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AI weapons could increase risk of nuclear wars, says new study
A new study from the RAND Corporation suggests that the adoption of AI-powered weapons in the military could result in an increased risk of nuclear war. According to the study, the utilization of smart technologies could undermine valuable military conventions such as “mutual assured destruction.”
Back in the Cold War, the condition of mutual assured destruction between the United States and the Soviet Union ended up maintaining the peace, since it was understood that a first-strike attack would result in massive damages on the aggressor. Due to mutual assured destruction, countries with advanced militaries found very little incentive to take violent actions that could trigger a full-scale war.
With AI weapons in consideration, however, some nations might adopt a first-strike stance during conflicts to counter the advantages brought by artificial intelligence-powered defense systems. Thus, undermining the strategic stability provided by mutual assured destruction.
While the risks of a nuclear war could increase with the emergence of AI weapons, however, the RAND study also states that smart technologies can be used as a means to preserve strategic stability, at least in the long run, as noted in a Science Daily report. Andrew Lohn, one of the authors of the RAND Corporation study, explained this in a statement.
“Some experts fear that an increased reliance on artificial intelligence can lead to new types of catastrophic mistakes. There may be pressure to use AI before it is technologically mature, or it may be susceptible to adversarial subversion. Therefore, maintaining strategic stability in coming decades may prove extremely difficult, and all nuclear powers must participate in the cultivation of institutions to help limit nuclear risk,” he said.
While the idea of using bleeding-edge tech for the military might seem like a frightening idea, the fields of national defense and artificial intelligence actually have a long history together. According to Edward Geist, another researcher from the RAND study, AI in itself started with military efforts in mind.
“The connection between nuclear war and artificial intelligence is not new; in fact, the two have an intertwined history. Much of the early development of AI was done in support of military efforts or with military objectives in mind,” he said.
In a lot of ways, Geist’s statements do ring true. Earlier this month alone, a senior Pentagon official, undersecretary of defense for research and engineering Michael D. Griffin, encouraged the United States to explore emerging tech fields such as AI to ensure the country’s safety in the years to come. According to Griffin, future skirmishes between rival nations could happen through cyber attacks and AI-driven threats. Hence, the US would be wise to pursue the development of AI now, since the technology is still in its infancy.
Outside the United States, China has already expressed its assertive stance on AI. Just recently, one of the country’s AI startups, SenseTime, a company which creates surveillance tech, reached a valuation of $4.5 billion after a funding round led by e-commerce giant Alibaba. In South Korea, KAIST University — a DARPA award-winning school — recently found itself on the receiving end of a boycott from the AI community, after it was found that a number of its researchers were helping a local arms manufacturer develop AI-powered weapons.
Here’s a look at some of the US’ advanced military combat robots.
Elon Musk
Tesla confirmed HW3 can’t do Unsupervised FSD but there’s more to the story
Tesla confirmed HW3 vehicles cannot run unsupervised FSD, replacing its free upgrade promise with a discounted trade-in.
Tesla has officially confirmed that early vehicles with its Autopilot Hardware 3 (HW3) will not be capable of unsupervised Full Self-Driving, while extending a path forward for legacy owners through a discounted trade-in program. The announcement came by way of Elon Musk in today’s Tesla Q1 2026 earnings call.
🚨 Our LIVE updates on the Tesla Earnings Call will take place here in a thread 🧵
Follow along below: pic.twitter.com/hzJeBitzJU
— TESLARATI (@Teslarati) April 22, 2026
The history here matters. HW3 launched in April 2019, and Tesla sold Full Self-Driving packages to owners on the understanding that the hardware was sufficient for full autonomy. Some owners paid between $8,000 and $15,000 for FSD during that period. For years, as FSD’s AI models grew more demanding, HW3 vehicles fell progressively further behind, eventually landing on FSD v12.6 in January 2025 while AI4 vehicles moved to v13 and then v14. When Musk acknowledged in January 2025 that HW3 simply could not reach unsupervised operation, and alluded to a difficult hardware retrofit.
The near-term offering is more concrete. Tesla’s head of Autopilot Ashok Elluswamy confirmed on today’s call that a V14-lite will be coming to HW3 vehicles in late June, bringing all the V14 features currently running on AI4 hardware. That is a meaningful software update for owners who have been frozen at v12.6 for over a year, and it represents genuine effort to keep older hardware relevant. Unsupervised FSD for vehicles is now targeted for Q4 2026 at the earliest, with Musk describing it as a gradual, geography-limited rollout.
For HW3 owners, the over-the-air V14-lite update is welcomed, and the discounted trade-in path at least acknowledges an old obligation. What happens next with the trade-in pricing will define how this chapter ultimately gets written. If Tesla prices the hardware path fairly, acknowledges what early adopters are owed, and delivers V14-lite on the June timeline it committed to today, it has a real opportunity to convert one of the longest-running sore subjects among early adopters into a loyalty story.
Elon Musk
Tesla isn’t joking about building Optimus at an industrial scale: Here we go
Tesla’s Optimus factory in Texas targets 10 million robots yearly, with 5.2 million square feet under construction.
Tesla’s Q1 2026 Update Letter, released today, confirms that first generation Optimus production lines are now well underway at its Fremont, California factory, with a pilot line targeting one million robots per year to start. Of bigger note is a shared aerial image of a large piece of land adjacent to Gigafactory Texas, that Tesla has prominently labeled “Optimus factory site preparation.”
Permit documents show Tesla is seeking to add over 5.2 million square feet of new building space to the Giga Texas North Campus by the end of 2026, at an estimated construction investment of $5 billion to $10 billion. The longer term production target for that facility is 10 million Optimus units per year. Giga Texas already sits on 2,500 acres with over 10 million square feet of existing factory floor, and the North Campus expansion is being built to support multiple projects, including the dedicated Optimus factory, the Terafab chip fabrication facility (a joint Tesla/SpaceX/xAI venture), a Cybercab test track, road infrastructure, and supporting facilities.
Credit: TESLA
Texas makes strategic sense beyond the existing infrastructure. The state’s tax structure, lower labor costs relative to California, and the proximity to Tesla’s AI training cluster Cortex 1 and 2, both located at Giga Texas and now totaling over 230,000 H100 equivalent GPUs, means the Optimus software stack and the factory producing the hardware will share the same campus. Tesla’s Q1 report also confirmed completion of the AI5 chip tape out in April, the inference processor designed specifically to power Optimus units in the field.
As Teslarati reported, the Texas facility is intended to house Optimus V4 production at full scale. Musk told the World Economic Forum in January that Tesla plans to sell Optimus to the public by end of 2027 at a price between $20,000 and $30,000, stating, “I think everyone on earth is going to have one and want one.” He has previously pegged long term demand for general purpose humanoid robots at over 20 billion units globally, citing both consumer and industrial use cases.
Tesla (NASDAQ: TSLA) reported its earnings for the first quarter of 2026 on Wednesday afternoon. Here’s what the company reported compared to what Wall Street analysts expected.
The earnings results come after Tesla reported a miss on vehicle deliveries for the first quarter, delivering 358,023 vehicles and building 408,386 cars during the three-month span.
As Tesla transitions more toward AI and sees itself as less of a car company, expectations for deliveries will begin to become less of a central point in the consensus of how the quarter is perceived.
Nevertheless, Tesla is leaning on its strong foundation as a car company to carry forward its AI ambitions. The first quarter is a good ground layer for the rest of the year.
Tesla Q1 2026 Earnings Results
Tesla’s Earnings Results are as follows:
- Non-GAAP EPS – $0.41 Reported vs. $0.36 Expected
- Revenues – $22.387 billion vs. $22.35 billion Expected
- Free Cash Flow – $1.444 billion
- Profit – $4.72 billion
Tesla beat analyst expectations, so it will be interesting to see how the stock responds. IN the past, we’ve seen Tesla beat analyst expectations considerably, followed by a sharp drop in stock price.
On the same token, we’ve seen Tesla miss and the stock price go up the following trading session.
Tesla will hold its Q1 2026 Earnings Call in about 90 minutes at 5:30 p.m. on the East Coast. Remarks will be made by CEO Elon Musk and other executives, who will shed some light on the investor questions that we covered earlier this week.
You can stream it below. Additionally, we will be doing our Live Blog on X and Facebook.
Q1 2026 Earnings Call at 4:30pm CT https://t.co/pkYIaGJ32y
— Tesla (@Tesla) April 22, 2026
Tesla confirmed HW3 can’t do Unsupervised FSD but there’s more to the story
Tesla isn’t joking about building Optimus at an industrial scale: Here we go
