NOTEWORTHY NEWS: OUR TAKE ON THE LATEST AI/GENAI NEWS
Noteworthy AI News for Agencies - Edition #13
By Jeremy Lockhorn , SVP, Creative Technologies & Innovation, 4A's
THE BIG STORY:?
The AI sector is beginning to show signs of a maturing market, as recent developments suggest a wave of consolidation is underway. Once the darling of venture capital, some AI startups are struggling to keep up with the hefty demands of research, talent, and infrastructure. A recent WSJ article highlights how a few of these startups are seeking bailouts from big tech companies, reflecting the growing difficulty of achieving sustainable growth without deep-pocketed backers. Meanwhile, those same deep-pocketed backers have told investors in recent earnings calls that they are playing the long game when it comes to realizing return on investment in AI.
At the same time, platforms like Canva are looking to supercharge their AI capabilities by acquiring Leonardo.AI. Such collaborations highlight how established companies are opting for strategic acquisitions or partnerships to enhance their offerings rather than developing these tools from scratch. This could be a sign that the race for AI dominance is entering a new phase, where a mix of home grown tools and partnerships / acquisitions becomes the recipe for success.
As these maneuvers unfold, it’s becoming clear that what some have called an AI bubble might not burst but rather leak a little air and then reshape. The market is no longer just about innovation—it's about who can integrate and scale these innovations effectively.?
For agencies, market maturity may eventually bring an ideal combination of choice, innovation and scale. In the near term, carefully assessing the AI technology stack required to meet your objectives - and thoroughly vetting any potential partners - remains a best practice for successful AI implementation.
Questions? Find answers on the 4A's AI/GENAI Hub.
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Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer
3 个月Jeremy's right; the market is shifting towards operational excellence. It's not enough to just have cutting-edge AI anymore, you need robust infrastructure and processes for deployment at scale. I mean, look at the recent surge in M&A activity it's a clear indication of this trend. How are agencies effectively leveraging federated learning to address data privacy concerns while scaling their GenAI solutions?