Tesla (TSLA) is making big progress in testing driverless robotaxis on public roads and attracting attention from analysts and investors. The company started testing its self-driving cars in Austin, Texas, on December 15. No human safety monitor was on board. This was a milestone that Tesla’s leaders said would happen by year’s end. This shift represents a key part of the EV giant’s long‑term strategy for autonomous vehicles and future mobility services.
At the same time, Wall Street firms, including Morgan Stanley, are issuing forecasts about Tesla’s robotaxi plans and their potential impact on the company’s future. Analysts calculate the scale of robotaxi fleets and potential valuation effects over the next decade.
These changes have kept Tesla’s stock in the spotlight for investors and the market, even with challenges in electric vehicle sales growth.
Driverless Robotaxis Hit Austin Streets
Tesla (TSLA stock) began testing its self-driving cars on public roads in Austin, Texas. There were no human drivers or safety monitors in the front seats. CEO Elon Musk confirmed that fully driverless tests are happening. He sees this as an important step toward commercial operation.
Earlier in 2025, Tesla had already launched a limited robotaxi service in Austin using modified Model Y vehicles. Initially, these vehicles included a human safety monitor in the passenger seat to observe system performance.
Over the months, Tesla grew its service area and fleet size. By December 2025, reports showed about 31 active robotaxis operating in the city.
Recent tests without monitors show progress. However, they are still for internal validation, not for daily commercial use. Tesla confirmed that tests aren’t open to paying customers yet. The company hasn’t provided a specific date for when fully autonomous rides will be available to the public.
The Technology Behind Tesla’s Autonomous Effort
Tesla’s autonomous driving push relies on its Full Self‑Driving (FSD) software and onboard sensors. The FSD system can manage various driving situations. It uses cameras, radar inputs, and neural network processing. This differs from some competitors that rely on additional sensors such as LiDAR for redundancy.
In June 2025, Tesla shared its Q2 tech update. The company boosted AI training by adding tens of thousands of GPUs at its Gigafactory in Texas. This expansion supports improvements in FSD, where the company reported its first autonomous delivery. A Model Y drove itself without human help for 30 minutes.
Vehicles with FSD software need regulatory approval to drive on their own. In the Austin pilot, removing physical safety monitors marks progress toward that goal. Achieving fully reliable, unsupervised autonomy is still a challenge. This is true, especially when it comes to safety standards and different road conditions.
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Wall Street Eyes Tesla’s Robotaxi Potential, Sending Stock Near Record Highs
Tesla’s autonomous ambitions are closely watched by financial analysts. Morgan Stanley just shared forecasts that say Tesla could greatly grow its robotaxi presence in the next 10 years.
The bank says Tesla might have 1 million robotaxis on the road by 2035. These will operate in various cities as part of its autonomous fleet plan.
Morgan Stanley’s analysis sees active robotaxi units growing in 2026. However, the first fleets will be small compared to the long-term plan. The forecasts show the possible size of the autonomous vehicle market. They also highlight Tesla’s role in this growth. However, there are uncertainties tied to technology and regulations.
Stock markets have reacted to these developments. Tesla’s stock price nearly hit record highs. It rose almost 5% during trading sessions. Investors were excited about progress in driverless testing and the promise of future autonomous revenue. Analysts say Tesla’s value might go up more if its autonomous services and AI products perform well.

Tesla’s Vision for Autonomous Mobility Services
Tesla’s robotaxi initiative fits into its broader vision of mobility services and artificial intelligence (AI)‑driven transport. The company plans to launch purpose-built autonomous vehicles, like the Cybercab. These vehicles won’t have traditional controls, such as steering wheels or pedals. They aim for mass production in April 2026.
Tesla sees a future where owners can add their cars to a decentralized robotaxi network. This could boost fleet availability and usage. This strategy could shift parts of Tesla’s revenue profile away from vehicle sales toward recurring service revenues if adopted at scale. The global robotaxi market could reach over $45 billion in 2030, as shown below.

Analysts say that major technical, regulatory, and safety issues still stand in the way of robotaxis operating widely and making a profit. Building public trust, meeting varied local regulations, and demonstrating consistent safety across different road environments will be key factors in future deployment.
Tesla vs Competitors and Safety Regulations
Tesla is not alone in the autonomous vehicle race. Other companies, such as Alphabet’s Waymo, owned by Alphabet, have been operating fully autonomous services in multiple cities for several years and continue to expand.
The company operates about 2,500 robotaxis across multiple cities. Waymo has logged millions of paid autonomous rides and already meets higher autonomy standards in some regions. In comparison, Tesla operates around 31 robotaxis in Austin, with plans to expand to several major U.S. cities by 2026.

Tesla chose camera-centric sensors over multi-sensor arrays. This decision shows their focus on scalability and cost. Critics and some experts argue that adding LiDAR or other sensors could improve safety and performance under challenging conditions.
Regulators also play an important role. In some states, pilot autonomously driven services are permitted under special testing allowances. Widespread commercial use needs approval from both state and federal agencies. This ensures that vehicles meet safety and operational standards.
What’s Next for Tesla’s Driverless Fleets
Tesla’s move to test robotaxis without onboard safety monitors in Austin marks a clear technical milestone, though it is not yet a commercial service. The company’s next steps will likely focus on scaling test fleets, improving software robustness, and navigating regulatory approvals to allow expanded operations in other cities in 2026 and beyond.
Morgan Stanley and other analysts think robotaxis might play a big role in Tesla’s growth. They could boost service revenue as traditional vehicle sales slow down. However, forecasts at this stage remain based on long‑range assumptions about adoption, pricing, and regulatory landscapes.
Investor sentiment has been mixed. Stock movements show excitement about tech advances but also worry about short-term vehicle sales and profit pressures in the auto industry.
Overall, Tesla’s autonomous ambitions continue to shape its corporate strategy and public profile. The speed of robotaxi rollout, along with improvements in Full Self-Driving software and AI, will be key to seeing if the company can shift from an EV maker to a driverless mobility platform.
The post Tesla Tests Driverless Robotaxis in Austin While Analysts Predict 1 Million by 2035 Growth, Sending Stocks Up appeared first on Carbon Credits.
Carbon Footprint
AI Solutions from Microsoft and NVIDIA Power DOE’s Nuclear Energy Genesis Mission
The nuclear energy industry is entering a new phase of transformation. This shift is no longer just about building reactors—it is about building them faster, smarter, and more efficiently.
A recent breakthrough led by the U.S. Department of Energy (DOE), in collaboration with Idaho National Laboratory, Argonne National Laboratory, Microsoft, NVIDIA, Everstar, and Aalo Atomics, highlights that AI tools can streamline the nuclear regulatory process.
AI and DOE’s Genesis Mission: Breaking Bottlenecks in Nuclear Energy Deployment
The work supports President Trump’s Genesis Mission, a national initiative aimed at driving a new era of AI-accelerated innovation and discovery. The mission focuses on using advanced technologies like AI to solve critical national challenges, from energy to healthcare and beyond.
Under the Genesis Mission, DOE recently announced $293 million in competitive funding to tackle twenty-six pressing science and technology challenges, including one dedicated to speeding up nuclear energy deployment.
Rian Bahran, Deputy Assistant Secretary for Nuclear Reactors. said,
“Now is the time to move boldly on AI-accelerated nuclear energy deployment,” “This partnership, combined with the President’s orders, represents more than incremental ‘uplift’ improvements. It has the potential to transform how industry prepares its regulatory submissions and deploys nuclear energy while upholding the highest standards of safety and compliance.”
Simply put, from licensing to construction and operations, AI is now helping eliminate long-standing bottlenecks.
Faster Nuclear Licensing with Advanced Tools
The DOE’s recent announcement is a big step in modernizing nuclear regulation. Normally, preparing licensing documents for nuclear reactors is slow and complicated. It requires reviewing thousands of pages of technical data and making sure everything meets strict rules.
This shows how AI can make nuclear licensing faster and more accurate, helping advanced reactors reach the market sooner. Here’s how AI is simplifying this usually long and complex process.

Kevin Kong, CEO and Founder of Everstar, added:
“Nuclear is poised to solve today’s critical energy challenges,” said “We’re excited to partner with INL to meet the moment, working together to accelerate regulatory review and commercialization.”
Microsoft and NVIDIA Partnership: Building AI Infrastructure for Nuclear Energy
While the DOE demonstration focused on licensing, the broader transformation is being driven by a powerful collaboration between Microsoft and NVIDIA.
Together, they are developing a full-stack AI ecosystem designed specifically for nuclear energy. This platform combines cloud computing, simulation tools, and advanced AI models to streamline every phase of a nuclear project.
Key technologies in this ecosystem include:
- NVIDIA Omniverse for simulation and digital modeling
- NVIDIA CUDA-X and AI Enterprise for high-performance computing
- Microsoft Azure AI for data processing and automation
- Microsoft’s Generative AI tools for permitting and documentation
This integrated system enables developers to manage complex workflows in a unified environment. Instead of working with disconnected tools and datasets, teams can now operate within a single, AI-powered framework.
As a result, nuclear projects become more efficient, transparent, and predictable.
Carmen Krueger, Corporate Vice President, US Federal, Microsoft, further added:
“Our collaborations with DOE, INL, and across the industry are demonstrating how we can effectively bring secure, scalable AI technologies to solve key energy challenges and achieve the broader national and economic security goals envisioned by the Department’s Genesis Mission.”
Aalo Atomics: Cutting Permitting Time and Costs with AI
One of the most compelling real-world examples of AI impact comes from Aalo Atomics.
By leveraging Microsoft’s Generative AI for Permitting solution, Aalo has achieved dramatic improvements in project timelines. The company reported:
- A 92% reduction in permitting time
- Estimated annual savings of $80 million
These results show how AI can address one of the biggest challenges in nuclear development—delays caused by regulatory complexity.
Permitting often takes years and requires extensive documentation. However, AI can automate much of this work, allowing teams to focus on critical decision-making rather than repetitive tasks.
For Aalo, the value goes beyond speed. The technology also improves confidence in project execution by ensuring that all documentation is consistent, complete, and aligned with regulatory expectations.
This video demonstrated further details:
AI-Powered Nuclear Lifecycle: From Design to Operations
The impact of AI is not limited to licensing. It extends across the entire lifecycle of a nuclear plant. In the blog post, written by Darryl Willis, Corporate Vice President, Worldwide Energy and Resources Industry of Microsoft, explained how AI can help nuclear in a broader context.
- Design and Engineering Optimization: AI and digital twins allow engineers to simulate reactor designs in real time. This enables faster iteration and better decision-making. Developers can reuse proven design patterns and instantly evaluate how changes affect performance, safety, and cost.
- Licensing and Permitting Automation: Generative AI handles document drafting, data integration, and gap analysis. It ensures that applications are complete and consistent, reducing delays during regulatory review. This allows experts to focus on safety assessments instead of administrative tasks.
- Construction and Project Delivery: Advanced simulations now include time and cost dimensions. These 4D and 5D models allow developers to track progress, predict delays, and avoid costly rework. AI also enables real-time monitoring, ensuring that construction stays on schedule and within budget.
- Predictive maintenance and Plant Performance: Once a plant is operational, AI continues to add value. Predictive maintenance systems can detect issues early, reducing downtime and improving reliability. Digital twins provide continuous insights into plant performance, helping operators maintain optimal efficiency.
The post AI Solutions from Microsoft and NVIDIA Power DOE’s Nuclear Energy Genesis Mission appeared first on Carbon Credits.
Carbon Footprint
$10 Trillion in Carbon Cost? How U.S. Emissions Hit the Global Economy
Climate change is not only a physical threat, but it also affects the world’s economy. A major new study published in the journal Nature on March 25, 2026, puts a clear number on this impact. It finds that carbon dioxide (CO₂) emissions from the United States caused about $10.2 trillion in total economic damage worldwide between 1990 and 2020. This makes the U.S. the largest single contributor to climate-related economic loss over that period.
The study shows that emissions slow economic growth in many countries. Rising temperatures cut productivity, lower output, and hurt long-term economic performance around the globe.
Marshall Burke, the lead author of the study, remarked:
“If you warm people up a little bit, we see very clear historical evidence, you grow a little bit less quickly. If you accumulate those effects over 30 years, you just get a really large change by the end of 30 years. It’s like death by a thousand cuts. And you have people being harmed who did not cause the problem, and that feels just fundamentally unfair.”
The researchers focused on carbon dioxide, the most common greenhouse gas. They used data on how temperature affects economic activity and then linked that to how much CO₂ different countries have emitted since 1990. This method links climate science to real economic results, including slower growth, lower productivity, and smaller national outputs.
Counting the Dollars: $10 Trillion in U.S.-Linked Damage
One of the study’s central findings is striking. From 1990 to 2020, U.S. emissions likely caused around $10.2 trillion in global economic damage. This means that warming linked to U.S. emissions has reduced economic production across many countries. The study links these impacts to heat’s long-term effects on labor, agriculture, and overall economic growth.
The damage is not confined to other nations. Roughly 30% of that $10.2 trillion figure is estimated to have occurred within the United States itself. In other words, U.S. emissions have slowed economic growth at home as well as abroad. The remaining impacts are spread across the global economy.
The researchers found that U.S. emissions led to about $500 billion in damage in India and around $330 billion in Brazil during that time. These figures show how carbon released in one area can affect economies far away.

A New Framework for Loss and Damage
The Nature study introduces a new framework for assessing what scientists call “loss and damage.” This term refers to harms that cannot be prevented by reducing emissions or avoided through adaptation alone.
The study uses economic data and climate models. It tracks how temperature changes over the years impact economic output.
- To put the numbers into context: one tonne of CO₂ emitted in 1990 is estimated to have caused about $180 in global economic damages by 2020.
But that same tonne is projected to cause an additional $1,840 of cumulative damage by 2100, as warming continues and its effects compound over time. This highlights that past emissions still contribute to future economic harm.
The researchers highlight that these estimates focus on economic output, like goods and services. They do not account for all types of climate damage. They do not include costs from loss of life, health impacts, biodiversity collapse, cultural heritage losses, or many kinds of infrastructure damage. These excluded impacts could raise the true total cost of climate change even further.
The Social Cost of Carbon Revisited
This study is part of a broader scientific effort to understand the economic impacts of climate change. Climate and economic models show that rising temperatures are already slowing economic growth. If emissions stay high, this slowdown will get worse in the future.
Analyses by major international institutions and research groups project that climate change could reduce global GDP by a significant percentage by mid-century. This is compared to scenarios with strong mitigation, though exact figures vary by method.
The concept of estimating a “social cost of carbon” (SCC) — a monetary estimate of economic damage per tonne of CO₂ — has been used in policy analysis for years. It helps governments weigh trade-offs in climate policy. For example, they can decide how much to invest in emissions cuts versus adaptation.

However, traditional SCC estimates have been debated. They depend on assumptions about future growth, discount rates, and climate sensitivity. The Nature study advances this approach by tying economic outcomes directly to observed climate impacts.
Economists and climate scientists agree that warming impacts several areas. These include agricultural yields, labor productivity, energy demand, and health outcomes. These effects reduce economic output and increase costs for businesses and governments. The latest research makes these links more explicit by assigning dollar values to the historical impacts of emissions.
Equity and Global Responsibility
The research’s results also highlight important equity questions. Low-income countries often face bigger economic impacts compared to their emissions histories.
For example, nations with warmer climates and more fragile infrastructure may experience greater output losses due to temperature increases. These effects grow over time and can worsen existing development challenges.
At the same time, richer countries with higher historical emissions may take a larger share of responsibility for damage. The Nature study shows it is possible to calculate responsibility in monetary terms. However, turning those numbers into legal or financial obligations is still complex.
Tail Risks and Future Costs
The researchers also point toward the future. It finds that future damages from past emissions are much larger than the losses already accrued.
Since CO₂ remains in the atmosphere for centuries, its warming effects — and the economic damages linked to them — will persist well beyond 2020. This “tail risk” means that the total cost of historical emissions could rise sharply over the rest of this century.
Climate risk is increasingly integrated into economic planning and finance. Governments, businesses, and international institutions are incorporating climate scenarios into investment decisions and risk models.
This includes assessing how rising temperatures may affect infrastructure costs, insurance markets, supply chains, and national budgets. Without strong mitigation and adaptation measures, these economic pressures are expected to grow.
A Shared Reality, Quantified
The Nature study offers a clear and data-based way to think about the economic harms of climate change. Emissions from the United States since 1990 have caused over $10 trillion in global economic damage. This includes harm in the U.S., India, and Brazil.
These findings do not assign legal liability. However, they provide a meaningful picture of how climate change affects the global economy in terms of the social costs of carbon. They show that the costs of climate impacts are measurable and significant.
As the world continues to adapt and respond to climate change, understanding these economic links will be crucial for policymakers, businesses, and communities.
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Carbon Footprint
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