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Government Prioritizes Energy Security While Reducing Consumer Costs

Energy Security As The Primary Policy Focus

Michalis Damianos reiterated that ensuring energy security remains the government’s primary objective, with reducing costs for consumers identified as the next key priority. Speaking on a televised program, he explained that there is general agreement on the targets set in cooperation with Public Power Corporation, although approaches to achieving these objectives continue to differ. The discussion reflects an ongoing effort to balance long-term planning with immediate market pressures.

Natural Gas: A Strategic Pillar

A central element of this strategy is the introduction of natural gas into the energy system at Vasiliko. According to the minister, natural gas is expected to offer a more cost-efficient and less polluting alternative compared with existing fuels. Its integration is seen as a necessary step toward modernizing the energy mix and supporting a more stable pricing environment over time.

Investing In Storage And Renewables For Future Stability

Alongside natural gas, attention is also being directed toward expanding energy storage capacity and accelerating the development of renewable energy sources. These initiatives are expected to play a key role in improving grid stability and managing fluctuations in supply. Over the medium term, such investments are also intended to contribute to a gradual reduction in electricity costs, while supporting broader environmental objectives.

Addressing Persistent Challenges

Progress on major infrastructure projects has not been without obstacles. Delays affecting developments such as the Vasiliko power station and the broader rollout of natural gas have been acknowledged. Minister Damianos attributed these setbacks to structural and coordination challenges involving multiple stakeholders, noting that large-scale energy transitions often require complex alignment between public institutions, private operators, and regulatory bodies.

Engaging In Constructive Dialogue

Recent developments involving labor groups and the potential for further industrial action have added another layer of complexity to the sector. In response, the government has emphasized the importance of maintaining open and constructive dialogue. The aim is to reach balanced solutions that address workforce concerns while ensuring that strategic energy objectives remain on track.

Meta Unveils Muse Wonder Models In A Bid For Frontier AI Leadership

Meta has introduced two new AI models, Muse Wonder and Muse Wonder Full, signalling a more ambitious move into frontier AI. The releases build on Muse Spark but target more demanding tasks, including coding, while placing greater emphasis on long-term memory and personalised assistance.

Larger Models Target More Demanding Tasks

Unlike Muse Spark, which was designed for low latency and lightweight use, Muse Wonder and Wonder Full are significantly larger and, according to Meta, more capable. The company reports stronger benchmark performance and improvements on demanding workloads, including coding.

According to Meta’s model card, both models achieved benchmark results comparable to Fable 5.1 and GPT-6 Astra in the company’s internal testing. Meta described the release as “the next step on our scaling ladder and a major leap toward personal superintelligence” in its announcement.

Pricing Reflects Meta’s Frontier Ambitions

The models’ pricing places them in the premium AI segment. Through the Meta Model API, Muse Wonder will cost $4 per million input tokens and $20 per million output tokens, putting it in a similar pricing tier to GPT-6 Sol and Opus 5.5.

Muse Wonder Full, by contrast, will not be offered through the API. Meta says it will be exclusive to Muse Code subscribers, tying access to its most advanced model to a premium product. Both models will also power the Muse agent.

Long-Term Memory Takes Centre Stage

Meta is increasingly positioning its AI strategy around assistants that can support users over time rather than respond only to individual prompts. A central part of that approach is what the company calls “active memory”: the ability to retain, organise and synthesise information provided by users across extended periods.

Meta points to the MemoryBench benchmark, which evaluates systems designed for continual learning and memory construction. The company says Muse Wonder now outperforms Fable, Opus and Astra on this measure.

“We put a tremendous amount of effort into making sure your model can remember what’s important to you over days, weeks, months, and beyond,” research scientist Kavya Kopparapu said in a launch video.

New Models Follow Meta’s AI Reorganisation

The launch is also significant within Meta’s broader AI strategy. It is the company’s first frontier-model release since Scale AI founder Alexandr Wang reorganised its AI operations in August 2025. Meta has since introduced Muse Spark and the Muse agent, while making fewer high-profile model announcements than competitors such as OpenAI and Anthropic.

The new models were trained at Meta’s Prometheus data centre in central Ohio, which came online earlier in 2026, according to the company. The facility highlights the growing importance of infrastructure in the race to develop advanced AI systems, where performance depends not only on algorithms and data but also on computing capacity, data-centre investment and access to capital.

As competition intensifies, Muse Wonder and Wonder Full represent a broader bet on larger models, persistent memory and premium AI products. Their longer-term significance will depend on how their reported capabilities translate into real-world performance and whether users find enough value to pay for access.

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