When we met Sterling Anderson in 2024, he was the chief product officer of Aurora, the self-driving startup he cofounded in 2016 after several years at Tesla. Just over a year ago, though, Anderson decamped from the startup world for something a little more established, taking over as chief product officer at General Motors, the nation's largest automaker. Since then, he's had a good view of how GM is entering what he calls the third epoch of engineering and design.
"There was a time when humans looked at birds and were like, 'OK, those wings seem to work pretty well. Let's go and design something that looks like them.'" Anderson said, describing the first age of engineering. "And they just kind of iterated their way to something that was marginally feasible."
The first few hundred years of inventing "was this era of highly empirical iterative design development and engineering," he said. "And by that I mean humans largely started with what we know or had seen, built prototypes of something that kind of looked like it and maybe tweaked some things, hoping to make it perform better, tested it, iterated, and kind of went through this slow guess-and-check process until we got to something that marginally worked."
When we met Sterling Anderson in 2024, he was the chief product officer of Aurora, the self-driving startup he cofounded in 2016 after several years at Tesla. Just over a year ago, though, Anderson decamped from the startup world for something a little more established, taking over as chief product officer at General Motors, the nation's largest automaker. Since then, he's had a good view of how GM is entering what he calls the third epoch of engineering and design.
"There was a time when humans looked at birds and were like, 'OK, those wings seem to work pretty well. Let's go and design something that looks like them.'" Anderson said, describing the first age of engineering. "And they just kind of iterated their way to something that was marginally feasible."
The first few hundred years of inventing "was this era of highly empirical iterative design development and engineering," he said. "And by that I mean humans largely started with what we know or had seen, built prototypes of something that kind of looked like it and maybe tweaked some things, hoping to make it perform better, tested it, iterated, and kind of went through this slow guess-and-check process until we got to something that marginally worked."
When it comes to excuses from the front office, Jets fans have heard it all. The beleaguered New York franchise continues to hold the longest playoff drought of all major-league men’s sports teams, a situation which has been blamed on everything from management and coaching to players and locker room culture. Fans have likewise heard all the promises of hare-brained schemes sold as the team’s salvation, from the short-lived Sam Darnold rebuild to the infamous Aaron Rodgers gamble.
Now, the organization has hatched a new plot to snap their historic dry spell: going all-in on AI.
New reporting by the Sports Business Journal revealed the Jets front office has been making a concerted push to embrace AI in their day-to-day work. According to Iwao Fusillo, the Jets’ recently appointed chief data and analytics officer, roughly 91 percent of front office staffers are now daily users of Microsoft Copilot.
“I call that level one, or horizon one, which is adoption,” Fusillo told Sports Business. “Do we have large business gains from that level one? Not really. But have we changed the culture of the entire front office? Yes. To think AI-first.”
During department-level AI workshops led by the digital consulting firm Next League, Sports Business reports staffers “generated” a whopping 60 ideas about where to deploy AI throughout the front office, and “probably double that” for the football side.
Of course, the real question is whether any of those ideas were good. Writ large, it remains a mystery how simply adopting AI is supposed change the depressing reality of life in the Jets organization.
The AI initiative and Fusillo’s appointment are the brainchild of Jets owner Woody Johnson, great-grandson of Robert Wood Johnson, founder of the eponymous Johnson & Johnson. Often described as easily influenced by agreeable toadies and public sentiment, the Jets mogul evidently isn’t aware that the infamously sycophantic tech will probably just tell him whatever he wants to hear. Johnson’s long-suffering fanbase, however, lacks that particular feature.
“Jets finally acknowledging they need to outsource for intelligence as there is none in the building itself,” one Redditor quipped. “We’re going 0-17,” afan wrote on X-formerly-Twitter.
“Lol I asked ChatGPT [to] ‘make the Jets a Superbowl contender’ and the short of it was literally just get rid of any and everybody from the Jets,” one New York Giants fan shared in a Reddit post. “Some of its top recommendations were to change the coaching staff completely and somehow get a top 10 offense by year two.”
AI agents used to be all the rage, the supposed next hit product category after generative AI failed to generate productive returns. Now, the bill on all that hype is coming due.
According to some estimates, up to 79 percent of US corporate execs have some type of AI agent in the making — but one Gartner prediction found 40 percent of these projects will implode due to poor risk controls.
In a nutshell, AI agents are capable of inflicting tremendous amounts of damage on a company when instructed to complete critical tasks. One particularly glaring example, outlined by network consulting engineer Sayali Patil in VentureBeat, involves AI agents designed to fix slow network connections when they detect problems.
That sounds like a reasonable task to automate, like unplugging your router when your wifi starts acting up. But while these AI agents can technically get the job done, Patil says she’s had incidents where they shut down the server while three other important services are handling a rush of web traffic.
When the agent goes ahead and restarts that server anyway, it leads to disaster for those other three services. In the end, the chaotic network event becomes far more disruptive than the initial slowdown. Worse yet, the critical failure becomes too much for the AI tool to understand, or as Patil puts it, a “cascade the agent was never designed to model.”
“The blast radius of that agent action was not the service restart. It was everything downstream of the restart, in a system state the agent had no complete picture of,” Patil writes.
Even if engineers were able to account for every pitfall, AI agents still present some horrifying security vulnerabilities. Stress tests of AI agents equipped with email privileges revealed some major pain points, like where agents obey strangers from outside their network or transfer data to unauthorized personnel.
This gap between performance expectations and production reality is precisely why AI agents aren’t the one-size-fits-all tool the tech industry desperately wants them to be. Whether that changes in the long view is anyone’s guess — but today’s reality is falling way behind the hype.
Imagine this scenario: your algorithm has pulled up a background YouTube video, or maybe a podcast. Unbeknownst to you, hackers have embedded inaudible sounds in it, designed to hijack your smart speaker or phone’s AI assistant — meaning the cybercriminals can now access your private photos, bank accounts, or any other personal information you’ve hooked up to your AI system.
It sounds like an also-ran episode of “Black Mirror,” but it’s exactly what researchers have shown is possible in new research being presented this week at the IEEE Symposium on Security and Privacy.
Basically, a team of researchers in China and Singapore found that they can construct “adversarial audio,” completely undetectable to the human ear, that tricks voice AI models into doing things they shouldn’t. Then it’s a breeze to hide it in innocent-sounding audio — a song, a movie, or anything else that unsuspecting targets might play in the background — and lay in wait for users to accidentally compromise their digital lives.
“It takes just half an hour to train this signal, and then, because this signal is context-agnostic, you can use it to attack the target model whenever you want, no matter what the user says,” lead author Meng Chen, a PhD candidate at China’s Zhejiang University, told IEEESpectrum of the work. “These single-point defenses struggle to resist our attack because we found it’s very hard for these models to distinguish the normal user intent and our adversary attack.”
One catch, at least for now: the technique required the hackers to have access to the full weights of the AI model they’re targeting, meaning they were only able to attack open source models. But because many commercial AI systems are built on open source models, that meant that their exploit was effective against mainstream products by Microsoft and Mistral.
Mistral didn’t respond to IEEE‘s request for comment, but Microsoft issued a statement that should probably give anyone pause before connecting any important information whatsoever to one of the company’s voice AI models.
“We appreciate the researchers’ work to advance understanding of this type of technique,” it read. “This study evaluates model resilience through controlled, direct interactions with the model itself, which helps inform our approach to building model resiliency. In practice, AI models are often integrated into user applications, and we offer developers tools and guidance they can use to implement additional layers of protection that help safeguard users.”
The long awaited Democratic Party “autopsy” of the 2024 election failure has finally been released, and it’s riddled with errors.
Facing mounting pressure to release the report, Democratic National Convention chairman Ken Martin finally relented, sharing an “unfinished’ draft with CNN.
Though Martin caveated to CNN that the report wasn’t ready for public consumption — despite having two years to prepare it — the DNC chair figured the spectacle he created by delaying its release is now more embarrassing than the spectacle that would’ve been created had the party just shared the thing in the first place, as promised way back in 2024.
Indeed, it’s pretty rough-hewn. It’s short on citations and chock full of errors, many of which fit the profile of hallucinations from a large language model like ChatGPT. While a few incongruities are to be expected with any rough draft, some of them are beyond the pale, drawing into question why they would have been included in the document to begin with.
In the postmortem’s analysis of the North Carolina gubernatorial election, for example, CNN points out that the document incorrectly lists Republican candidate Mark Robinson as having won both 45 percent and 42.7 percent of the vote. Neither figure is actually correct: in reality, he won 40.1 percent in his 2024 loss to Democratic candidate Josh Stein.
Numerous names are misspelled, like that of former Kentucky Governor Matt Bevin, listed as Matt “Brevin,” and former New Jersey Governor “John” Corzine, whose name is really spelled without the “h.”
There’s also some inconsistent analysis in the document, like in the case of Washington-state Democratic candidate Bob Ferguson. Some portions of the autopsy heap praise on Ferguson, lauding him for running “on a platform of housing affordability, reducing costs for families throughout the state, and improving public safety.”
Later on in the document, however, it chastises Ferguson for underperforming presidential candidate Kamala Harris. Though Ferguson won his election and Harris lost, the document nonetheless makes it clear that “Ferguson underperformed Harris in Democratic strongholds,” demonstrating that “anti-Trump sentiment alone was insufficient to motivate voters.”
That would be a perfectly reasonable criticism of Harris, but it directly counters the earlier claim that Ferguson won his election by choosing a platform based on affordability and public safety.
Whether any of this was AI isn’t clear. LLMs are notoriously horrid at consistently citing correct numbers. They likewise struggle to maintain cohesive narratives when generating long-form text, which could easily explain the inconsistent messaging around Ferguson’s electoral campaign.
The DNC has not responded to Futurism’s request for comment on the use of AI in the document. Given how long it took them to release the postmortem in the first place, there’s no telling if we’ll ever get our answer — or if Democratic functionaries will learn any lessons from the bloodbath that was 2024.
Login
