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The Future of Shopping? AI + Actual Humans.

AI has changed how consumers shop by speeding up research. But one thing hasn’t changed: shoppers still trust people more than AI.

Levanta’s new Affiliate 3.0 Consumer Report reveals a major shift in how shoppers blend AI tools with human influence. Consumers use AI to explore options, but when it comes time to buy, they still turn to creators, communities, and real experiences to validate their decisions.

The data shows:

  • Only 10% of shoppers buy through AI-recommended links

  • 87% discover products through creators, blogs, or communities they trust

  • Human sources like reviews and creators rank higher in trust than AI recommendations

The most effective brands are combining AI discovery with authentic human influence to drive measurable conversions.

Affiliate marketing isn’t being replaced by AI, it’s being amplified by it.

Download the full report to see what this means for your brand.

How new Xe driver enhancements unlock better performance and scalability for AI workloads

Linux continues to be the backbone of modern AI infrastructure—and Intel is doubling down on that reality. With Linux Kernel 7.0, Intel has introduced a major upgrade to its Xe GPU driver: Multi-GPU Shared Virtual Memory (SVM).

This change may sound technical, but its impact on AI workloads, performance, and developer productivity is significant.

What is Multi-GPU Shared Virtual Memory (SVM)?

Shared Virtual Memory allows CPUs and GPUs to access the same memory space without explicit copying. When extended across multiple GPUs, SVM enables:

  • Seamless memory sharing across GPUs

  • Reduced data-copy overhead

  • Simpler programming models

  • Faster execution for data-intensive workloads

For AI and machine learning, where massive datasets are constantly moved between accelerators, this is a big deal.

Why this matters for AI on Linux

AI workloads are increasingly multi-GPU by design—especially for training large models. Without efficient memory sharing, performance suffers.

With Multi-GPU SVM:

  • Models scale more efficiently across GPUs

  • Memory bottlenecks are reduced

  • Latency drops for large tensor operations

  • Developers spend less time managing memory manually

This brings Intel’s Linux GPU stack closer to the needs of modern AI pipelines.

What’s new in the Intel Xe driver

The updated Xe driver in Linux kernel 7.0 introduces:

  • Improved GPU-to-GPU memory access

  • Better coordination between CPU and multiple GPUs

  • Foundations for more advanced AI and HPC workloads

  • Stronger alignment with open-source accelerator frameworks

These improvements help position Intel GPUs as more competitive options for AI workloads on Linux.

Why open source makes this important

Unlike proprietary stacks, Intel’s Linux GPU work happens largely in the open.

This means:

  • Faster community feedback

  • Easier integration with AI frameworks

  • Greater transparency for developers

  • Long-term stability for enterprise deployments

For Linux users, these kernel-level improvements arrive without vendor lock-in.

What this means for developers and sysadmins

For developers:

  • Simpler multi-GPU programming

  • Better performance with fewer code changes

  • More efficient AI training and inference

For system administrators:

  • Improved scalability on Intel GPU hardware

  • Better utilization of multi-GPU servers

  • Stronger Linux-native AI infrastructure

This is especially relevant for organizations exploring alternatives in the rapidly evolving AI hardware landscape.

Final Thoughts

Intel’s addition of Multi-GPU SVM to the Xe driver in Linux Kernel 7.0 is a clear signal:
Linux-based AI is becoming more open, more scalable, and more hardware-agnostic.

As AI workloads continue to grow in size and complexity, kernel-level innovations like this will quietly power the next generation of performance gains—without changing how developers work.

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