Patrick

Ultra

Vera

Billion

<div>
 <h3 id="key-takeaways">Key takeaways</h3>
 <ul>
 <li>Jensen Huang says AI optimizes NVIDIA chip designs</li>
 <li>NVIDIA's gross margin reaches 75% despite rising costs</li>
 <li>Research spending jumps 64% amid intensifying semiconductor competition</li>
 </ul>
 NVIDIA CEO Jensen Huang says artificial intelligence has become indispensable to the company's chip development, with automated systems exploring new designs overnight to improve performance and energy efficiency.
 The remarks, originally made during a 2024 interview and circulating again, highlight an increasingly important question for investors: can AI-assisted engineering help NVIDIA protect its extraordinary profitability as AMD, custom silicon developers and major cloud companies advance competing technologies?&amp;lt;iframe src=”https://widgets.coincodex.com/w/1bddae19-b2e0-4e92-bd13-f9b739fd88fb?site=coinpaper&amp;amp;mode=light” width=”100%” height=”568” frameborder=”0” referrerpolicy=”no-referrer-when-downgrade” style=”border:0;background:transparent;border-radius:0px;”&amp;gt;&amp;lt;/iframe&amp;gt; 
 NVIDIA's latest financial results offer fresh context, with quarterly revenue reaching $96.2 billion and gross margins climbing to 75%, even as research expenses continue increasing.Jensen Huang Says NVIDIA Cannot Design Chips Without AI 
 During a <a href="https://stripe.com/se/sessions/2024/a-conversation-with-nvidias-jensen-huang" rel="noopener noreferrer nofollow">Stripe Sessions interview</a> with Patrick Collison, Huang explained that NVIDIA increasingly relies on artificial intelligence throughout its engineering process.
 These systems help optimize chip architecture, power consumption and performance while engineers concentrate on evaluating results and developing new technologies.
 Huang also described using AI to identify software bugs, analyze their likely causes and direct issues toward the engineers responsible for resolving them.
 The approach extends beyond automating repetitive tasks. By continuously examining alternative designs, AI could help NVIDIA discover more efficient configurations without proportionally increasing engineering labor.
 However, the company has not disclosed how much AI automation specifically reduces chip development costs or accelerates individual product launches.NVIDIA's 75% Gross Margin Faces a New Competitive Test 
 According to <a href="https://investor.nvidia.com/news/press-release-details/2026/NVIDIA-Announces-Financial-Results-for-Second-Quarter-Fiscal-2027/" rel="noopener noreferrer nofollow">NVIDIA's latest earnings release</a>, the company generated $96.2 billion in second-quarter fiscal 2027 revenue, representing 106% annual growth.
 Data Center revenue reached $89 billion, increasing 117% year over year, while GAAP gross margin improved from 72.4% to 75%.
 NVIDIA attributed the margin improvement primarily to a stronger product mix involving Blackwell Ultra rather than directly to AI-assisted engineering.
 Meanwhile, quarterly research and development expenses climbed from $4.29 billion to $7.05 billion, an increase of approximately 64%.
 That means NVIDIA is spending substantially more on innovation despite already incorporating artificial intelligence into its development processes.
 The company expects third-quarter gross margins of approximately 74%, suggesting some pressure compared with the latest reported quarter.
 The outlook comes as competitors expand AI accelerator development, an issue previously highlighted during the <a href="https://coinpaper.com/16978/nvda-stock-forecast-nvidia-hits-record-high-on-vera-rubin-ai-chips" rel="noopener noreferrer nofollow">Vera Rubin rollout</a>.Can AI-Designed Chips Protect NVIDIA Against AMD and Custom Silicon? 
 NVIDIA's challenge increasingly extends beyond delivering faster processors.
 Customers are also evaluating energy efficiency, memory capacity, networking performance and total operating costs as AI infrastructure spending expands.
 Rising memory prices have already contributed to higher <a href="https://coinpaper.com/34617/nvidia-ai-server-prices-rise-more-than-15-as-memory-costs-surge" rel="noopener noreferrer nofollow">AI server costs</a>, showing that chip design improvements cannot eliminate external supply-chain pressures.
 Meanwhile, AMD continues developing competing accelerators, while hyperscalers are investing in custom processors designed for particular AI workloads.
 These developments could eventually pressure NVIDIA's pricing power, even if its technology continues improving.
 AI-assisted engineering may help protect profitability by accelerating optimization, reducing unnecessary design iterations and improving performance per watt.
 Yet competitors can potentially adopt similar techniques, limiting the durability of that advantage.
 NVIDIA's <a href="https://coinpaper.com/34901/what-could-a-129b-hugging-face-deal-mean-for-nvidias-ai-strategy" rel="noopener noreferrer nofollow">expanding software ecosystem</a> may therefore matter as much as automated hardware design.
</div>

Jensen Huang Says NVIDIA Uses AI to Design Better Chips as Research Spending Jumps 64% to $7 Billion

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