#dojo supercomputer

[the-ai-perspective]

Tesla's AI Supercomputers: Dojo and Cortex (link in the bio)

[libertyracer]

Our dojo needed a supercomputer to answer the question of life, universe and everything.

@kingofcaptura

@meleeprowess

[selfdrivings]

What are the top features of Tesla’s self driving technology?

Tesla Self Driving technology includes Navigate on Autopilot, Auto Lane Change, Autopark, Smart Summon, and Traffic Light & Stop Sign Control.

These features are powered by Tesla’s neural net and Dojo supercomputer, which allows the system to learn from real-world data and improve over time. It’s one of the most advanced semi-autonomous systems available today.

[wolfofstocks]

NVDL Stock Soars in 2025: What Investors Must Know Now

NVDL Stock Soars in 2025: What Investors Must Know Now

NVDL has become one of the hottest leveraged ETFs in 2025. This 2x leveraged fund is directly tied to Nvidia (NVDA) performance, making it a favorite for those seeking high short-term gains in the booming AI and semiconductor space. But is it too risky for your portfolio?

Let’s break down everything you need to know — the history, performance, risk, comparison, and future of NVDL.

What is NVDL?

NVDL is the ticker for the GraniteShares 2x Long NVDA Daily ETF. This fund aims to deliver twice the daily return of Nvidia’s stock. That means if Nvidia goes up by 3% today, NVDL is designed to go up by 6%. Similarly, losses are also magnified.

This ETF is not for long-term holding. It’s built for active traders and short-term investors who understand how leverage works.

NVDL Performance Overview

Here’s a quick look at NVDL’s performance as of June 2025:MetricValueYTD Performance (2025)+178.3%1-Year Return+320.5%VolatilityHighExpense Ratio0.99%Daily Volume1.3M+ sharesUnderlying AssetNvidia (NVDA)Leveraged Exposure2x (Daily Compounding)

Note: Because of daily compounding, long-term returns may vary greatly based on volatility.

Why is NVDL So Popular in 2025?

AI Boom: Nvidia is powering almost every major AI product, from ChatGPT to Tesla’s Dojo supercomputer. This has caused NVDA stock to surge.

High Returns: Traders looking for higher returns are flocking to NVDL instead of NVDA.

Retail Trader Favorite: Platforms like Robinhood and Webull show NVDL among the most-held ETFs by retail investors.

Short-Term Play: Its popularity stems from quick profits over short windows — hours, days, or a few weeks.

NVDL vs NVDA: Which is Better?

FactorNVDLNVDAType2x Leveraged ETFRegular StockIdeal ForShort-Term TradersLong-Term InvestorsVolatilityVery HighModerate to HighExpense Ratio0.99%NoneRiskHighMediumReward PotentialVery High (Short-Term)High (Long-Term)

Verdict: NVDL is for experienced traders. NVDA is safer for long-term investing.

Historical Trends and Key Events

Launched:��December 2022 by GraniteShares

Initial Reaction: It saw modest trading volume until Nvidia’s stock breakout in mid-2023.

2024 Surge: NVDA doubled, and NVDL rose more than 3x, gaining media attention.

2025 Growth: As of mid-2025, Nvidia’s dominance in AI and chips keeps NVDL trending upward.

Risks Associated with NVDL

NVDL is not for everyone. Let’s look at key risks:

Daily Reset: Returns are calculated daily. Holding it for longer can result in “decay,” especially in sideways markets.

High Volatility: A 5% drop in Nvidia could mean a 10% drop in NVDL in a single day.

Not for Passive Investing: This is not something to hold in a retirement or long-term growth portfolio.

Always remember: Leverage magnifies both gains and losses.

Expert Opinions

Morgan Stanley (2025 Q2 report): “Leveraged ETFs like NVDL are suitable only for disciplined investors with real-time tracking strategies.”

CNBC Commentator: “NVDL is like holding a lightning bolt. It’s powerful, but dangerous if you don’t know how to use it.”

Should You Buy NVDL?

If you’re:

Actively monitoring markets daily

Understand Nvidia’s movements

Have a stop-loss strategy

Comfortable with high risk

Then NVDL could be a strong tactical tool for short bursts of trading activity.

But if you’re:

A long-term investor

Risk-averse

New to trading

Then NVDA or a diversified tech ETF like QQQ might be safer bets.

Future Outlook for NVDL

NVDL’s future depends entirely on Nvidia’s stock performance. As AI continues to reshape industries, Nvidia is likely to remain a central player.

If Nvidia continues its growth in:

AI chips

Cloud computing partnerships

Automotive AI and robotics

Then NVDL could continue delivering big short-term returns. However, any downturn or correction in the tech market could trigger massive drawdowns in NVDL.

Final Thoughts

NVDL is one of the most exciting ETFs of 2025, but it’s also among the riskiest.

Use it smartly — as a short-term trading tool, not a core investment. Study Nvidia, set clear exit rules, and always track performance daily.

For casual or long-term investors, stick to NVDA or safer ETFs.

Disclaimer: The content provided in this article is for informational and educational purposes only and does not constitute financial, investment, or trading advice.

[generativeinai]

How Leading Companies Are Leveraging AI in Manufacturing to Future-Proof Their Operations and Outpace the Competition?

Manufacturing is undergoing a radical transformation—and at the heart of it lies Artificial Intelligence (AI). Global manufacturing giants, from Siemens and GE to Tesla and Foxconn, are reimagining their processes with AI to boost efficiency, lower costs, and stay resilient in the face of disruption. According to McKinsey, AI-enabled predictive maintenance alone could reduce machine downtime by 50% and increase equipment life by 20–40%.

In 2025, leading manufacturers aren’t just adopting AI—they’re weaving it into the fabric of their operations to automate decision-making, improve quality, and unlock new business models. This blog explores how top companies are leveraging AI to future-proof their operations, along with the real use cases, benefits, and technologies shaping the next generation of smart factories.

1. The Core Areas Where AI Is Revolutionizing Manufacturing

Predictive Maintenance

Traditional maintenance strategies—either reactive or scheduled—often lead to unplanned downtime or unnecessary service. AI changes this equation by using machine learning algorithms to predict failures before they occur. For example, Bosch uses AI to analyze sensor data from factory equipment and accurately forecast wear and tear, reducing maintenance costs and increasing machine uptime.

Quality Control and Defect Detection

Leading firms like BMW and Samsung use AI-driven computer vision systems to inspect products at scale. These systems identify defects invisible to the human eye with incredible speed and accuracy. Using deep learning models, manufacturers can detect flaws in real time on the assembly line and automatically reroute defective units for repair—saving millions annually in rework costs.

Supply Chain Optimization

AI helps companies gain end-to-end visibility in their supply chains. Algorithms can predict demand, identify bottlenecks, and optimize inventory levels. Schneider Electric, for instance, uses AI-powered demand forecasting to reduce excess inventory and improve delivery times—even during global disruptions.

Robotic Process Automation (RPA) for Routine Tasks

AI-driven RPA is transforming back-office and shop floor tasks alike. Whether it’s automating procurement processes or handling real-time adjustments on the production line, AI-powered bots streamline workflows and improve operational consistency. Foxconn uses collaborative robots (“cobots”) with AI sensors that adapt to human motion, increasing both safety and efficiency.

2. Real-World Examples of AI in Manufacturing Leadership

Tesla: AI-First Production Strategy

Tesla’s gigafactories are built around AI at every level—from machine learning algorithms that optimize battery assembly to neural networks that detect anomalies during vehicle inspection. Their Dojo supercomputer processes large-scale production data to train AI models, enabling Tesla to constantly improve production workflows.

Siemens: Industrial AI for Smart Factories

Siemens employs its Industrial Edge platform, combining AI and IoT to deliver real-time insights on production efficiency. By deploying AI models at the edge of networks, Siemens minimizes latency in decision-making—an essential factor in automated manufacturing environments.

General Electric (GE): Predictive Maintenance Pioneer

GE’s Predix platform uses AI and big data to monitor industrial equipment in real-time. Its AI-driven digital twins simulate equipment performance under different conditions, helping engineers optimize usage, prevent failures, and plan proactive maintenance across energy and aviation sectors.

3. AI Technologies Powering the Smart Manufacturing Revolution

Machine Learning and Deep Learning

From classification of defects to real-time optimization of robotic movements, machine learning models play a central role. Convolutional Neural Networks (CNNs) are used for visual inspection, while Reinforcement Learning (RL) helps optimize decision-making in dynamic factory environments.

Computer Vision

Computer vision is widely deployed for visual inspections, object recognition, and 3D mapping. It supports functions like real-time quality control, worker safety monitoring, and even autonomous material handling via AI-powered robots.

Natural Language Processing (NLP)

NLP allows AI systems to understand human commands, read documents, and automate communications. Manufacturing companies use chatbots for internal support, voice-controlled systems on the floor, and automated document processing in logistics and compliance.

Digital Twins

A digital twin is a virtual replica of a physical process or asset. AI-enhanced digital twins simulate production systems to detect inefficiencies or forecast outcomes. For example, Rolls-Royce uses digital twins to monitor and optimize jet engine manufacturing.

Industrial Internet of Things (IIoT)

IIoT devices collect vast amounts of real-time data from sensors across the factory. When paired with AI, this data becomes a goldmine for predictive analytics, real-time monitoring, and intelligent decision-making.

4. Benefits of AI-Driven Manufacturing

Increased Operational Efficiency

AI minimizes idle time, reduces manual intervention, and ensures consistent production quality. With AI, manufacturers can run 24/7 operations with fewer breakdowns and higher throughput.

Lower Operational Costs

Through energy optimization, efficient resource utilization, and predictive maintenance, AI significantly reduces overheads. Manufacturers also save costs related to defects, returns, and inventory mismanagement.

Enhanced Product Quality and Customization

AI enables real-time quality assurance, eliminating variations in product quality. It also supports mass customization by analyzing customer data and adjusting production parameters accordingly.

Agile and Resilient Supply Chains

AI helps navigate supply chain shocks by simulating disruptions and optimizing contingency plans. It also supports localized production strategies with real-time demand-supply alignment.

Data-Driven Innovation and New Revenue Models

Manufacturers can monetize their AI-driven data insights, offer outcome-based services, and create smarter products with embedded AI. This unlocks new business models and differentiates them from the competition.

5. Challenges and Considerations in AI Adoption

Data Quality and Integration

AI is only as good as the data it learns from. Manufacturers often struggle to integrate data across legacy systems, making AI model training and performance difficult.

Change Management and Workforce Readiness

There’s a skills gap in AI and digital technologies across the manufacturing workforce. Companies must invest in training programs and foster a culture of innovation to enable smooth AI integration.

Cybersecurity Risks

With more connected devices and real-time AI decision-making, cybersecurity risks increase. Protecting sensitive operational and customer data becomes critical.

High Initial Investment

AI implementation can be capital-intensive. Cloud infrastructure, sensor networks, and AI platforms require upfront investment, though long-term ROI tends to be substantial.

6. How to Get Started with AI in Manufacturing

Step 1: Identify High-Impact Use Cases Start with areas where AI can deliver quick wins—like predictive maintenance or quality inspection.

Step 2: Audit Data and Infrastructure Ensure you have clean, relevant data and the right digital infrastructure (cloud, edge computing, IIoT).

Step 3: Build or Partner for AI Expertise Either train your team or collaborate with AI solution providers to design and implement use-case-specific solutions.

Step 4: Pilot and Scale Run pilot projects and evaluate KPIs. Once validated, scale AI applications across your production and supply chain.

Step 5: Continuously Monitor and Improve Use real-time feedback loops to retrain AI models and adapt to new conditions or business goals.

Conclusion: AI Is Not Just a Tool—It’s the Future Operating System of Manufacturing

AI is no longer optional for manufacturers that want to stay relevant and competitive. It’s a strategic imperative that transforms how products are made, maintained, and delivered. As leading companies have shown, AI in manufacturing drives productivity, innovation, and resilience at scale. Whether it’s through computer vision, predictive analytics, or autonomous robots, AI is redefining what's possible in manufacturing.

Businesses that start today will lead tomorrow. But the longer you wait, the further behind you’ll fall in this AI-powered industrial revolution.

[nawapon17]

Tesla Dojo: Elon Musk's big plan to build an AI supercomputer, explained | TechCrunch

[dahlbrendan]

Tesla has a not-so-secret plan to replace a fundamental-but-flawed building block of the Internet — Musk's Exascale DOJO supercomputer will use TTPoE instead of the universal TCP and wants the world to embrace it | TechRadar

[isfeed]

WeRide preps for an IPO, meet the man who built a startup pipeline at CNH and Waymo’s nightly honk-a-thon

Welcome back to TechCrunch Mobility — your central hub for news and insights on the future of transportation. Sign up here for free — just click TechCrunch Mobility! We brought you a Tesla Dojo explainer last week. Now we’re giving you a handy Dojo timeline that tracks the progress of Tesla’s AI supercomputer. The timeline […] © 2024 TechCrunch. All rights reserved. For personal use only. Source:…

[y2fear]

What went wrong at Cruise, a pivot at Vroom and a home for Tesla's Dojo supercomputer

[eclectic-insights]

What went wrong at Cruise, a pivot at Vroom and a home for Tesla’s Dojo supercomputer http://dlvr.it/T20WdK

[worldmarketus]

Tesla’s Market Value Soars on Dojo Supercomputer Estimates $500 Billion

Tesla Inc. is a towering figure in the ever-changing landscape of electric vehicles (EVs), constantly pushing boundaries and redefining the automotive industry. Tesla recently took another giant step forward with its Dojo supercomputer, generating interest in both the tech and financial sectors. According to Morgan Stanley analysts, this innovative technology has the potential to increase Tesla’s market value by $600 billion. Let’s get into the specifics of this game-changing development.

Tesla’s Market Value Soars on Dojo Supercomputer Estimates $500 Billion

Dojo, Tesla’s cutting-edge supercomputer, went into production in July with the primary goal of speeding up the training of artificial intelligence (AI) models for self-driving cars. With over $1 billion earmarked for its development by next year, this supercomputer has the potential to be a game changer in the field of autonomous driving.

Dojo’s ability to process massive amounts of data at unprecedented speeds is one of its key advantages. This capability is expected to significantly improve Tesla’s self-driving technology, making it safer and more dependable. As a result, Tesla will be able to accelerate its entry into the worlds of robotaxis and software services.

Unlocking New Markets Beyond Traditional Vehicle Sales

Dojo’s true potential lies not only in self-driving cars but also in its ability to open up new markets that go far beyond vehicle sales. Analysts at Morgan Stanley envision a future in which Dojo enables any device equipped with a camera to make real-time decisions based on its visual field. This increase in addressable markets has the potential to reshape Tesla’s business model, emphasizing software and services rather than selling vehicles at fixed prices.

A Bullish Outlook

As a result of these developments, Morgan Stanley has upgraded Tesla’s stock from “equal-weight” to “overweight” and named it their “top pick,” replacing Ferrari’s US-listed shares. The Wall Street brokerage also increased its 12-month target price on Tesla stock by 60% to $400, the highest among all Wall Street brokerages. This goal is expected to boost Tesla’s market capitalization to around $1.39 trillion, a whopping 76% increase from its current market value of around $789 billion.

The Future of Tesla

Morgan Stanley analysts, led by Adam Jonas, believe Dojo will be the most valuable asset in Tesla’s software and services division. They also increased their revenue forecast for Tesla’s network services business to $335 billion in 2040, up from $157 billion previously. Furthermore, Jonas predicts that by 2040, this unit will contribute more than 60% of Tesla’s core earnings, nearly doubling its contribution in 2030.

This significant increase can be attributed to new opportunities in third-party fleet licensing as well as higher average monthly revenue per user (ARPU). As it expands its services beyond traditional vehicle sales, Tesla is poised to tap into these revenue streams.

A Market Leader with a Premium Valuation

Tesla’s 12-month forward price-to-earnings ratio of 57.9 outperforms legacy automakers such as Ford (6.31) and General Motors (4.56). This premium valuation reflects not only Tesla’s dominance in the electric vehicle market, but also its global potential to revolutionize transportation, software, and services.

To summarize, Tesla’s Dojo supercomputer marks a watershed moment in the company’s journey toward autonomy and beyond. Tesla’s market value could skyrocket as a result of its potential to transform not only the automotive industry but also a variety of other markets. Investors and enthusiasts alike are eagerly anticipating the future shaped by Dojo’s groundbreaking capabilities as the electric vehicle pioneer continues to break new ground.