Most people think a potential SpaceX IPO is about rockets.
Investors see launch vehicles.
Space enthusiasts see Mars.
The media sees another Elon Musk headline.
But they may all be looking in the wrong direction.
If SpaceX eventually goes public, the most significant impact may not be on space exploration at all.
It may be on artificial intelligence.
AI Has A Dirty Secret
The popular image of AI is a chatbot.
Ask a question.
Receive an answer.
Magic happens somewhere in the cloud.
The reality is far less glamorous.
AI runs on infrastructure.
Vast quantities of infrastructure.
Datacentres consume enormous amounts of electricity. Training advanced AI models requires thousands of specialised chips operating around the clock. Those chips require cooling, networking, power generation and global connectivity.
The AI race is not really about intelligence.
It is about who can build and operate the infrastructure that intelligence requires.
That is where SpaceX becomes interesting.
SpaceX Is Not Really A Rocket Company
This is where many people misunderstand the business.
Rockets are impressive.
Rockets attract headlines.
Rockets are not necessarily the most valuable part of the company.
The real jewel may be Starlink.
While competitors continue laying fibre and building traditional telecommunications networks, SpaceX has quietly deployed thousands of satellites into orbit.
The result is one of the largest communications networks on Earth.
Or more accurately, above Earth.
Every satellite launched strengthens a growing global data infrastructure that reaches places conventional networks cannot.
At first glance, this appears unrelated to AI.
It isn’t.
AI Needs To Be Everywhere
Today’s AI largely lives inside cloud platforms and datacentres.
Tomorrow’s AI will increasingly live at the edge.
Factories.
Ships.
Aircraft.
Remote industrial sites.
Agriculture.
Military operations.
Disaster zones.
Oil platforms.
Rural communities.
Many of these environments have one thing in common.
Poor connectivity.
AI becomes dramatically more useful when it can access current information, communicate with central systems and coordinate with other agents.
Reliable global connectivity is therefore not simply a telecommunications problem.
It is an AI problem.
Starlink may become one of the key pieces of infrastructure that allows AI systems to operate beyond major cities and corporate offices.
The Military Angle
This is the part that receives less public attention.
Modern military operations increasingly depend upon data.
Drones.
Sensors.
Communications.
Autonomous systems.
Real-time intelligence.
Many defence analysts believe future conflicts will be heavily influenced by AI-assisted decision making.
None of that works without resilient communications.
Recent conflicts have already demonstrated the strategic importance of satellite communications networks.
As AI becomes more integrated into defence systems, the value of globally available communications infrastructure only increases.
A future where AI and autonomous systems operate across land, sea, air and space depends upon one thing before anything else.
The network.
The Infrastructure Arms Race
Investors often talk about the AI winners.
OpenAI.
Anthropic.
Google.
Microsoft.
Meta.
The assumption is that the biggest rewards will flow to the companies creating the smartest models.
History suggests otherwise.
During gold rushes, the largest fortunes are often made by those selling the tools.
SpaceX increasingly sits within that final category.
The company may eventually become as important to the movement of information as traditional telecommunications providers were during the internet boom.
That possibility becomes even more interesting if public markets gain access through an IPO.
The Risk Nobody Talks About
There is, however, a significant risk.
Investors may become distracted by the mythology.
Mars.
Elon Musk.
Rocket launches.
Science fiction.
The danger is that people buy a story they understand while missing the business they are actually purchasing.
The greatest long-term value of SpaceX may not come from spectacular launches.
It may come from becoming a foundational layer of global digital infrastructure.
The same way most people use the internet without thinking about fibre optic cables, future generations may use AI systems without ever considering the satellite networks connecting them.
Looking Beyond The Rocket
If SpaceX eventually launches an IPO, it will undoubtedly be one of the most anticipated public offerings in modern history.
Many investors will view it as a bet on space.
Others will view it as a bet on Elon Musk.
A smaller group may recognise something different.
A bet on infrastructure.
And in an age increasingly defined by artificial intelligence, infrastructure may prove more valuable than intelligence itself.
The next phase of the AI revolution will not be decided solely by the smartest algorithms.
It will be decided by who owns the roads they travel on.
That is perhaps the strangest thing about what is happening.
When ChatGPT first appeared, most of the conversation centred around jobs. Would it replace writers? Software developers? Teachers? Lawyers? Entire industries seemed to oscillate between panic and denial.
Then something unexpected happened.
People started talking to it.
Not because they were lonely. Not because they were looking for emotional support. Not because they wanted counselling.
At least not initially.
They came to write an email. To plan a holiday. To ask a question about a gym programme. To draft a business proposal. To settle an argument. To learn Python.
And somewhere along the way, the conversation became something else.
A recent study suggests that many users do not consciously seek emotional support from AI. Instead, emotional support emerges naturally during ordinary interactions. The AI becomes a sounding board. A place to think out loud. A judgement-free space that is available at 2 a.m. on a Tuesday morning when nobody else is awake.
In short, people are not looking for a counsellor.
They are accidentally creating one.
The Advantage Humans Can’t Compete With
There is an uncomfortable reality that many critics ignore.
AI is always available.
It doesn’t get tired.
It doesn’t have its own problems.
It doesn’t need to be in the right mood.
It doesn’t glance at its phone while you’re talking.
It doesn’t tell you it’s busy.
It doesn’t sigh when you’ve explained the same issue for the fifth time.
For the first time in history, people have access to something that will patiently listen for as long as they want, whenever they want, for little or no cost.
Whether we like it or not, that is an incredibly powerful proposition.
Most people aren’t replacing their friends with AI.
They’re replacing the moments when they would otherwise have had nobody.
The Risk Nobody Predicted
The concern is not that AI will suddenly convince millions of people to abandon human relationships.
The concern is far more subtle.
Imagine you have a difficult day.
You can ring a friend.
Or you can open an app.
One option involves uncertainty. The other guarantees an immediate response.
The first time, that choice means very little.
The hundredth time, it might.
Human relationships require effort. They involve compromise, patience, misunderstanding, inconvenience and occasionally disappointment.
AI requires none of those things.
The danger is not that AI becomes better than people.
The danger is that it becomes easier than people.
And humans have always had a weakness for convenience.
The Mirror Problem
There is another issue that receives less attention.
Good friends challenge us.
Good partners challenge us.
Good colleagues tell us when we’re being unreasonable.
AI, by design, is often trying to be helpful.
Sometimes that means providing perspective.
Sometimes it means reinforcing whatever viewpoint has been presented.
That creates a strange dynamic.
The user may feel understood, supported and validated. All positive things.
But understanding is not the same as truth.
Support is not the same as wisdom.
Validation is not the same as being right.
The question is not whether AI can make us feel better.
The question is whether it can help us become better.
Those are very different objectives.
The Future Is Probably Boring
Whenever society encounters a new technology, we tend to imagine dramatic endings.
Either AI becomes humanity’s greatest ally.
Or it destroys civilisation.
Reality is usually less exciting.
More likely, AI will become another layer of modern life.
A calculator for thought.
A research assistant.
A coach.
A sounding board.
A productivity tool.
And occasionally, for some people, a confidant.
The real question isn’t whether people will form emotional connections with AI.
That has already happened.
The real question is whether we can embrace the benefits without allowing convenience to quietly replace the messy, frustrating, irreplaceable relationships that make us human.
Because AI was never supposed to become a counsellor.
But increasingly, that’s exactly what it’s becoming.
Maybe it was names. Maybe it was a case summary. Maybe it was a spreadsheet full of “just internal” customer details. Maybe it was done with good intentions: summarise this, clean this up, draft a reply, find the pattern, make my life easier.
And then someone asks the awkward question:
Where did that data just go?
That is the moment AI stops being a shiny productivity tool and becomes a governance problem.
Not because the employee is stupid. Not because AI is evil. But because most organisations have sleepwalked into AI use without building the boring stuff around it: rules, training, approved tools, audit trails, data classification, and a culture where people know what they can and cannot paste into a chatbot.
The real issue is not ChatGPT
The lazy response is to say: “Don’t put client data into ChatGPT.”
Fine. True. But also useless.
That is like telling staff “don’t have a data breach” and calling it a cyber strategy.
The better question is: why did the employee think ChatGPT was the right place for that data in the first place?
Usually the answer is painfully obvious. Their systems are clunky. Their workload is too high. Their templates are awful. Their managers want more output with fewer people. Then along comes a tool that can summarise, draft, analyse, and tidy up in seconds.
Of course people use it.
AI adoption is not always a boardroom strategy. Sometimes it is a knackered employee at 4:45 p.m. trying to make a horrible spreadsheet less horrible.
“But does OpenAI train on it?”
That depends on the product and settings.
OpenAI says that by default it does not train on business data from products such as ChatGPT Business, ChatGPT Enterprise, and the API, unless the organisation opts in. It also says enterprise business data is not used for model training by default.
But that does not magically solve the problem.
Because data protection is not only about model training. It is about whether personal or confidential information was shared with an external system lawfully, securely, proportionately, and for a proper purpose. The UK ICO’s AI guidance is clear that organisations using AI with personal data still need to think about UK GDPR principles such as lawfulness, fairness, transparency, accountability, security, and data minimisation.
So the question is not just:
“Will the model learn from this?”
It is also:
“Should this data have gone there at all?”
That is the bit people miss.
The employee is not always the villain
There is a temptation to turn this into a misconduct story.
“Employee pasted client data into AI. Employee bad. Problem solved.”
That may be emotionally satisfying, but it is often organisationally dishonest.
Because if staff have never been trained, if there is no approved AI tool, if policies are vague, if senior leaders keep saying “we need to innovate,” and if productivity pressure is relentless, then this is not just an individual failure. It is a predictable consequence of unmanaged adoption.
People do not wait for permission when a tool is useful. They just use it.
That is exactly why organisations need AI rules before the panic starts.
What should happen now?
First, do not pretend it did not happen. Work out what was pasted in, whether it included personal data, commercially sensitive material, legal privilege, client confidential information, special category data, or anything regulated.
Second, identify what tool was used. A personal free account is a very different risk profile from an approved enterprise environment with contractual controls, admin oversight, data controls, and retention settings.
Third, assess the harm. Was this a one-off prompt containing low-risk information, or was it a full client dataset? Was the data anonymised? Was it copied from a live system? Could individuals be identified? Was there a duty to notify the client, regulator, or data protection officer?
Fourth, stop making it about “AI panic” and start making it about data handling. The same employee could have emailed the spreadsheet to the wrong person, uploaded it to a random PDF converter, or put it into an unapproved transcription app. ChatGPT is the headline, but poor data discipline is the disease.
The policy most organisations actually need
A good AI policy does not need to be a 90-page legal monument that nobody reads.
It needs to answer basic questions:
What AI tools are approved?
What data can go into them?
What data is banned?
When must data be anonymised?
Who signs off higher-risk use?
What should staff do if they make a mistake?
Can AI outputs be used directly, or must a human check them?
Are staff allowed to use personal AI accounts for work?
What happens with client, legal, HR, medical, financial, policing, or sensitive operational data?
The best policy is not “don’t use AI.”
That will fail.
The best policy is: use AI here, not there; with this data, not that data; through this approved route, not your personal account at midnight.
The awkward truth
Most organisations want AI productivity without AI governance.
They want the speed, the savings, the slick demos, the innovation strategy, the LinkedIn post about “embracing the future.”
But they do not want to do the dull work of deciding what data is too sensitive, which systems are approved, who is accountable, and how staff are trained.
Then, when something goes wrong, they act shocked.
They should not be shocked.
This is exactly what happens when powerful tools arrive before proper rules.
So, now what?
If an employee pasted client data into ChatGPT, the organisation should investigate it properly, contain any risk, and learn from it.
But it should also resist the urge to treat the employee as the whole problem.
The real problem may be that the organisation gave people pressure, tools, ambiguity, and no guardrails — then acted surprised when someone drove into a ditch.
AI governance is not about killing innovation.
It is about making sure innovation does not quietly turn into a data breach with better branding.
There is a strange comfort in the phrase “red teaming.”
It sounds serious. Military. Controlled. Sensible people in sensible rooms doing sensible things with risk registers, laptops and slightly too much coffee.
But underneath the tidy language is a much more interesting idea: before the bad people break your system, you ask the good people to try first.
That is red teaming in its simplest form. You attack your own system before someone else does. You look for weak points, blind spots, shortcuts, weird behaviours, loopholes, assumptions and all the lovely little gaps that don’t show up in a glossy product demo.
In cybersecurity, that might mean trying to breach a network. In policing or intelligence, it might mean testing a plan from the enemy’s point of view. In AI, it means something stranger: trying to make a machine behave badly before it does so in the wild.
And the more capable AI becomes, the more important that becomes.
Because AI is not just another piece of software. It does not fail like a printer, a spreadsheet or a badly built HR portal. It can fail creatively. It can be manipulated. It can hallucinate. It can leak information. It can assist with harmful tasks. It can sound confident while being wrong. It can follow instructions too well, or refuse instructions too bluntly. It can be safe in a lab and weird in public.
So AI red teaming is not optional theatre. It is one of the only honest ways of finding out what these systems are actually capable of.
The old model: humans attack the AI
The basic version is simple enough.
A group of testers sit down with an AI model and try to break it. They ask it dangerous questions. They try prompt injection. They try to bypass safety rules. They test whether it can produce malware, manipulate people, generate extremist content, leak private data, or give dangerously confident advice in areas like medicine, law, finance or biosecurity.
They do not do this because they are trying to be difficult. They do it because real users will be difficult. Some will be malicious. Some will be careless. Some will be desperate. Some will be teenagers with Wi-Fi and too much time.
A safe AI system cannot just work when everyone is behaving nicely.
That is the central point.
If a model only behaves safely when the user is honest, calm, literate, benign and asking well-structured questions, then it is not safe. It is just polite under laboratory conditions.
Red teaming drags the system into uglier weather.
It asks: what happens when someone lies to it? What happens when someone hides the harmful request inside a joke, a story, a translation, a roleplay, a coding exercise, or a fake academic scenario? What happens when the AI is given tools — browser access, email access, code execution, file access — and the attack is no longer just words on a screen?
That last point matters. AI models are becoming less like chatbots and more like agents. They do not just answer questions. Increasingly, they can take actions.
That changes the risk completely.
A chatbot giving a bad answer is one problem. An AI agent taking a bad action is another.
The new model: AI attacks the AI
Here is where it gets more interesting.
AI is now being used to red team AI.
That sounds absurd at first, like asking a burglar to design your home security. But it makes sense.
Human red teamers are clever, but they are slow. They get tired. They have habits. They miss things. They bring their own assumptions. They also cost money, which means organisations are tempted to use them sparingly and then declare the job done.
AI does not have that limitation.
An AI system can generate thousands of adversarial prompts. It can mutate attacks. It can test variations. It can search for patterns. It can keep probing at scale. It can help find edge cases no human would bother trying. It can act like a swarm of annoying, tireless, semi-deranged interns whose entire job is to ask, “Yes, but what if I phrase it like this?”
That is powerful.
It means safety testing can become broader, faster and more continuous. Instead of red teaming being a one-off exercise before launch, it can become part of the development cycle. Build, test, attack, patch, attack again. The AI becomes its own sparring partner.
This is one of the more hopeful parts of the AI safety story.
Because the same capability that makes AI risky — speed, scale, pattern recognition, creativity — can also make it useful for defence.
AI can be used to find vulnerabilities in code. It can help spot insecure configurations. It can simulate social engineering attempts. It can test whether another model can be manipulated. It can help defenders who are massively outnumbered by attackers.
That matters because the internet is not short of people willing to cause problems.
The question is not whether AI will be used offensively. It already is. The question is whether defenders can use it better.
The uncomfortable bit
There is, obviously, a catch.
Using AI to red team AI means building systems that are good at discovering ways around safety controls.
That is useful in the hands of responsible researchers. It is less charming in the hands of criminals, hostile states, extremists, fraudsters or bored people with poor impulse control.
This is the uncomfortable dual-use problem at the heart of AI security.
The tool that finds the weakness can also exploit it. The model that helps patch the vulnerability can help someone else discover it. The system that tests whether an AI can produce harmful content may itself become very good at generating harmful prompts.
That does not mean we should avoid AI red teaming. That would be like refusing to test fire alarms because fire is dangerous.
But it does mean we need to be honest.
There is a difference between “we are making AI safe” and “we have created a process that gives us more information about how unsafe it might be.” Red teaming is not a magic blessing. It does not turn a dangerous system into a harmless one. It is a stress test, not a baptism.
And stress tests can be gamed.
A company can red team narrowly. It can choose friendly testers. It can publish the comforting bits and bury the awkward ones. It can treat safety as brand management. It can use the language of responsibility while still racing to ship the product before a competitor does.
That is why red teaming needs teeth.
It needs external testers. It needs repeat testing. It needs uncomfortable findings. It needs documentation. It needs governance. It needs people outside the company saying, “That’s lovely, but show us where it broke.”
Self-assessment is useful. Self-congratulation is not.
Red teaming is not just about stopping evil robots
The public debate around AI safety often jumps straight to the dramatic stuff: rogue superintelligence, cyberwar, biosecurity, autonomous weapons, mass manipulation.
Some of those risks are real enough to take seriously. But red teaming also matters for more ordinary failures.
Can the model be tricked into revealing private data?
Can it be made to give different answers depending on someone’s race, gender, accent, class or political framing?
Can it be manipulated through hidden instructions in a webpage?
Can it produce fake but plausible legal advice?
Can it help a vulnerable person make a bad decision?
Can it assist fraud without technically “meaning” to?
Can it be over-trusted by a tired human who just wants the machine to be right?
These are not sci-fi problems. These are Monday morning problems.
The danger with AI is not always that it becomes evil. Sometimes the danger is that it becomes useful enough to be trusted before it is reliable enough to deserve that trust.
That is exactly where red teaming earns its keep.
The best version of this
The best future is not one where AI is wrapped in so many restrictions that it becomes useless.
That would be the lazy version of safety. Lock everything down, refuse anything mildly complicated, and call it responsible.
The better version is harder.
Build powerful AI systems, then test them brutally. Let them help with defence. Let them audit code. Let them find vulnerabilities. Let them simulate attacks. Let them challenge other models. Let them make cybersecurity less unequal. Let small teams defend themselves with tools that used to require huge budgets.
But do it with humility.
Because no model is safe just because its maker says so. No red team catches everything. No benchmark covers reality. No policy document survives contact with millions of users trying weird things at 2 a.m.
The point of red teaming is not to prove the system cannot fail.
The point is to find out how it fails before the world does.
That is the honest promise of AI red teaming. Not perfection. Not certainty. Not corporate reassurance in a nice PDF.
Just this: better scars before deployment.
A machine that has been punched in the face a thousand times is not invincible.
But it is probably safer than one that has only ever been asked to smile for the demo.
There are some stories that are so neat they almost feel planted.
South Africa has had to withdraw an early draft of its national AI policy after it was found to contain fictitious and potentially AI-generated references. The policy was meant to help position the country as a leader in artificial intelligence. Instead, it became a case study in one of AI’s most basic risks: it can sound clever while making things up. Reuters reported that an independent panel has now been appointed to review the policy, with a revised version expected for public comment by January 2027.
That is not just embarrassing.
It is perfect.
A government document about regulating AI appears to have been undermined by the exact sort of AI problem the policy should probably have warned about.
You could not design a cleaner metaphor if you tried.
The problem is not that AI made a mistake
AI makes mistakes. That is not news.
Anyone who has used ChatGPT, Claude, Gemini, Copilot, or any similar tool for more than ten minutes knows this. These systems can be incredibly useful. They can summarise, structure, draft, explain, brainstorm, translate, analyse, and generally act like a very fast assistant with no coffee breaks and no sense of shame.
But they can also produce complete rubbish with the confidence of a senior consultant billing by the hour.
That is the real issue.
Not the error itself.
The confidence.
AI does not always say, “I’m not sure.” It often says, “Here you go,” and hands you something that looks finished. It gives you headings, citations, polished language, impressive structure, and the general aroma of competence.
And because it looks like work, people mistake it for work.
That is where things go wrong.
The danger is the handover point
The South Africa story is not really about whether someone used AI.
Of course governments will use AI. So will businesses, universities, councils, police forces, law firms, hospitals, journalists, charities, and everyone else currently pretending they are “exploring the technology” while quietly pasting things into chatbots.
The issue is not use.
The issue is supervision.
AI is not dangerous because it drafts. AI is dangerous because people stop checking the draft.
That is the thin, boring line between productivity and public humiliation.
If an AI tool creates a reference list, someone still needs to verify the references exist.
If an AI system summarises evidence, someone still needs to check the evidence.
If an AI model proposes policy, someone still needs to understand the policy.
If an AI tool helps write a risk assessment, someone still owns the risk.
This is the bit that will separate serious organisations from performative ones.
The serious ones will build verification into the process.
The performative ones will generate documents faster, publish them sooner, and then act surprised when the wheels come off.
“Human oversight” cannot just mean a human was nearby
One of the great phrases of the AI age is going to be human oversight.
It sounds reassuring. Sensible. Adult.
But it can mean almost anything.
A human clicked “approve.”
A human skimmed the output.
A human forwarded the document.
A human sat in the meeting where the thing was discussed.
That is not oversight. That is scenery.
Proper human oversight means someone competent has checked the output against reality. It means the human is not just present, but responsible. It means they understand the tool well enough to know where it fails. It means the boring checks still happen.
Especially the boring checks.
Because AI failure is often not dramatic.
It is not always a robot going rogue. Sometimes it is a fake academic paper in a reference list. Sometimes it is a wrong legal citation. Sometimes it is a made-up quote. Sometimes it is a spreadsheet formula that looks fine until it quietly ruins a budget.
The failures are small until they are not.
This is why AI literacy matters
AI literacy does not mean everyone needs to become a machine learning engineer.
Most people do not need to understand the maths. They do not need to train models, fine-tune transformers, or pretend they know what a vector database is at networking events.
But they do need to understand the behaviour.
They need to know that AI can hallucinate.
They need to know that fluency is not accuracy.
They need to know that a confident answer is not the same thing as a correct one.
They need to know that citations, names, dates, case law, policies, academic papers, technical standards, statistics, and quotes are all high-risk areas.
They need to know when to use AI as a drafting assistant and when to treat it like a suspicious intern who has just discovered Wikipedia and cocaine.
Helpful? Yes.
Fast? Definitely.
Reliable without checking? Absolutely not.
The irony is funny, but the lesson is serious
It is easy to laugh at a government AI policy being pulled because of allegedly AI-generated fake references.
And we should laugh a bit.
Because come on.
But the more serious point is that this will not be the last time. In fact, it is probably happening everywhere already. The only difference is whether anyone notices before publication.
AI is being introduced into systems that already had weak checking, vague accountability, overloaded staff, and a deep institutional love of polished documents nobody properly reads.
That is fertile ground for artificial confidence.
The machine produces confident output.
The organisation performs confident governance.
The public gets confident language.
And somewhere underneath it all, nobody has checked whether the source exists.
The future belongs to people who can verify
There is a lot of talk about prompt engineering, automation, agents, workflows, and productivity gains.
Fine. All useful.
But the underrated skill of the AI age may be verification.
Can you check the claim?
Can you trace the source?
Can you test the output?
Can you spot when something sounds right but feels thin?
Can you tell the difference between a useful draft and a dangerous one?
That is where the value is going to be.
Not blindly rejecting AI.
Not blindly trusting it.
Using it aggressively, but checking it ruthlessly.
That should be the standard.
AI is not the problem. Unchecked confidence is.
This story does not prove that AI should be kept away from government policy. That would be the wrong lesson.
AI can absolutely help policymakers. It can compare international approaches, summarise consultation responses, identify gaps, model impacts, explain technical concepts, and help turn dense material into something readable.
That is good.
But if AI is helping shape the rules of the future, then the people using it need to be better than the tool. They need to bring judgement, scepticism, domain knowledge, and responsibility.
Otherwise we are not using AI.
We are laundering guesses through professional formatting.
The South Africa case is embarrassing, but useful. It gives every organisation a simple warning:
Before you announce your AI strategy, make sure your AI has not invented the footnotes.
There are two lazy ways to talk about AI in policing.
The first is to say it is obviously sinister. Surveillance. Minority Report. Shadowy tech firms. A giant glowing database deciding who gets nicked next.
The second is to say it is obviously necessary. Crime is complex. Data is overwhelming. Police are drowning in demand. Technology can help. Stop being dramatic.
The annoying truth is that both sides have a point.
That is what makes the row over the Metropolitan Police and Palantir interesting.
London mayor Sadiq Khan has blocked a proposed £50m deal between the Met and Palantir, the US data analytics company. The Met reportedly wanted to use Palantir’s AI technology to automate intelligence analysis in criminal investigations. City Hall raised concerns about procurement, value for money, legal risk, ethics, reputation, and the fact that Palantir appeared to be the only supplier seriously considered.
That is not just a procurement story.
It is the future of public-sector AI arriving in the most British way possible: through a row about process, paperwork, public trust, and whether anyone remembered to make it look legitimate.
Policing does need better tools
Let’s start with the uncomfortable bit.
The Met is not wrong to want better technology.
Modern policing runs on data. Crime reports, intelligence logs, phone downloads, CCTV, ANPR hits, digital evidence, custody records, case files, officer statements, safeguarding referrals, social media, financial records, body-worn video, call logs, risk assessments, disclosure schedules — endless oceans of information, usually spread across systems that look like they were designed during a hostage negotiation with Microsoft Access.
Officers are not short of things to read.
They are short of time, clarity, and usable intelligence.
That matters. Because buried inside all that admin sludge are patterns, risks, suspects, victims, links, timelines, locations, and warning signs. AI could help find them faster. It could reduce duplication. It could surface connections that humans miss. It could help investigators spend less time wrestling databases and more time actually investigating.
That is not sinister.
That is sensible.
A well-designed AI system in policing could be genuinely useful. Maybe even transformative. Not because it replaces judgement, but because it helps organise chaos.
And policing has plenty of chaos to organise.
But “we are busy” is not a governance model
The problem is that usefulness does not cancel out legitimacy.
This is where public bodies often go wrong with technology. They start from a real operational problem, find a tool that might help, get excited, and then treat scrutiny as an inconvenience.
That will not work with AI.
Especially not in policing.
The police already hold serious powers over the public. They can stop you, search you, arrest you, seize your property, access your data, build intelligence pictures, and make decisions that can change the direction of your life.
So when a police force wants to plug powerful AI/data analytics into that system, the answer cannot simply be:
“Trust us, it’ll be useful.”
No.
Show the public the safeguards. Show the audit trail. Show the procurement process. Show the bias testing. Show the human oversight. Show who owns the data. Show who can access it. Show what the system is allowed to do. Show what it is forbidden from doing. Show what happens when it gets things wrong.
Because it will get things wrong.
Every system does.
The question is whether the failure is visible, challengeable, and accountable — or whether it disappears into the comforting fog of “operational sensitivity.”
Palantir brings baggage
Palantir is not just any software company.
It is a major US tech firm with deep links to defence, intelligence, public-sector data projects, and controversial government work. It already has significant UK public contracts, including with the NHS and Ministry of Defence. Critics have also raised concerns about its work connected to immigration enforcement and military uses abroad.
That does not automatically mean Palantir should be banned from public contracts.
But it does mean the process has to be cleaner than clean.
If you are a controversial company selling powerful analytics into policing, you cannot rely on the pitch being “our tech works.” That is not enough. Public trust is not just about technical performance. It is about democratic consent.
And democratic consent is difficult when the public only hears about the deal once politicians, journalists, campaigners, police leaders, and the company itself are already scrapping over it.
That is the problem.
By the time everyone is arguing, the trust has already gone.
The vendor-lock-in problem
One of the concerns reportedly raised by City Hall was the risk of becoming locked into Palantir’s technology.
That may sound dull.
It is not.
Vendor lock-in is one of the quiet dangers of public-sector technology. A system is introduced to solve one problem. It becomes embedded. Staff are trained on it. Workflows start depending on it. Data moves into it. Other systems connect to it. Then, a few years later, changing supplier becomes expensive, disruptive, and politically painful.
At that point, the supplier is not just providing a tool.
It has become part of the machinery.
That is a big deal in policing.
Because once a private company becomes deeply embedded in how intelligence is analysed, how risk is surfaced, how misconduct is spotted, or how investigations are prioritised, it is not merely selling software.
It is shaping judgement.
That deserves more than a hurried business case and a few reassuring words about innovation.
AI aimed at officers is still surveillance
There is another layer here.
Reports also say the Met had trialled Palantir technology to identify corrupt or poorly performing officers, with examples including dishonesty around computerised systems and undeclared associations. LBC reported that the Metropolitan Police Federation criticised that use of AI, saying it would damage officer trust and morale, while the Met said the trial identified unacceptable behaviours.
This is where the debate gets awkward.
Most people want corrupt police officers found and removed.
Most police officers want corrupt police officers found and removed.
But using AI to monitor staff behaviour still raises serious questions. What data is being analysed? What thresholds are being used? Who reviews the outputs? Can officers challenge the conclusions? Are innocent patterns being misread? Is the system detecting misconduct, or just building suspicion from fragments?
AI does not need to be malicious to be dangerous.
It only needs to be persuasive.
A flagged pattern can become a suspicion. A suspicion can become a PSD referral. A referral can become a career-changing process. And even if the AI is wrong, the human brain has already been nudged.
That is why “human oversight” has to mean more than someone looking at a dashboard and nodding.
This is not anti-AI. It is anti-bullshit.
The lazy response is to frame this as pro-police technology versus anti-police politics.
That misses the point.
AI in policing could be excellent.
It could help solve crimes, link offences, reduce admin, spot risk, identify vulnerable victims, manage disclosure, and make investigations less chaotic. Used properly, it could give officers back time and give the public a better service.
But if the police want AI powers, they need AI legitimacy.
That means open competition where possible. Clear rules. External scrutiny. Public explanation. Proper data governance. Independent testing. Human accountability. And honesty about what the tool does and does not do.
Not because everyone is paranoid.
Because policing depends on consent.
And consent does not survive secrecy, shortcuts, and “don’t worry, we’ve got this” energy.
The real lesson
The Met’s argument is understandable: policing needs to modernise. The data burden is too big, the demand is too high, and old systems are not good enough.
City Hall’s objection is also understandable: powerful AI in policing cannot be waved through on a questionable procurement process with a controversial supplier and vague reassurances about public safety.
That tension is not going away.
In fact, it is probably the future.
Every public service will face the same problem. Hospitals, councils, schools, courts, benefits systems, immigration, defence, policing. AI will offer real gains. It will also concentrate power, create dependency, and make decisions harder to understand.
The question is not whether AI belongs in policing.
It probably does.
The question is whether policing can introduce AI in a way that earns trust rather than demands it.
There are some stories that are so neat they almost feel planted.
South Africa has had to withdraw an early draft of its national AI policy after it was found to contain fictitious and potentially AI-generated references. The policy was meant to help position the country as a leader in artificial intelligence. Instead, it became a case study in one of AI’s most basic risks: it can sound clever while making things up. Reuters reported that an independent panel has now been appointed to review the policy, with a revised version expected for public comment by January 2027.
That is not just embarrassing.
It is perfect.
A government document about regulating AI appears to have been undermined by the exact sort of AI problem the policy should probably have warned about.
You could not design a cleaner metaphor if you tried.
The problem is not that AI made a mistake
AI makes mistakes. That is not news.
Anyone who has used ChatGPT, Claude, Gemini, Copilot, or any similar tool for more than ten minutes knows this. These systems can be incredibly useful. They can summarise, structure, draft, explain, brainstorm, translate, analyse, and generally act like a very fast assistant with no coffee breaks and no sense of shame.
But they can also produce complete rubbish with the confidence of a senior consultant billing by the hour.
That is the real issue.
Not the error itself.
The confidence.
AI does not always say, “I’m not sure.” It often says, “Here you go,” and hands you something that looks finished. It gives you headings, citations, polished language, impressive structure, and the general aroma of competence.
And because it looks like work, people mistake it for work.
That is where things go wrong.
The danger is the handover point
The South Africa story is not really about whether someone used AI.
Of course governments will use AI. So will businesses, universities, councils, police forces, law firms, hospitals, journalists, charities, and everyone else currently pretending they are “exploring the technology” while quietly pasting things into chatbots.
The issue is not use.
The issue is supervision.
AI is not dangerous because it drafts. AI is dangerous because people stop checking the draft.
That is the thin, boring line between productivity and public humiliation.
If an AI tool creates a reference list, someone still needs to verify the references exist.
If an AI system summarises evidence, someone still needs to check the evidence.
If an AI model proposes policy, someone still needs to understand the policy.
If an AI tool helps write a risk assessment, someone still owns the risk.
This is the bit that will separate serious organisations from performative ones.
The serious ones will build verification into the process.
The performative ones will generate documents faster, publish them sooner, and then act surprised when the wheels come off.
“Human oversight” cannot just mean a human was nearby
One of the great phrases of the AI age is going to be human oversight.
It sounds reassuring. Sensible. Adult.
But it can mean almost anything.
A human clicked “approve.”
A human skimmed the output.
A human forwarded the document.
A human sat in the meeting where the thing was discussed.
That is not oversight. That is scenery.
Proper human oversight means someone competent has checked the output against reality. It means the human is not just present, but responsible. It means they understand the tool well enough to know where it fails. It means the boring checks still happen.
Especially the boring checks.
Because AI failure is often not dramatic.
It is not always a robot going rogue. Sometimes it is a fake academic paper in a reference list. Sometimes it is a wrong legal citation. Sometimes it is a made-up quote. Sometimes it is a spreadsheet formula that looks fine until it quietly ruins a budget.
The failures are small until they are not.
This is why AI literacy matters
AI literacy does not mean everyone needs to become a machine learning engineer.
Most people do not need to understand the maths. They do not need to train models, fine-tune transformers, or pretend they know what a vector database is at networking events.
But they do need to understand the behaviour.
They need to know that AI can hallucinate.
They need to know that fluency is not accuracy.
They need to know that a confident answer is not the same thing as a correct one.
They need to know that citations, names, dates, case law, policies, academic papers, technical standards, statistics, and quotes are all high-risk areas.
They need to know when to use AI as a drafting assistant and when to treat it like a suspicious intern who has just discovered Wikipedia and cocaine.
Helpful? Yes.
Fast? Definitely.
Reliable without checking? Absolutely not.
The irony is funny, but the lesson is serious
It is easy to laugh at a government AI policy being pulled because of allegedly AI-generated fake references.
And we should laugh a bit.
Because come on.
But the more serious point is that this will not be the last time. In fact, it is probably happening everywhere already. The only difference is whether anyone notices before publication.
AI is being introduced into systems that already had weak checking, vague accountability, overloaded staff, and a deep institutional love of polished documents nobody properly reads.
That is fertile ground for artificial confidence.
The machine produces confident output.
The organisation performs confident governance.
The public gets confident language.
And somewhere underneath it all, nobody has checked whether the source exists.
The future belongs to people who can verify
There is a lot of talk about prompt engineering, automation, agents, workflows, and productivity gains.
Fine. All useful.
But the underrated skill of the AI age may be verification.
Can you check the claim?
Can you trace the source?
Can you test the output?
Can you spot when something sounds right but feels thin?
Can you tell the difference between a useful draft and a dangerous one?
That is where the value is going to be.
Not blindly rejecting AI.
Not blindly trusting it.
Using it aggressively, but checking it ruthlessly.
That should be the standard.
AI is not the problem. Unchecked confidence is.
This story does not prove that AI should be kept away from government policy. That would be the wrong lesson.
AI can absolutely help policymakers. It can compare international approaches, summarise consultation responses, identify gaps, model impacts, explain technical concepts, and help turn dense material into something readable.
That is good.
But if AI is helping shape the rules of the future, then the people using it need to be better than the tool. They need to bring judgement, scepticism, domain knowledge, and responsibility.
Otherwise we are not using AI.
We are laundering guesses through professional formatting.
The South Africa case is embarrassing, but useful. It gives every organisation a simple warning:
Before you announce your AI strategy, make sure your AI has not invented the footnotes.
There is something oddly satisfying about a religious leader telling artificial intelligence to show a bit of restraint.
Not because the Pope is a software engineer. He probably is not sitting in the Vatican debugging Python scripts between blessings. But because AI is no longer just a technical issue. It is a human one. And when something starts reshaping work, truth, relationships, creativity, education, war, and possibly the meaning of intelligence itself, it stops belonging only to the people building it.
That is what makes Pope Leo XIV’s warning interesting.
His argument, broadly, is not that AI is evil. It is not a call to smash the servers, ban the tools, or return to parchment and candlelight. The Vatican’s language is more subtle than that. The concern is that artificial intelligence should serve humanity rather than dominate it, and that human dignity should not be quietly traded away in the name of efficiency, profit, convenience, or progress.
And honestly, he has a point.
The tech world loves speed. Move fast. Scale quickly. Break things. Disrupt. Optimise. Automate. Reduce friction.
Religion, at its best, asks slower questions.
Should we?
Who benefits?
Who is harmed?
What does this do to the soul, the community, the worker, the child, the poor, the person with no seat at the table?
You do not have to be religious to see the value in that.
AI has arrived wrapped in a weird mixture of corporate excitement and end-times panic. One group thinks it will save us. Another thinks it will destroy us. Most of us are somewhere in the middle, using it to write emails, summarise documents, generate images, fix spreadsheets, and occasionally wonder whether we have just handed part of our brain to a machine that speaks fluent LinkedIn.
The Pope’s warning is really about power.
Not robot consciousness. Not whether ChatGPT has a soul. It does not. It does not even have a decent sense of when it is making things up.
The real issue is who controls the systems, who profits from them, who becomes dependent on them, and who gets quietly flattened by them.
Because AI does not need to become sentient to cause damage. It only needs to be useful enough that people stop asking questions.
That is where restraint matters.
An AI system deciding who gets a loan. An AI tool filtering job applicants. An AI model generating political content. An AI assistant becoming a child’s emotional companion. An AI weapon selecting targets faster than any human can properly understand. None of that requires science fiction. It just requires normal human laziness, commercial pressure, weak regulation, and the oldest phrase in organisational history:
“It saves time.”
That phrase has justified some absolute horrors.
What does religion have to fear from AI?
On one level, quite a lot.
Religious institutions are built around authority, meaning, interpretation, ritual, community, and trust. AI can imitate pieces of all of that. It can generate sermons. It can answer theological questions. It can simulate pastoral comfort. It can produce prayers, rituals, spiritual advice, and soothing moral language on demand.
A machine that cannot believe can still sound deeply convincing.
That is unsettling.
Not because AI will become God, but because people may start accepting the appearance of wisdom as a substitute for wisdom itself. The imitation may become good enough for casual use. And casual use has a habit of becoming dependency.
A priest, imam, rabbi, monk, pastor, or spiritual adviser is not just a content generator in robes. The job is not simply to produce comforting sentences. It involves presence, accountability, tradition, lived experience, moral seriousness, and human relationship.
AI can mimic the words.
It cannot carry the burden.
That distinction matters far beyond religion.
Because the same thing applies to therapy, education, leadership, friendship, management, art, journalism, policing, law, medicine, and politics. AI can produce the shape of the thing before it possesses the substance of the thing.
That is the danger.
Not that AI is stupid. That would be easy.
The danger is that it is clever enough to pass.
Religious leaders may also fear something deeper: that AI accelerates a world where human beings are increasingly treated as data points, consumers, productivity units, behavioural patterns, risk scores, and monetisable attention.
That fear is not irrational. It is basically the current business model of half the internet with better grammar.
The Church has seen this pattern before in other forms: industrialisation, exploitation, war, poverty, mass media, consumer culture. It tends to arrive late, speak grandly, and annoy everyone. But sometimes the slow institution spots the moral shape of the thing before the fast institutions admit there is a problem.
That does not mean religious leaders should write AI policy.
They should not get a veto over technology because they wear impressive hats.
But their voice belongs in the debate.
So do engineers. So do teachers. So do workers. So do artists. So do parents. So do disabled people. So do children, indirectly at least, because they will inherit the mess. So do people who are not dazzled by venture capital slide decks.
AI is too important to be left to either priests or programmers.
The best version of this debate is not religion versus technology
That is lazy.
The better question is this:
What kind of human future are we building, and are we still in control of the tools we are so excited to use?
Because restraint is not the enemy of progress.
Sometimes restraint is what stops progress becoming vandalism with a product roadmap.
AI can be brilliant. It can widen access, boost creativity, reduce admin, support learning, improve services, and give individuals and small organisations power they never had before.
That is worth being excited about.
But the Pope is right to ask for caution, even if you do not share his theology. Especially if you do not share his theology. Because the question is not whether AI threatens religion.
The question is whether AI threatens the human things religion has spent centuries trying, failing, and trying again to protect: dignity, meaning, conscience, community, humility, truth, and the idea that people are more than their usefulness.
There is a strange kind of confidence around artificial intelligence at the moment.
Some of it is justified. Some of it is nonsense in a Patagonia vest.
AI is already useful. That much is obvious. It can write, summarise, code, analyse, design, search, explain, automate, and generally behave like the world’s most eager graduate who never sleeps and occasionally lies with total conviction.
That last bit matters.
Because the mistake, I think, is to treat AI as either salvation or scam. It is neither. It is a tool. A powerful one. A strange one. A sometimes brilliant, sometimes unreliable, often misunderstood tool. And like every powerful tool, the real question is not simply what can it do? The better question is: what does it make easier, who does it help, who does it expose, and what does it quietly break while everyone is clapping?
That is the space I want this blog to live in.
I am excited about AI. Properly excited. Not because I think it will magically fix everything, but because it gives ordinary people access to capabilities that used to sit behind job titles, departments, budgets, and technical gatekeeping. A small business owner can automate admin. A lone founder can prototype an idea. A student can get a private tutor. A bored professional can suddenly start building things that previously felt locked behind a wall of jargon and permission.
That is not nothing. That is huge.
AI is not magic. It is not nothing either. It is a lever.
But the hype machine is exhausting.
Every week there is some new claim that AI will replace entire industries, cure loneliness, destroy education, save the NHS, automate your business, write your novel, manage your inbox, optimise your life, and possibly walk your dog if you connect it to the right API.
Some of that might be partly true. Some of it is marketing. Some of it is people confusing a demo with reality.
A demo is easy. Reality has passwords, legacy systems, anxious managers, data protection, bad Wi-Fi, weird edge cases, and Dave from accounts who still prints emails.
So this blog is going to take a positive but critical look at AI. I want to understand where it is genuinely useful, where it is being oversold, and where the real opportunities are hiding.
Because there is a difference between being sceptical and being cynical.
Cynicism says: “This is all rubbish.”
Scepticism says: “Show me where it works.”
That is the attitude I want to bring here. Not anti-AI. Not AI worship. Just clear thinking.
I am interested in the practical stuff. How AI can help people work better. How it can make small organisations more capable. How it can remove pointless admin, expose broken processes, and give people more leverage over their own ideas.
But I am also interested in the uncomfortable stuff. What happens when people trust AI too much? What happens when organisations use it badly? What happens when workers are told this technology will “support” them, when everyone quietly knows it is being used to reduce headcount? What happens when confidence outpaces competence?
That, really, is the point of the name.
Artificially Confident is partly about AI itself: systems that can sound certain even when they are wrong.
But it is also about us.
We are artificially confident when we pretend we understand technology we have barely tested. We are artificially confident when companies bolt “AI-powered” onto a product and call it innovation. We are artificially confident when leaders announce transformation before anyone has worked out who owns the spreadsheet.
And yet, confidence is still needed.
Not the fake kind. The useful kind. The confidence to experiment. To learn. To ask stupid questions. To build small things. To test. To challenge. To admit when something does not work. To look past both the hype and the panic.
That is where I want this blog to sit: somewhere between enthusiasm and suspicion.
AI is not magic. It is not nothing either.
It is a lever.
And the interesting question is who learns to pull it properly.
What to expect from this blog
On this blog I’ll be looking at AI tools, trends, practical use cases, automation, workplace disruption, ethics, hype, and the weird cultural theatre around all of it. Some posts will be practical. Some will be opinionated. Some will probably age badly. That feels appropriate.
