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# Nvidia's Own Analyst Says the AI King Is Losing Ground
- URL: https://wire.fourthweb.ai/nvidias-own-analyst-says-the-ai-king-is-losing-ground/
- Published: 2026-08-27T06:03:47.000Z
- Updated: 2026-08-27T06:03:48.000Z
- Description: The company that built the AI gold rush is now running uphill just to stay ahead of the crowd it created. Seaport analyst Jay Goldberg says Nvidia faces more downside risk than upside potential heading into Q2 earnings, with competition intensifying across the AI chip landscape
- Author: Travis Wright
- Tags: AI Agent Economy, AI Agents, AI Infrastructure, Compute Wars, Microsoft, Nvidia, IPO Watch, Funding Rounds, Big Tech

**The company that built the AI gold rush is now running uphill just to stay ahead of the crowd it created.**

### The Summary

- Seaport analyst Jay Goldberg says [Nvidia faces more downside risk than upside potential](https://www.bloomberg.com/news/videos/2026-08-26/getting-harder-for-nvidia-to-compete-seaport-s-goldberg-video?ref=wire.fourthweb.ai) heading into Q2 earnings, with competition intensifying across the AI chip landscape
- Despite expected strong results, Goldberg warns [the numbers aren't "impressive enough"](https://www.bloomberg.com/news/videos/2026-08-26/nvidia-results-not-impressive-enough-seaport-s-goldberg-video?ref=wire.fourthweb.ai) given the competitive pressure mounting from customers building their own chips
- Mizuho's Vijay Rakesh notes [investors will focus heavily on AI spending data and revenue outlook](https://www.bloomberg.com/news/videos/2026-08-26/nvidia-holders-to-focus-on-ai-data-mizuho-s-rakesh-says-video?ref=wire.fourthweb.ai), signaling the bar for beating expectations has moved beyond just hitting numbers

### The Signal

[Nvidia](https://wire.fourthweb.ai/tag/nvidia/) built the infrastructure for the agent economy. Now its biggest customers are its biggest competitive threat. [Seaport's Jay Goldberg warns](https://www.bloomberg.com/news/videos/2026-08-26/getting-harder-for-nvidia-to-compete-seaport-s-goldberg-video?ref=wire.fourthweb.ai) that "there's a lot more that can go wrong here than go right" as the company reports Q2 earnings. The risk isn't execution. It's structural.

The hyperscalers who bought Nvidia chips by the boatload are now designing their own silicon. Google has TPUs. Amazon has Trainium and Inferentia. [Microsoft](https://wire.fourthweb.ai/tag/microsoft/) is building Maia. Meta has MTIA. Every major AI player is building custom ASICs tailored to their specific workloads, optimized for inference costs that Nvidia's general-purpose architecture can't match.

> "The results aren't impressive enough given what's happening in the competitive landscape."

[Goldberg's assessment](https://www.bloomberg.com/news/videos/2026-08-26/nvidia-results-not-impressive-enough-seaport-s-goldberg-video?ref=wire.fourthweb.ai) cuts through the reverence. Nvidia can post another quarter of massive revenue and still be losing ground. The issue is margin compression and wallet share erosion. When your customers are also your competitors, growth rate matters less than where that growth is coming from. If the hyperscalers are buying fewer H100s because they're spinning up their own chips for inference workloads, Nvidia's training dominance becomes a narrower moat.

[Mizuho's Vijay Rakesh highlights](https://www.bloomberg.com/news/videos/2026-08-26/nvidia-holders-to-focus-on-ai-data-mizuho-s-rakesh-says-video?ref=wire.fourthweb.ai) what sophisticated investors are actually watching: AI spending trends and forward revenue guidance. The headline number doesn't matter if the guidance suggests softening demand or elongated replacement cycles. Nvidia's valuation prices in dominance. Anything less than continued acceleration reads as vulnerability.

Key competitive pressures:

- Custom ASICs from hyperscalers optimized for inference at lower cost per token
- AMD gaining traction in enterprise with MI300 series
- Startups like Cerebras and Groq targeting specific AI workloads with novel architectures

The agent economy runs on inference, not training. Training happens once. Inference happens millions of times per day, every time an agent takes an action. That's where cost matters. That's where custom chips win. Nvidia built the foundation, but the companies running massive agent workloads at scale have every incentive to bring chip design in-house.

### The Implication

If you're building in the agent space, watch Nvidia's guidance more than its earnings. Slowing demand or cautious outlook signals hyperscalers are getting serious about custom silicon, which means inference costs are about to drop. That's good for anyone deploying agents at scale. Cheaper [compute](https://wire.fourthweb.ai/tag/ai-infrastructure/) per action means more complex agents become economically viable sooner.

For Nvidia, the path forward depends on staying ahead in the training market and expanding into inference with Hopper and Blackwell chips optimized for serving models, not just building them. The company that enabled AI's Cambrian explosion now has to prove it can thrive in the commoditization phase that always follows a gold rush. The picks and shovels metaphor works until everyone starts making their own shovels.

### Sources

[Bloomberg Tech](https://www.bloomberg.com/news/videos/2026-08-26/nvidia-results-not-impressive-enough-seaport-s-goldberg-video?ref=wire.fourthweb.ai)