Artificial intelligence
fromAxios
9 hours agoBehind the Curtain: We've been warned
The rapid advancement of AI technology poses significant risks and uncertainties that require urgent attention and responsible leadership.
Generative AI is dissolving the economic logic that made standardized enterprise software the only practical choice for most companies. What replaces it will be shaped not just by the rapidly evolving capabilities of this new technology, but by leaders willing to ask a harder question: Which workflows do we actually need to own?
I looked at the config and noticed the customer did not have a default route set. He wasn't sure if that was the problem, so he made some changes he thought might be useful. The router Caleb worked on then rebooted, which he expected. But when it restarted, its previous configuration was gone.
Tax season is stressful for many, making it an ideal time for scammers to target unsuspecting and distracted taxpayers. Awareness is our first, and best, line of defense. Criminals often pose as the IRS, payroll companies, tax preparation services, or even trusted financial institutions in an effort to steal money and sensitive information.
Think of an AI Agent like a new employee who has the keys to every office in your building but doesn't have a name tag. Because these agents act on their own, they often have access to sensitive information that nobody is watching. Hackers have figured this out. They don't need to break your password anymore-they just need to trick your AI Agent into doing the work for them.
Purpose-Built AI vs. General AI: The Bonfire vs. Blowtorch Analogy Mathew shares a powerful analogy from Daza Greenwood about the difference between general AI tools (ChatGPT, Claude, Gemini) and purpose-built legal AI. General AI is like a massive bonfire-incredibly powerful and versatile, but not specialized. Purpose-built legal AI is like taking that fire and harnessing it into a blowtorch - you can now weld with precision.
But if you dig deeper into how businesses in this industry are actually approaching AI deployments - if you ask questions like how they are governing their data, how they are ensuring data quality, and how easily are they connecting AI tools directly to data platforms - you'll soon realize that claims about AI adoption in financial services don't always align with reality.