Accountability is the biggest unaddressed challenge for AI implementation.
When one person is able to do too much too quickly, they can create more liability than they can accommodate if something fails.
It is essential that a human is responsible for the utilization of any AI output in the real world, but that is not enough. For our own sakes, we must find ways to minimize the tech-debt bankruptcy blast-radius of those who would utilize (knowingly or unknowingly) AI to create flawed systems upon which others rely.
An example: Jim vibe-codes an extremely popular micropayments app. He hires a few people and sees the company as the WhatsApp of money -- a few engineers and some agentic support staff. It pulls in a few million in VC money -- enough to draw in tens of millions of users. One day, a flaw in the infrastructure causes all of the users' unsalted banking information to be released.
Agentic AI allows that entire list of customers to be exploited rapidly, so the losses for society are in the tens of billions. Jim's company is immediately bankrupt, of course, but there are only a few million dollars to go around.
Today, most of Jim's incentives are to go ahead and build that app. The same is true for his few employees and a small VC contribution. There's not much capital at risk compared with the societal exposure.
How do we ensure that AI users are accountable not just for their actions, but for the size of the risk-exposure that they create?
An exploding job-recruitment offer might not attract the kind of folks we want designing a system that absolutely must work after a decade in space.
I've worked with NASA and ESA employees/contractors who've made technical miracles happen in space. I don't think any of them would be drawn to this style of recruitment.
Furthermore, there are numerous allegations that the documents that have been released contain CSAM, which (referencing the PDF above) may fall afoul of 18 U.S.C. 2252–2252A.
In addition, one need only glance at the action in US courts to see egregious violations of the Constitution and valid court orders playing out daily.
The US administration is, at present, regularly violating the law and ignoring court orders. Indeed, these very releases are patently in violation of multiple federal laws -- they're simultaneously insufficiently-responsive to meet the requirements of the law requiring the release of the files and fall afoul of CSAM laws by being incompletely redacted.
The challenge, as we're all experiencing together, is that the law is not inherently self-enforcing.
Scientists use pairs of satellites to map the small variations in Earth's gravitational field. It is possible to see groundwater depletion and changes in distribution of glacial ice, among many things.
The primary challenge in determining the mass of Earth is actually measuring the gravitational constant, G, itself. Everything else involved is known at much higher precision. The product of G and Earth's mass is known to two parts in a billion, but the uncertainty in G is ~22 parts per million.
LISA is primarily sensitive to time-varying gravitational gradients on timescales of a fraction of a minute to a few hours and won't be terribly useful for determining the orbits of objects in our solar system. (but it is very, very cool).
It is likely that many airlines accommodate people who are being deported as regular passengers on outbound international flights.
For movement of humans at industrial scale, though, there are only a few operators with ICE contracts. Avelo, GlobalX, Eastern, Key Lime Air, and Omni come immediately to mind. For international flights, there's at least one Learjet operator that flies a bunch for them, too.
Who decides what behaviors we should be nudged toward? Is it us, or someone else?
To me, one of the greatest dangers of the present moment is that we can't tell whether the LLMs are being asked to give subtly biased answers (or product-placement) on some questions. One cannot readily tell from the output.
For as long as Backblaze has been doing this and at this level of quality, I have no doubt that these reports are good for business.
(As an anecdotal example -- I first heard about Backblaze from these reports many years ago and have relied on them to an extent in selecting new drives. I'm now a Backblaze customer.)
If not now, soon, the bottleneck will be responsibility. Where errors in code have real-world impacts, "the agentic system wrote a bug" won't cut it for those with damages.
As these tools make it possible for a single person to do more, it will become increasingly likely that society will be exposed to greater risks than that single person's (or small company's) assets can cover.
These tools already accelerate development enough that those people who direct the tools can no longer state with credibility that they've personally reviewed the code/behavior with reasonable coverage.
It'll take over-extensions of the capability of these tools, of course, before society really notices, but it remains my belief that until the tools themselves can be held liable for the quality of their output, responsibility will become the ultimate bottleneck for their development.
When one person is able to do too much too quickly, they can create more liability than they can accommodate if something fails.
It is essential that a human is responsible for the utilization of any AI output in the real world, but that is not enough. For our own sakes, we must find ways to minimize the tech-debt bankruptcy blast-radius of those who would utilize (knowingly or unknowingly) AI to create flawed systems upon which others rely.
An example: Jim vibe-codes an extremely popular micropayments app. He hires a few people and sees the company as the WhatsApp of money -- a few engineers and some agentic support staff. It pulls in a few million in VC money -- enough to draw in tens of millions of users. One day, a flaw in the infrastructure causes all of the users' unsalted banking information to be released.
Agentic AI allows that entire list of customers to be exploited rapidly, so the losses for society are in the tens of billions. Jim's company is immediately bankrupt, of course, but there are only a few million dollars to go around.
Today, most of Jim's incentives are to go ahead and build that app. The same is true for his few employees and a small VC contribution. There's not much capital at risk compared with the societal exposure.
How do we ensure that AI users are accountable not just for their actions, but for the size of the risk-exposure that they create?