OpenAI Unveils New AI Models: GPT-4.1, o3, and o4 Mini

OpenAI is preparing to release a range of new AI models, according to sources familiar with the company’s plans. Among the new models will be GPT-4.1, a revamped version of OpenAI’s GPT-4o multimodal model, which one source describes as a significant improvement.

GPT-4.1: The Next Generation of Multimodal AI

GPT-4o was introduced last year as a flagship model that could reason across audio, vision, and text in real-time. The new GPT-4.1 model is expected to build upon this technology, offering even more advanced capabilities. Additionally, OpenAI will launch smaller GPT-4.1 mini and nano versions, which could debut as soon as next week.

Other New AI Models: o3 and o4 Mini

OpenAI is also readying the full version of its o3 reasoning model and an o4 mini version. These models were discovered by AI engineer Tibo Blaho in a new ChatGPT web version earlier today, suggesting their imminent release. It is expected that o3 and o4 mini will debut next week, unless OpenAI decides to postpone their launch.

Capacity Issues May Delay Launch

OpenAI CEO Sam Altman has previously warned that the company may delay the introduction of new models due to capacity issues. This is because the company’s more advanced image generation capabilities have put a strain on its servers, forcing it to temporarily rate limit requests last month. Altman has stated that “our GPUs are melting” due to the popularity of the built-in image generator for users of ChatGPT’s free tier.

Conclusion

OpenAI’s new AI models, including GPT-4.1, o3, and o4 mini, are expected to offer significant improvements in multimodal reasoning and capabilities. While capacity issues may delay their launch, the company is working to address these challenges and bring its new models to market as soon as possible.

FAQs

Q: What is GPT-4.1?
A: GPT-4.1 is a revamped version of OpenAI’s GPT-4o multimodal model, which can reason across audio, vision, and text in real-time.

Q: When can we expect the new AI models to launch?
A: The new AI models, including GPT-4.1, o3, and o4 mini, are expected to debut next week, unless OpenAI decides to postpone their launch due to capacity issues.

Q: What are o3 and o4 mini?
A: o3 is the full version of OpenAI’s o3 reasoning model, while o4 mini is a smaller version of the o4 model. Both are expected to offer advanced multimodal reasoning capabilities.

Q: Why may the launch of the new AI models be delayed?
A: The launch may be delayed due to capacity issues, which have been caused by the popularity of OpenAI’s image generation capabilities and the need to rate limit requests to prevent server overload.

The International Energy Agency (IEA) has explored the opportunities and challenges brought about by AI with regards to global energy.

Surging Data Centre Investments

Global investment in data centres has nearly doubled since 2022, reaching half a trillion dollars in 2024, sparking concerns about escalating electricity needs. While data centres accounted for approximately 1.5% of global electricity consumption in 2024 (around 415 terawatt-hours, TWh), their local impact is far more significant. Consumption has grown annually by about 12% since 2017, vastly outpacing overall electricity demand growth.

The US leads this consumption (45%), followed by China (25%) and Europe (15%). Almost half of US data centre capacity is concentrated in just five regional clusters.

Looking ahead, the IEA projects global data centre electricity consumption to more than double by 2030 to reach approximately 945 TWh. To put that in context, that’s slightly more than Japan’s current total electricity consumption.

AI is pinpointed as the "most important driver of this growth". The US is projected to see the largest increase, where data centres could account for nearly half of all electricity demand growth by 2030. By the decade’s end, US data centres are forecast to consume more electricity than the combined usage of its aluminium, steel, cement, chemical, and other energy-intensive manufacturing industries.

The IEA’s "Base Case" extends this trajectory, anticipating around 1,200 TWh of global data centre electricity consumption by 2035. However, significant uncertainties exist, with projections for 2035 ranging from 700 TWh ("Headwinds Case") to 1,700 TWh ("Lift-Off Case") depending on AI uptake, efficiency gains, and energy sector bottlenecks.

Meeting the Global AI Energy Demand

Powering this AI boom requires a diverse energy portfolio. The IEA suggests renewables and natural gas will take the lead, but emerging technologies like small modular nuclear reactors (SMRs) and advanced geothermal also have a role.

Renewables, supported by storage and grid infrastructure, are projected to meet half the growth in data centre demand globally up to 2035. Natural gas is also crucial, particularly in the US, expanding by 175 TWh to meet data centre needs by 2035 in the Base Case. Nuclear power contributes similarly, especially in China, Japan, and the US, with the first SMRs expected around 2030.

However, simply increasing generation isn’t sufficient. The IEA stresses the critical need for infrastructure upgrades, particularly grid investment. Existing grids are already strained, potentially delaying around 20% of planned data centre projects globally due to complex connection queues and long lead times for essential components like transformers.

The Potential of AI to Optimise Energy Systems

Beyond its energy demands, AI offers significant potential to revolutionise the energy sector itself.

• Energy supply: The oil and gas industry – an early adopter – uses AI to optimise exploration, production, maintenance, and safety, including reducing methane emissions. AI can also aid critical mineral exploration.
• Electricity sector: AI can improve forecasting for variable renewables, reducing curtailment. It enhances grid balancing, fault detection (reducing outage durations by 30-50%), and can unlock significant transmission capacity through smarter management—potentially 175 GW without building new lines.
• End uses: In industry, widespread AI adoption for process optimisation could yield energy savings equivalent to Mexico’s total energy consumption today. Transport applications like traffic management and route optimisation could save energy equivalent to 120 million cars, though rebound effects from autonomous vehicles need monitoring. Building optimisation potential is significant but hampered by slower digitalisation.
• Innovation: AI can dramatically accelerate the discovery and testing of new energy technologies, such as advanced battery chemistries, catalysts for synthetic fuels, and carbon capture materials. However, the energy sector currently underutilises AI for innovation compared to fields like biomedicine.

Collaboration is Key to Navigating Challenges

Despite the potential, significant barriers hinder AI’s full integration into the energy sector. These include data access and quality issues, inadequate digital infrastructure and skills (AI talent concentration is lower in energy sectors), regulatory hurdles, and security concerns.

Cybersecurity is a double-edged sword: while AI enhances defence capabilities, it also equips attackers with sophisticated tools. Cyberattacks on utilities have tripled in the last four years.

Supply chain security is another critical concern, particularly regarding critical minerals like gallium (used in advanced chips), where supply is highly concentrated.

The IEA concludes that deeper dialogue and collaboration between the technology sector, the energy industry, and policymakers are paramount. Addressing grid integration challenges requires smarter data centre siting, exploring operational flexibility, and streamlining permitting.

While AI presents opportunities for substantial emissions reductions through optimisation, exceeding the emissions generated by data centres, these gains are not guaranteed and could be offset by rebound effects.

"AI is a tool, potentially an incredibly powerful one, but it is up to us – our societies, governments, and companies – how we use it," said Dr. Birol.

"The IEA will continue to provide the data, analysis, and forums for dialogue to help policymakers and other stakeholders navigate the path ahead as the energy sector shapes the future of AI, and AI shapes the future of energy."

Conclusion

The integration of AI in the energy sector is a complex and multifaceted issue. While AI presents opportunities for substantial emissions reductions and energy efficiency gains, it also raises concerns about energy consumption and infrastructure investments.

The IEA’s report highlights the need for deeper dialogue and collaboration between the technology sector, the energy industry, and policymakers to address the challenges and opportunities presented by AI.

By working together, we can navigate the challenges and unlock the full potential of AI to optimise energy systems and create a more sustainable and efficient energy future.

Frequently Asked Questions

* Q: What is the current global data centre electricity consumption?
A: Approximately 415 terawatt-hours (TWh) in 2024.
* Q: What is the projected growth in data centre electricity consumption by 2030?
A: More than double, reaching approximately 945 TWh.
* Q: Which region leads in data centre consumption?
A: The US, accounting for 45% of global data centre consumption.
* Q: What is the potential impact of AI on energy consumption in the US?
A: Data centres could account for nearly half of all electricity demand growth by 2030, and consume more electricity than the combined usage of its aluminium, steel, cement, chemical, and other energy-intensive manufacturing industries by the decade’s end.
* Q: What is the potential of AI to optimise energy systems?
A: AI can improve forecasting for variable renewables, reduce curtailment, enhance grid balancing, and unlock significant transmission capacity through smarter management.

How Ominimo Attracts Top Talent in the Insurance Industry

The Challenges of Hiring Top Talent

How do you get talented engineers to work for a startup in a mundane field at a time when more exciting companies are paying well and hiring aggressively? One insurance startup, Ominimo, has found a way to attract top talent by giving engineers the license to apply their skills and reinvent how the field works.

Ominimo’s Approach

Launchedin 12 months ago, Ominimo believes it has found a different and better approach to understanding and pricing risk. The company is already profitable and growing fast, with 300,000 policies signed up in its first market of Hungary. To fuel its next stage of life, Ominimo is taking its first outside investment from a strategic backer, Zurich Insurance Group.

Investment and Valuation

TechCrunch understands from sources that Zurich is making a €10 million equity investment (around $11 million) for 5% of the company, valuing Ominimo at €200 million ($220 million). Neither Ominimo nor Zurich commented on the amount invested, but both have confirmed the valuation.

The Opportunity

Ominimo’s funding comes at a time when one of the most well-known and well-capitalized insurance startups in Europe, WeFox, is selling off parts of its business and picking up lifeline financing to keep from going under. This serves as both a cautionary tale about how to grow an insurance business and a clear opportunity.

Ominimo’s Business Model

Ominimo is already profitable in its current business, but it’s a modest effort. The company is active in just one market, Hungary, and focuses on one kind of insurance, car insurance for consumers. The plan is to replicate its model to more geographies and categories. Ominimo will expand into more than 10 new markets, starting with Poland, Sweden, and the Netherlands, using Zurich as its risk carrier.

Using AI and Big-Data Analytics

The crux of what Ominimo is doing is applying some AI-based reasoning around big-data analytics. When building and pricing an insurance quote, a traditional insurance company might use five or six main parameters to determine a price. A newer insurer might add another 10 or 15 parameters to that. Ominimo takes all of these details, plus population density and more, into account when it’s going through its calculations.

Competitive Advantage

Ominimo’s track record speaks for itself, according to CEO Dusan Komar. "I think what really matters is actually performance in the market, so if you compare our performance to Lemonade’s, you will actually see the difference," he said.

Key to Attracting Top Talent

Giving talent a place to do the kind of work they want to be doing is key to attracting and retaining key people, according to Komar. "We have eight medalists from mathematics and physics olympiads among our data science team," he said. "These are really brilliant young minds who now, for the first time, get to deploy their full potential on a global scale."

Conclusion

Ominimo’s approach to attracting top talent in the insurance industry is a refreshing take on traditional recruitment strategies. By giving engineers the license to apply their skills and reinvent how the field works, the company has been able to attract and retain key talent.

FAQs

Q: What is Ominimo’s business model?
A: Ominimo is an insurance startup that uses AI and big-data analytics to offer car insurance to consumers in Hungary and plans to expand to more geographies and categories.

Q: How does Ominimo use AI and big-data analytics?
A: Ominimo uses AI-based reasoning around big-data analytics to take into account a wide range of parameters when building and pricing insurance quotes.

Q: What is Ominimo’s competitive advantage?
A: Ominimo’s track record speaks for itself, with a loss ratio below the market average and a market share of 7% in Hungary.

Q: How does Ominimo attract top talent?
A: Ominimo gives engineers the license to apply their skills and reinvent how the field works, attracting and retaining key talent.