Decentralisation has long been an advocate of equilibrium in power structures, fostering resilience, and curbing monopolies. As we march deeper into the 21st century, this principle finds its newest avatar in a rather unexpected realm—the Artificial Intelligence (AI) industry. Emboldened by the rise of cloud-independent AI models, decentralisation of intelligence is now poised to disrupt the AI landscape, challenging its traditional power dynamics.

The genesis of AI was characterized by centralization, primarily due to the hefty computational resources required to train and run these models. Enterprises monopolized AI services with proprietary algorithms hosted on their massive cloud infrastructure, turning it into a sort of oligopoly. However, cloud-independent AI, propelled by advancements in edge computing and AI algorithms, is disrupting this status quo.

**Breaking the Chains: The Advent of Cloud-Independent AI**

Cloud-independent AI—systems which operate and learn at the device level, free from constant cloud tethering—are on the rise. Thanks to improved processors and power-efficient algorithms, AI capabilities can now be embedded within devices, from smartphones to Internet of Things (IoT) sensors, enabling real-time insights, privacy preservation, and lesser reliance on internet connectivity.

**A Shifting Landscape: Impacts on the AI Industry**

The AI industry is poised for a seismic shift due to this decentralisation. With edge AI's potential to democratise access, the barriers to entry for AI applications could be drastically reduced, fostering a fertile ground for innovation and competition.

Secondly, data privacy could be significantly enhanced with cloud-independent models. Since data processing occurs locally, the risk of data breach during transmission to the cloud is diminished. This could bolster consumer confidence and drive wider acceptance of AI applications in sensitive sectors like healthcare or finance.

Additionally, the reduced reliance on the internet for AI functionality has profound implications for digital equity. Cloud-independent AI can function in low-connectivity areas, unlocking the potential of AI for developing regions and further democratizing access.

**Challenges and Considerations: Navigating the New Order**

However, the road to this new order isn't devoid of hurdles. Edge devices, despite their advancements, have limitations in terms of storage and processing power. This might restrict the complexity of the models they can host, potentially hindering the breadth of AI capabilities on offer.

Further, there are challenges related to the security of edge devices, as these may become appealing targets for hackers. Ensuring robust security measures on these devices is essential to prevent malevolent intrusions.

**A New Dawn: Preparing for the Future**

As we navigate this transformation, adaptability will be key. Traditional AI powerhouses need to reimagine their strategies, leaning into the shift rather than resisting it. Investing in edge AI capabilities and developing robust, efficient AI models suitable for edge devices could be crucial for maintaining competitive advantage.

The rise of cloud-independent AI demands a recalibration of policy and regulatory frameworks as well. Policymakers need to preemptively address potential security concerns and privacy issues to ensure a safe, fair, and ethical AI ecosystem.

As we stand at the precipice of this profound shift in the AI industry, the decentralisation of intelligence could be an antidote to the centralisation tendencies of the digital age. Unshackling AI from the cloud is not merely a technological evolution, but a step towards democratizing intelligence, fostering inclusivity, and rebalancing power within the AI landscape.