A Californian company Nuvve, focused on vehicle-to-grid technology, has announced plans to invest up to 30% of its excess cash into Bitcoin. The move is part of the company’s broader strategy to diversify its treasury holdings. The actual amount of Bitcoin purchased will depend on market conditions and the company’s financial needs, according to a press release from 28 January.
In addition to holding Bitcoin, Nuvve intends to accept it as a payment method for both customers and suppliers. It is part of the company’s mission to promote grid electrification and offer more payment options with potentially lower transactional friction.
Nuvve’s decision to incorporate Bitcoin follows a growing trend among public companies, such as Oxbridge Re Holdings, which have added Bitcoin to their treasury reserves. Following the announcement, Nuvve’s shares saw a slight increase of 1.42% in pre-market trading.
SoftBank is set to invest $500 million in SkildAI, a fast-growing AI robotics startup, at a valuation of $4 billion. The company, founded just two years ago, specialises in building AI models that can be adapted for different robotic applications. Previous investors include Jeff Bezos, Lightspeed Venture Partners, and Coatue Management, who contributed to a $300 million round last July.
The investment comes amid surging interest in AI-powered robotics, with major backers like Bezos ramping up funding in the sector. Startups such as Physical Intelligence and Figure AI have also secured hundreds of millions in recent months to develop advanced robotic “brains” and humanoid robots.
SkildAI’s latest funding highlights the growing competition in AI-driven automation, with investors betting on smarter, more adaptable robots. As demand for robotics expands across industries, firms like SkildAI are positioning themselves at the forefront of this technological revolution.
Figure AI has announced the creation of the Centre for the Advancement of Humanoid Safety, a new initiative aimed at ensuring humanoid robots can operate safely in workplaces. Led by former Amazon Robotics safety engineer Rob Gruendel, the centre will focus on testing AI-controlled robots for stability, human detection, and navigation to minimise accidents.
The rise of humanoid robots in warehouses and factories has sparked concerns about their potential risks. Unlike traditional industrial robots, which were confined to cages, these machines move freely among workers, raising safety questions. Existing solutions, such as Amazon’s wearable safety vest and Veo Robotics’ vision-based systems, have helped, but regulation remains largely absent.
Figure AI plans to release regular safety reports detailing its progress, testing methods, and solutions for potential hazards. As companies push to integrate humanoid robots into daily operations, and eventually, into homes, the need for clear safety standards is becoming increasingly urgent.
Hugging Face has introduced Inference Providers, a new feature that allows developers to run AI models on third-party cloud services. Partnering with companies like SambaNova, Fal, Replicate, and Together AI, the platform now offers users the flexibility to deploy models on different infrastructures directly from their project pages.
Previously, Hugging Face primarily focused on its in-house AI hosting solutions, but the company is shifting towards a more collaborative approach. By integrating with external serverless providers, developers can now scale their models without managing hardware, making deployment easier and more cost-efficient. Users will pay standard provider rates, and Hugging Face Pro subscribers will receive additional free credits.
Since its founding in 2016, Hugging Face has grown into a leading AI model hub, backed by major investors like Google, Amazon, and Nvidia. With its latest move, the company continues to expand its ecosystem, making AI more accessible for developers worldwide.
The digital revolution has brought in remarkable innovations, and quantum computing is emerging as one of its brightest stars. As this technology begins to showcase its immense potential, questions are being raised about its impact on blockchain and cryptocurrency. With its ability to tackle problems thought to be unsolvable, quantum computing is redefining the limits of computational power.
At the same time, its rapid advancements leave many wondering whether it will bolster the crypto ecosystem or undermine its security and decentralised nature. Can this computing breakthrough empower crypto, or does it pose a threat to its very foundations? Let’s dive deeper.
What is quantum computing?
Quantum computing represents a groundbreaking leap in technology. Unlike classical computers that process data in binary (0s and 1s), quantum computers use qubits, capable of existing in multiple states simultaneously due to quantum phenomena such as superposition and entanglement.
For example, Google’s new chip, Willow, is claimed to solve a problem in just five minutes—a task that would take the world’s fastest supercomputers approximately ten septillion years—highlighting the extraordinary power of quantum computing and fuelling further debate about its implications.
These advancements enable quantum machines to handle problems with countless variables, benefiting fields such as electric vehicles, climate research, and logistics optimisation. While quantum computing promises faster, more efficient processing, its intersection with blockchain technology adds a layer of complexity so the story takes an interesting twist.
How does quantum computing relate to blockchain?
Blockchain technology relies on cryptographic protocols to secure transactions and ensure decentralisation. Cryptocurrencies like Bitcoin and Ethereum use elliptic curve cryptography (ECC)to safeguard wallets and transactions through mathematical puzzles that classical computers cannot solve quickly.
Quantum computers pose a significant challenge to these cryptographic foundations. Their advanced processing power could potentially expose private keys or alter transaction records, threatening the trustless environment that blockchain depends upon.
Opportunities: Can crypto benefit from quantum computing?
While the risks are concerning, quantum computing offers several opportunities to revolutionise blockchain:
Enhanced security: Developers can leverage quantum principles to create stronger, quantum-secure algorithms.
Smarter decentralisation: Quantum-powered computations could enhance the functionality of smart contracts and decentralised apps (DApps).
By embracing quantum advancements, the blockchain industry could evolve to become more robust and scalable— hopefully great news for the crypto community, which is optimistic about the potential for progress.
How does quantum computing threaten cryptocurrency?
Despite its potential benefits, quantum computing poses significant risks to the cryptocurrency ecosystem, depending on how it is used and who controls it:
Breaking public key cryptography Quantum computers equipped with Shor’s algorithm can decrypt ECC and RSA encryption. Tasks that would take classical computers millennia could be accomplished by a quantum computer in mere hours. This capability threatens to expose private keys, allowing hackers to access wallets and steal funds.
Mining oligopoly The mining process, vital for cryptocurrency creation and transaction validation, depends on computational difficulty. Quantum computers could dominate mining activities, disrupting the decentralisation and fairness fundamental to blockchain systems.
Dormant wallet risks Wallets with exposed public keys, particularly older ones, are at heightened risk. A quantum attack could compromise these funds before users can adopt protective measures.
With projections suggesting that quantum computers capable of breaking current encryption standards could emerge within 10–20 years—or perhaps even sooner—the urgency to address these threats is intensifying.
Solutions: Quantum-resistant tokens and cryptography
Where there is a challenge, there is a solution. The crypto industry is proactively addressing quantum threats with quantum-resistant tokens and post-quantum cryptography. Lattice-based cryptography, for example, creates puzzles too complex for quantum computers, with projects like CRYSTALS-Kyber leading the charge. Hash-based methods, such as QRL’s XMSS, ensure data integrity, while code-based cryptography, like the McEliece system, uses noisy signals to protect messages. Multivariate polynomial cryptography also adds robust defences through complex equations.
As we can see, promising solutions are already actively working to uphold blockchain principles. These innovations are crucial not only for securing crypto assets but also for maintaining the integrity of blockchain networks. Quantum-resistant measures ensure that transaction records remain immutable, safeguarding the trust and transparency that decentralised systems are built upon.
The quantum future for crypto
Quantum computing holds tremendous promise for humanity, but it also brings challenges, particularly for blockchain and cryptocurrency. As its capabilities grow, the risks to existing cryptographic protocols become more apparent. However, the crypto community has shown remarkable resilience, with quantum-resistant technologies already being developed to secure the ecosystem. This cycle of threats and solutions is a perpetual motion—each technological advancement introduces new vulnerabilities, met with equally innovative defences. It is the inevitable price to pay for embracing the modern decentralised finance era and the transformative potential it brings.
The future of crypto does not have to be at odds with quantum advancements. With proactive innovation, collaboration, and the implementation of quantum-safe solutions, blockchain can survive and thrive in the quantum era. So, is quantum computing a threat to cryptocurrency? The answer lies in our ability to adapt. After all, with great power comes great responsibility—and opportunity.
Global technology stocks experienced a sharp decline on Tuesday, with a second day of losses triggered by the emergence of a low-cost Chinese AI model. This new AI assistant, launched by China’s DeepSeek, has raised doubts about the dominance of established AI leaders like Nvidia and OpenAI. Nvidia’s shares plummeted 17% on Monday, erasing $593 billion from its market value. Other major tech companies such as Broadcom, Microsoft, and Alphabet also saw significant declines, fueling broader market concerns.
The unexpected launch of DeepSeek’s AI, which claims to use fewer data and lower costs than existing models, has disrupted the market, causing scepticism among investors. While OpenAI CEO Sam Altman praised the model, calling it ‘impressive,’ the sudden rise of a competitor from China has surprised many and highlighted the rapid pace of advancements in AI technology. This development has led to a global sell-off in tech stocks, with significant drops in companies across the US, Europe, and Japan.
The sell-off has raised concerns about the high valuations of AI and tech stocks, which have seen inflated prices due to the AI boom. Nvidia, for example, had been trading at nearly 60 times its earnings, far above the broader market’s 22 times. The market downturn underscores the risks tied to the heavy concentration of tech stocks in investor portfolios, with many fearing that the industry’s rapid expansion has created an unsustainable bubble.
This market shakeup also reflects the broader issue of leverage in the system, with investors increasingly borrowing to buy high-priced tech stocks. As a result, the unwinding of these positions, combined with algorithmic trading, has intensified the sell-off. With key earnings reports from companies like Apple and Microsoft expected this week, investors are closely watching how tech executives address concerns about capital spending and the future of AI investments.
OpenAI CEO Sam Altman has called Chinese startup DeepSeek’s R1 model “impressive,” highlighting its ability to deliver advanced AI performance at a fraction of the cost. According to DeepSeek, its R1 model is 20 to 50 times cheaper to use than OpenAI’s own models, offering significant affordability without sacrificing quality.
Chinese AI, DeepSeek gained global recognition last month when it revealed that training its DeepSeek-V3 model required less than $6 million in computing resources, leveraging lower-cost Nvidia H800 chips. In contrast, Altman noted that OpenAI remains committed to prioritising increased computing power, suggesting this as an important factor in achieving AI progress.
The emergence of DeepSeek has disrupted the AI industry, leading to a significant sell-off in tech stocks, including Nvidia, which recorded a historic single-day loss of $593 billion in market value. Analysts say DeepSeek’s cost-efficient approach raises doubts about the necessity of the massive financial investments made by US tech firms in AI development.
As DeepSeek continues to attract attention, the startup’s success underscores a shift in the AI market, with low-cost models challenging traditional notions of progress in AI.
Chinese AI startup DeepSeek has announced that its Janus-Pro-7B model has surpassed competitors, including OpenAI’s DALL-E 3 and Stability AI’s Stable Diffusion, in benchmark rankings for text-to-image generation. This achievement solidifies DeepSeek’s reputation as a key player in the rapidly evolving AI market.
According to a technical report, the Janus-Pro model builds upon its predecessor by incorporating enhanced training processes, higher-quality data, and advanced scaling, resulting in improved stability and more detailed image outputs. The company credited the inclusion of 72 million high-quality synthetic images, combined with real-world data, for the model’s superior performance.
This success follows the launch of DeepSeek’s new AI assistant based on the DeepSeek-V3 model, which has become the top-rated free app in the US Apple App Store. The news sent shockwaves through the tech industry, leading to declines in shares of companies like Nvidia and Oracle, as investors reassessed the competitive dynamics in AI development.
OpenAI and Stability AI have yet to comment on the claims. DeepSeek’s achievements highlight the growing influence of Chinese firms in cutting-edge AI innovation, setting the stage for heightened competition in the global tech market.
Taiwan described its semiconductor business with the US as a mutually beneficial partnership in response to tariff threats by Donald Trump. The economy ministry highlighted the complementary relationship between US-designed and Taiwan-produced chips, which has bolstered industries in both nations.
Trump proposed tariffs on imports of chips, pharmaceuticals, and steel, aiming to bring production to US soil. Taiwan stressed its commitment to close cooperation with Washington to address global challenges while supporting shared national interests. The presidential office reinforced this sentiment, emphasising trust and collaboration in high-tech fields.
Taiwan Semiconductor Manufacturing Co. (TSMC), the world’s largest contract chipmaker, remains central to the global tech supply chain. Despite tariff concerns, TSMC’s ongoing $65 billion investment in US facilities demonstrates a commitment to bilateral cooperation. Taiwan’s economy minister noted minimal expected impact from tariffs due to the island’s technological leadership.
Taiwan’s trade surplus with the US surged 83% last year, fuelled by semiconductor demand. While Taiwan remains cautious about evolving US trade policies, it remains optimistic about maintaining robust economic ties.
Microsoft’s upcoming quarterly forecast will reveal whether its significant investments in AI, including its partnership with OpenAI, drive growth in its key Azure cloud business. Despite earlier optimism, Azure’s growth has slowed for two consecutive quarters, and investors are anxious about Microsoft’s ability to monetise AI. The company has committed about $80 billion in capital spending this year, but doubts linger over the effectiveness of its strategy, especially after a sharp drop in stock price following the launch of a competitive AI model by Chinese startup DeepSeek.
Azure, which contributes around a third of Microsoft’s revenue, is expected to show 31.8% growth in the second quarter, a slight slowdown from the previous quarter. Microsoft’s relationship with OpenAI remains a key growth driver, with Azure set to handle much of OpenAI’s cloud traffic. However, investor sentiment has soured, with growing concerns about AI monetisation, margins, and capital expenditure. Microsoft also faces the impact of a stronger dollar, which could hurt its international earnings.
In addition to Azure, Microsoft is banking on the success of its Microsoft 365 Copilot AI assistant, but adoption has been slower than anticipated. To stimulate demand, the company has adjusted its pricing, adding AI features to lower-tier Microsoft 365 plans. While the Copilot’s potential remains high, analysts project a modest penetration rate of 10%, suggesting it could add significant revenue in the coming years. Despite these challenges, Microsoft’s productivity division, which includes 365 Copilot and LinkedIn, is expected to see continued growth.
Overall, Microsoft is forecasted to report slower growth for the second quarter, with revenue expected to rise by 10.9% compared to 16% in the first quarter. Net profit is also projected to increase at a slower pace, raising questions about whether the company’s AI investments will pay off as anticipated.
