offer better pricing to customers. They might launch new product tiers optimized for the new infrastructure. Capabilities might improve as the models trained on this infrastructure launch.

Long term (3+ years), the competitive landscape shifts. AI infrastructure owned by a single company (even Open AI) is both a positive and negative. Positive: more focused innovation and better resource allocation. Negative: less competition and potential for higher prices once you're locked in.

For now, the practical advice is: don't make major infrastructure bets assuming this deal is locked in and deployed. Build your applications on APIs and assume pricing might change. Don't assume Open AI will indefinitely have compute advantage—the landscape moves fast.

Conclusion: A Deal Worth Watching

NVIDIA's investment in Open AI, whether it's the full $100 billion or a restructured version, represents a pivotal moment in AI infrastructure.

It signals that both companies see massive growth ahead. It demonstrates that compute remains the critical constraint in AI development. It shows that partnerships between chip makers and AI companies will shape the industry's future.

The negotiations aren't finished. The deal will likely look different from the original announcement. But the fundamental strategic logic—that NVIDIA and Open AI need each other and should invest in that relationship—remains sound.

What's remarkable is not that this deal is happening, but that it's happening on a scale previously unimaginable. $100 billion is not venture capital. It's not even traditional corporate spending. It's existential-level infrastructure commitment.

For the AI field, that commitment means the race to build bigger, better models will continue. The winners will be the companies that can marshal massive capital for infrastructure. The losers will be those left behind in the compute arms race.

Huang's confidence in Open AI and vice versa is justified by the math and the strategy. Whether the implementation succeeds at the announced scale remains to be seen. But the intent is clear. The future of AI belongs to whoever controls the infrastructure.

FAQ

What is the NVIDIA-Open AI investment deal?

In September 2024, NVIDIA and Open AI announced a partnership where NVIDIA would invest up to

How does the investment structure work?

The investment structure appears to be a multi-phase deployment rather than a single

Why did the deal negotiations stall?

According to Wall Street Journal reporting, the negotiations stalled over governance and operational concerns. CEO Huang allegedly criticized Open AI's business approach as lacking discipline, suggesting disagreements over how infrastructure would be managed and funded. NVIDIA wanted assurances about how its $100 billion investment would be utilized and what returns could be expected. These are normal negotiation friction points in mega-deals—they don't necessarily indicate the partnership will fail, just that both sides need time to align on terms and expectations.

What does 10 gigawatts of AI data center capacity actually mean?

Ten gigawatts represents roughly the power consumption equivalent of ten large power plants dedicated entirely to AI infrastructure. This would require substantial electrical grid upgrades, advanced cooling systems for GPU heat dissipation, ultra-low-latency networking infrastructure, and massive physical facilities. The full buildout would likely cost

How does this affect other AI companies and startups?

If the deal succeeds, it concentrates computational resources in Open AI's hands, making it harder for competitors and startups to access cutting-edge chips and infrastructure. This could reduce innovation from new entrants while enabling Open AI to push AI capabilities faster. Competitors like Google, Meta, and Amazon would need to invest heavily in their own chip development and infrastructure to remain competitive. Smaller AI companies and startups would face even higher barriers to entry without access to equivalent compute resources.

When will this infrastructure actually be operational?

The original timeline targeted the second half of 2026 for the first phase to go live. However, given that negotiations have taken longer than expected, this timeline might slip. Realistically, getting the first gigawatt or two of capacity operational by late 2026 or 2027 seems most likely. Full 10-gigawatt deployment would take several years of ongoing rollout after the initial phases, probably stretching into 2028-2030.

Is this deal actually happening or is it dead?

As of Huang's recent comments, the deal is alive but being renegotiated. The original structure is being revised, likely into a phased approach with smaller commitments per phase and clearer performance milestones. Don't expect a dramatic announcement that "the deal is off." Instead, expect a quieter revision to terms, followed by steady progress on infrastructure deployment. The fundamental strategic logic for both companies remains sound, which is why neither side has walked away.

What happens to GPU prices if this deal closes?

Paradoxically, consumer and smaller-business GPU prices might go up, not down. With $100 billion worth of chips locked into the Open AI partnership, less supply is available for other customers. This tightens the overall market, potentially pushing prices higher for everyone else. However, if the partnership enables Open AI to offer AI services at lower cost due to infrastructure efficiency gains, end-user customers might benefit through cheaper API access even if hardware prices increase.

Could this deal be blocked by governments or regulators?

It's possible but unlikely. The US would likely support it as proof of domestic AI infrastructure dominance. However, there could be national security reviews, particularly around technology transfer or foreign involvement. Geopolitically, the fact that this concentrates massive AI infrastructure with a US company might accelerate competition from China and Europe to build equivalent capabilities. Regulatory risk exists, but it's more about how governments structure oversight rather than outright blocks.

What's the biggest risk to this deal actually happening?

The biggest risks are probably operational and technical rather than financial. Executing a project of this scale and complexity is genuinely difficult. Power grid upgrades, site construction, cooling systems, networking infrastructure—any of these could face delays or unexpected costs. There's also market risk: if AI models plateau in capability or demand softens, the case for massive infrastructure investment weakens. Finally, competitive risk exists if a technical breakthrough (like more efficient chips or new architectures) renders this infrastructure less valuable than anticipated.

Key Takeaways

NVIDIA and Open AI's $100 billion partnership represents an unprecedented commitment to AI infrastructure, though negotiations are more complex than initial announcements suggested. The deal likely involves staged investments rather than a single massive commitment, with the first phase targeting deployment by late 2026 or 2027. While negotiations have faced friction over governance and operational control, both companies' fundamental strategic interests remain aligned—NVIDIA needs guaranteed customers for its chips, and Open AI needs access to cutting-edge compute without massive cash outlays. If successful, this infrastructure will dramatically increase Open AI's capabilities and entrench NVIDIA's market dominance, while potentially raising barriers to entry for AI competitors and startups. The deal's success ultimately depends on execution across multiple technical, operational, and regulatory challenges over the next several years.