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Oracle (ORCL) Stock Soars 40% on AI Boom and $455B Cloud Backlog While Going Green

Published

2 days ago

September 11, 2025

Oracle Corporation (NASDAQ: ORCL) surprised the markets today with a dramatic stock rally. Its shares jumped more than 40%, reaching record highs and placing the company near the trillion-dollar club. This sharp increase was powered by huge demand for Oracle’s cloud services, especially for artificial intelligence (AI) and big partnerships.

Wall Street focused on the financial side, but Oracle also highlighted something else: its environmental goals. The company wants to show that fast growth can go hand in hand with sustainability. By investing in both AI and green programs, Oracle is shaping an image as a modern tech leader that balances profit with responsibility.

Record-Breaking Rally: Oracle’s Biggest Jump in Decades

The jump in Oracle’s stock was its largest in more than 30 years. Investors reacted to news that Oracle signed multiple multi-billion-dollar contracts with tech giants such as OpenAI, Meta, and NVIDIA.

These contracts are tied to AI cloud services and pushed Oracle’s contract backlog to around $455 billion, a sharp rise from $130 billion just a quarter earlier.

Oracle ORCL stock Sept 2025

This backlog shows how fast demand for Oracle Cloud Infrastructure (OCI) is growing. The company responded by raising its forecast for OCI revenue. It now expects 77% growth this fiscal year, higher than its earlier estimate of 70%. The company also predicts $18 billion in cloud revenue in 2025 and has set a long-term target of $144 billion by 2030.

The growth reflects the global rush to build AI systems. Oracle has placed itself at the center of this movement, partnering in major projects such as the Stargate initiative led by SoftBank and OpenAI. These deals highlight Oracle’s role in powering the next generation of AI.

Recent Developments Strengthening Oracle’s Position

On top of these strong results, Oracle has made headlines with two new announcements that underline its growing role in AI.

The first is a massive deal with OpenAI. Beginning in 2027, OpenAI will purchase at least $300 billion worth of computing power from Oracle over five years. This is one of the largest cloud agreements in history, and it shows how central Oracle has become to advanced AI systems. For Oracle, it marks a major vote of confidence from one of the most important AI companies in the world.

Oracle’s stock surged to a record high. This boosted the company’s market value to nearly $1 trillion. The rally also made headlines for another reason: it boosted co-founder Larry Ellison’s wealth by more than $100 billion in a single day, making him the world’s richest person.

Greener Growth: Oracle’s Path to Net Zero

Amid the AI excitement and stock rally, Oracle is pushing its green message. The company has promised to be carbon neutral by 2050. It also set a nearer goal to cut greenhouse gas emissions in half by 2030, using 2020 as its baseline year. These goals cover its offices, data centers, and cloud services.

Oracle 2025 sustainability goals — Source: Oracle

Oracle has already achieved some key milestones:

Renewable power: 86% of OCI’s global energy came from renewables in 2023.
Regional progress: Europe and Latin America already run on 100% renewable power.
Global ambition: Oracle plans to hit 100% renewable energy worldwide by 2025.
Water and waste: Since 2020, water use has dropped by almost 25% and landfill waste by more than 35%.
Travel impact: Employee air travel emissions have been cut by 38% thanks to more virtual meetings.

These achievements prove Oracle is not only talking about sustainability but also acting on it. For a company scaling up fast in cloud and AI, these steps are important. They show Oracle is trying to balance expansion with its responsibility to the planet.

Pushing Green Standards Across the Supply Chain

Oracle knows its environmental impact extends beyond its own walls. A big part of its footprint comes from suppliers. That’s why the company is pushing its partners to meet strict environmental standards.

Oracle energy and GHG emissions 2024 — Source: Oracle

Here are some of the key steps:

Supplier programs: All major suppliers must have environmental programs.
Emission targets: At least 80% of suppliers are expected to set formal climate goals.
Progress: More than four in five suppliers already meet these expectations.
Broader impact: By setting these standards, Oracle ensures its ESG efforts reach across its global supply chain.

This approach boosts Oracle’s credibility. It tells investors and clients that the company’s sustainability commitments are not limited to its own operations. Instead, they cover the full ecosystem of partners that make its technology possible.

AI-Powered Tools for Climate Accountability

Oracle is also building tools to help other companies meet their climate goals. One of these is Fusion Cloud Enterprise Performance Management (EPM) for ESG. This platform allows organizations to automate sustainability reporting, integrate emissions data with financial information, and align with global standards.

The system uses AI to make reporting easier and more accurate. This is important as regulators push companies to disclose their environmental impacts in more detail.

It combines Scope 1, 2, and 3 emissions data based on the GHG Protocol Corporate Standard. This links emissions to financial and operational data, helping with better ESG management.
Oracle improved its ESG reporting with this platform. They cut reporting timelines by 30% using automation and AI-driven process management.
The platform collects unique identifiers from source documents. This ensures clear data tracking and auditability. It boosts transparency and lowers compliance risks.
It supports global reporting standards like IFRS, ESRS (CSRD), and GRI. This helps organizations align their disclosures with changing regulations easily.

Oracle has also introduced features in its cloud infrastructure that estimate emissions from customer workloads. This means clients can see how much carbon their computing generates and adjust operations to stay on track with their own sustainability commitments. By doing this, Oracle is not only greening its own business but also helping others.

The Tough Road Ahead: Energy Demands vs. Climate Goals

Still, Oracle faces challenges in meeting its promises. Reaching 100% renewable energy worldwide is difficult, especially in regions where clean energy options are limited. Ensuring suppliers stick to emissions goals is also complex, given the size of Oracle’s global network.

Another challenge is the massive energy demand of AI. As Oracle expands its role in AI infrastructure, its energy use will rise. Balancing this growth with its climate goals will require new investment in efficient data centers, renewable sourcing, and innovations in green computing.

Oracle’s record-breaking stock surge highlights its importance in the AI and cloud industry. But what makes its story more powerful is the balance it is trying to strike between growth and sustainability. By pledging net zero emissions by 2050, setting ambitious near-term targets, and building tools for others to track emissions, Oracle is showing that technology and responsibility can go together.

For investors, Oracle now offers both a high-growth AI story and a strong ESG narrative. For customers, it provides powerful cloud services backed by renewable energy and transparent carbon data.

As Oracle continues to grow, its ability to deliver on both financial and environmental goals may define its future as one of the world’s most influential technology leaders.

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Albania Turns to Artificial Intelligence in EU-Pressured Reforms

Published

13 minutes ago

September 13, 2025

The Editors

TLDRs;

Albania introduces Diella, an AI “minister,” to oversee public procurement amid EU pressure for anti-corruption reforms.
Prime Minister Edi Rama says Diella will make tenders faster, more efficient, and corruption-free.
Supporters see the AI as a step toward EU integration; critics dismiss it as unconstitutional and symbolic.
Global examples show AI can help fight corruption but also risks bias and ineffective outcomes.

Albania has taken an unprecedented step in its long fight against corruption, introducing Diella, an artificial intelligence system tasked with overseeing public procurement.

Prime Minister Edi Rama unveiled the virtual minister as part of reforms tied to the nation’s bid for European Union membership.

Although not legally a minister under Albanian law, which requires cabinet members to be human citizens, Diella is being presented as the country’s first fully AI-powered figure in government. Her mission is clear, to bring transparency, efficiency, and accountability to one of Albania’s most corruption-prone areas.

Diella’s role in public procurement

Diella is no stranger to Albanian citizens. She first appeared as a virtual assistant on the government’s e-Albania platform, helping more than a million people navigate bureaucratic processes such as applying for official documents. Now, her responsibilities have expanded dramatically.

Rama explained that Diella’s core task will be supervising public tenders. “We want to ensure a system where public procurement is 100% free of corruption,” he said

By automating oversight and decision-making, Diella is expected to limit human interference in sensitive processes, while also making procurement faster and more transparent.

To develop this AI system, Albania is collaborating with both local and international experts, hoping to set a global precedent for AI governance.

🇦🇱 ALBANIA HIRES AI MINISTER TO FIGHT GOVERNMENT CORRUPTION

Albania has appointed Diella, an AI-powered virtual minister, to oversee public procurement and eliminate corruption from government tenders.

Prime Minister Edi Rama announced the move at his Socialist Party… https://t.co/fjDDSjsis0 pic.twitter.com/54wr4Bl5fp

— Mario Nawfal (@MarioNawfal) September 12, 2025

Mixed reactions at home and abroad

The announcement has stirred heated debate within Albania and beyond. Supporters hail the move as a chance to rebuild public trust, especially as the country faces mounting EU pressure to eliminate systemic graft.

Dr. Andi Hoxhaj of King’s College London notes that the EU has made anti-corruption reforms a central condition for accession. “There’s a lot at stake,” he said, suggesting that Diella could serve as a tool to accelerate reforms.

However, critics see the initiative as political theatre. Opposition leaders argue that branding Diella a “minister” is unconstitutional and distracts from deeper structural issues. Some worry that AI cannot fully address entrenched human networks of influence, while others raise concerns about accountability if an algorithm makes a faulty decision.

Lessons from global experiments with AI governance

Albania’s experiment comes amid a wave of governments testing artificial intelligence in public administration. Brazil’s Alice bot has reduced fraud-related financial losses by nearly 30% in procurement audits, while its Rosie bot, which monitored parliamentary expenditures, faced limitations in producing actionable evidence.

In Europe, the Digiwhist project has shown how big data can expose procurement fraud across dozens of jurisdictions. Yet, the Netherlands’ failed attempt at AI-led welfare fraud detection, widely criticized for algorithmic bias, highlights the risks of misuse.

These examples underscore both the potential and pitfalls of AI in governance. Albania now finds itself at a critical juncture: if implemented responsibly, Diella could strengthen transparency and accelerate EU integration.

Looking ahead

Prime Minister Rama acknowledges the symbolic dimension of Diella’s appointment but insists that serious intent lies beneath the theatrics. Beyond tackling procurement fraud, he believes the AI minister will put pressure on human officials to rethink outdated practices and embrace innovation.

“Ministers should take note,” Rama said with a smile. “AI could be coming for their jobs, too.”

As Albania balances hope, skepticism, and the weight of EU expectations, Diella’s debut represents both a technological leap and a political gamble. Whether she becomes a catalyst for real reform or remains a publicity stunt will depend on execution and public trust.

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Artificial intelligence helps break barriers for Hispanic homeownership | Business

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3 hours ago

September 13, 2025

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Artificial intelligence helps break barriers for Hispanic homeownership | Business | journalgazette.net

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UW lab spinoff focused on AI-enabled protein design cancer treatments

Published

3 hours ago

September 13, 2025

The Editors

A Seattle startup company has inked a deal with Eli Lilly to develop AI powered cancer treatments. The team at Lila Biologics says they’re pioneering the translation of AI design proteins for therapeutic applications. Anindya Roy is the company’s co-founder and chief scientist. He told KUOW’s Paige Browning about their work.

This interview has been edited for clarity.

Paige Browning: Tell us about Lila Biologics. You spun out of UW Professor David Baker’s protein design lab. What’s Lila’s origin story?

Anindya Roy: I moved to David Baker’s group as a postdoctoral scientist, where I was working on some of the molecules that we are currently developing at Lila. It is an absolutely fantastic place to work. It was one of the coolest experiences of my career.

The Institute for Protein Design has a program called the Translational Investigator Program, which incubates promising technologies before it spins them out. I was part of that program for four or five years where I was generating some of the translational data. I met Jake Kraft, the CEO of Lila Biologics, at IPD, and we decided to team up in 2023 to spin out Lila.

You got a huge boost recently, a collaboration with Eli Lilly, one of the world’s largest pharmaceutical companies. What are you hoping to achieve together, and what’s your timeline?

The current collaboration is one year, and then there are other targets that we can work on. We are really excited to be partnering with Lilly, mainly because, as you mentioned, it is one of the top pharma companies in the US. We are excited to learn from each other, as well as leverage their amazing clinical developmental team to actually develop medicine for patients who don’t have that many options currently.

You are using artificial intelligence and machine learning to create cancer treatments. What exactly are you developing?

Lila Biologics is a pre-clinical stage company. We use machine learning to design novel drugs. We have mainly two different interests. One is to develop targeted radiotherapy to treat solid tumors, and the second is developing long acting injectables for lung and heart diseases. What I mean by long acting injectables is something that you take every three or six months.

Tell me a little bit more about the type of tumors that you are focusing on.

We have a wide variety of solid tumors that we are going for, lung cancer, ovarian cancer, and pancreatic cancer. That’s something that we are really focused on.

And tell me a little bit about the partnership you have with Eli Lilly. What are you creating there when it comes to cancers?

The collaboration is mainly centered around targeted radiotherapy for treating solid tumors, and it’s a multi-target research collaboration. Lila Biologics is responsible for giving Lilly a development candidate, which is basically an optimized drug molecule that is ready for FDA filing. Lilly will take over after we give them the optimized molecule for the clinical development and taking those molecules through clinical trials.

Why use AI for this? What edge is that giving you, or what opportunities does it have that human intelligence can’t accomplish?

In the last couple of years, artificial intelligence has fundamentally changed how we actually design proteins. For example, in last five years, the success rate of designing protein in the computer has gone from around one to 2% to 10% or more. With that unprecedented success rate, we do believe we can bring a lot of drugs needed for the patients, especially for cancer and cardiovascular diseases.

In general, drug design is a very, very difficult problem, and it has really, really high failure rates. So, for example, 90% of the drugs that actually enter the clinic actually fail, mainly due to you cannot make them in scale, or some toxicity issues. When we first started Lila, we thought we can take a holistic approach, where we can actually include some of this downstream risk in the computational design part. So, we asked, can machine learning help us designing proteins that scale well? Meaning, can we make them in large scale, or they’re stable on the benchtop for months, so we don’t face those costly downstream failures? And so far, it’s looking really promising.

When did you realize you might be able to use machine learning and AI to treat cancer?

When we actually looked at this problem, we were thinking whether we can actually increase the clinical success rate. That has been one of the main bottlenecks of drug design. As I mentioned before, 90% of the drugs that actually enter the clinic fail. So, we are really hoping we can actually change that in next five to 10 years, where you can actually confidently predict the clinical properties of a molecule. In other words, what I’m trying to say is that can you predict how a molecule will behave in a living system. And if we can do that confidently, that will increase the success rate of drug development. And we are really optimistic, and we’ll see how it turns out in the next five to 10 years.

Beyond treating hard to tackle tumors at Lila, are there other challenges you hope to take on in the future?

Yeah. It is a really difficult problem to predict how a molecule will behave in a living system. Meaning, we are really good at designing molecules that behave in a certain way, bind to a protein in a certain way, but the moment you try to put that molecule in a human, it’s really hard to predict how that molecule will behave, or whether the molecule is going to the place of the disease, or the tissue of the disease. And that is one of the main reasons there is a 90% failure in drug development.

I think the whole field is moving towards this predictability of biological properties of a molecule, where you can actually predict how this molecule will behave in a human system, or how long it will stay in the body. I think when the computational tools become good enough, when we can predict these properties really well, I think that’s where the fun begins, and we can actually generate molecules with a really high success rate in a really short period of time.

Listen to the interview by clicking the play button above.

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