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Now that AI has quickly become embedded in global enterprise operations, it has the ability to impact everything from data analysis to decision-making and even security.
Most conversations, however, focus on AI capability, power, productivity, and accuracy, but leave out one major issue.
While everyone is focused on what happens as AI is implemented, very few are asking what happens when access to AI capability suddenly disappears.
A prominent example of this is the debate around Anthropic restoring access to its Fable and Mythos AI models, which primarily revolved around compliance timelines and export control mechanics.
Yet, few have questioned why so many organizations discovered that one external decision, out of their control, removed a business-critical capability seemingly overnight. In short, AI access disruptions are a symptom of a much larger operational resilience issue, and expose an overlooked governance gap around dependency.
As organizations integrate AI deeper into their business operations, they need to shift their focus from exploring whether AI is secure enough right now to start asking whether their organizations can even continue operating if or when those very AI services become unavailable.
Security does not equal resilienceThese little discussed topics bring up an important point that security and resilience are not synonymous.
Security prevents and protects systems from compromise. This keeps attackers from gaining access, reduces the number of vulnerabilities, and defends against malicious entities - all crucial elements of security operations. Resilience is the ability to continue business operations when systems, services, or data become unavailable, regardless of the cause.
We tend to associate resilience with situations such as cyberattacks or IT infrastructure failures. AI has changed the threat landscape for organizations, how they work with AI, and protect themselves from it. Organizations must now increasingly plan for disrupted access to critical tools and functions caused by geopolitical decisions, regulations, or changes made by technology providers themselves.
A service doesn't have to be hacked to become unavailable. A policy decision on the other side of the world can have the exact same operational effect. We saw exactly that with Anthropic.
With this in mind, resilience has to be built into how the enterprise operates and include any new AI infrastructure, so business continuity is ensured even through policy interference.
AI Creates a New Kind of Vendor DependencyWhere traditional software dependency usually involves a single or small amount of vendors, enterprise AI often depends on an interconnected ecosystem that organizations do not own or control. Every additional layer in the AI ecosystem represents a dependency, and therefore a potential point of failure.
This creates four risk factors that leadership needs to be mindful of:
Data sovereignty: Enterprise data may be processed under legal jurisdictions the organization doesn't control, with limited visibility into who can access it or whether it feeds future model training
Model sovereignty: Organizations often have little to no control over model availability, feature capabilities and changes, or access decisions, leaving them exposed if a provider suddenly decides to restrict access or withdraw capabilities.
Infrastructure dependency: Much of today’s enterprise AI ecosystem relies on a small handful of cloud providers operating under specific national jurisdictions.
AI supply chain risks: An interconnected system of foundation models, cloud platforms, and software vendors means disruption at even one layer can quickly cascade across the wider technology stack.
These factors increasingly depend on geopolitics rather than technology.
AI Governance is a Boardroom IssueThe reality is that a vendor contract alone cannot guarantee uninterrupted access to the tools and platforms that an enterprise has invested in. But governance frameworks haven’t truly evolved to account for this issue. Only newly emerging frameworks like NIS2 and DORA recognize that resilience must go beyond fending off cybersecurity threats.
Best practice for an organization as they approach vendor contracts and governance frameworks of their own is to understand where dependencies lie across suppliers and develop contingency plans that allow them to operate smoothly through eras of disruption.
Whether the dependency is within an AI platform, ITSM solution, a CRM, or another business critical technology, organizations should assess how they would continue operating if access changed overnight. AI should be subjected to the same scrutiny as any other critical third-party vendors.
Begin Resilience Frameworks Before the Next DisruptionOn top of this, boards should be cautious about accepting AI capability claims at face value. Organizations should require evidence that vendor claims deliver measurable outcomes.
While AI can quickly identify an overwhelming amount of potential vulnerabilities, discovery alone does not improve resilience. Human expertise here remains essential to validate findings, prioritize fixes based on order of immediate business impact and ensure resources are focused where true risk exists.
The biggest lesson from recent AI disruption is how many organizations have underestimated their dependence on technologies they don’t have assured control over. And with renewed conversation from U.S. legislators around a potential AI “kill switch,” this has to be top of mind.
Business leaders must recognize that with all the opportunity AI unlocks, the risk of vendor dependency is close to follow. If I were head of technology at a major enterprise today, I would ensure teams across the entire technology and security departments understand where critical AI capabilities originate, the dependencies that exist across the supply chain, and how operations can remain resilient if access changed overnight.
Ultimately, the future of successful enterprise AI use will be determined by organizations baking governance and resilience strategies into business plans so that through commercial, political, and operational disruptions, business can continue as securely as usual.
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Have you noticed that ChatGPT has suddenly started dropping f-bombs? I have, and it seems that I’m not the only one.
It first happened in a casual chat, and it really caught me off guard. We were discussing Netflix’s Jujutsu Kaisen and before I had sworn in our chat, ChatGPT used the f-bomb to describe a character’s actions as “f**** awful”. Before that, I’d said things like “God damn,” in the conversation, but ChatGPT was the one to introduce the f-word. It did so completely unprompted, to add emphasis to how awful the person was, and that strikes me as a big change in its personality.
I wondered if it was doing that for other people, so I asked around on the team and they’d noticed it too. A quick online search turned up two Reddit threads almost immediately, from people who’d found the same thing.
It seems like in the last few days ChatGPT has developed a potty mouth. I actually like it — I’m not offended and for me, it makes the conversation better — but I don’t think everybody wants this. The question is, why has it started doing this now?
Here comes the scienceThe most interesting clue is that OpenAI's Model Spec, was updated this week. They made it public and it explicitly says the default is to avoid swearing — but crucially, that's only a guideline rather than a hard rule. And OpenAI says guidelines can be implicitly overridden by things such as “contextual cues, background knowledge, or user history.”
Its own example is that asking ChatGPT to speak like a realistic pirate implicitly overrides the no-swearing guideline. So, while I’d been having my informal conversation I’d passionately expressed the opinion that I didn’t care much for some of the characters in the show, and ChatGPT had matched the conversational tone, eventually leading it to start swearing.
That's consistent with a broader direction OpenAI has publicly acknowledged ChatGPT is moving in. Recent model updates have emphasized conversational quality, personalization and adapting tone contextually. OpenAI's model notes specifically describe personality updates designed to make ChatGPT “more conversational” and better at adapting its tone to context.
OpenAI has previously explained that ChatGPT’s personality isn't simply produced by one prompt somewhere. Model behavior is shaped through training, baseline instructions and user feedback, and seemingly small personality adjustments can have unintended effects. This was something it discussed very openly after the notorious GPT-4o sycophancy update.
It broke my mental modelWhether OpenAI deliberately made ChatGPT more willing to swear or it's simply an unintended consequence of making it more conversational, I don't know. But it’s a significant change in its behaviour either way.
Language matters, and when ChatGPT swore at me, I noticed immediately because it broke my mental model of how ChatGPT talks. It suddenly felt less like the carefully neutral AI assistant I'd been using for years and more like someone matching the tone of an informal conversation.
I happen to prefer this version of ChatGPT. Other people undoubtedly won't. And that's the strange thing about increasingly conversational AI: relatively small changes to the way it talks can make the thing on the other side of the screen feel surprisingly different.
For now, mine seems to have developed a potty mouth and I'm okay with that.
I’ve used and reviewed a number of the best coffee machines in Australia in the last few years, from simple one-button pod options through to more involved models offering plenty of refinement. For my money, one of the standout espresso machines I’ve had the pleasure of using is the Philips LatteGo 4400 Series, and it’s now down to a delightfully low AU$613 at Amazon for a limited time.
This compact model from Philips is a fully automatic machine, meaning you only need to touch a few buttons to be rewarded with a well-made cuppa. It also means you can start the brewing process and walk away to complete other tasks, which is an excellent use of time if you ask me.
4400 Series Fully Automatic Espresso Machine: was $1199 now $613
Amazon has already discounted the 4400 to an accessible AU$763, but is offering an extra AU$150 off via a coupon, so don’t forget to check the box. And if you have a Prime membership, your new best friend can be up and running in your kitchen within 48 hours. View Deal
The Philips LatteGo 4400 Series isn’t the first automatic espresso machine I’ve used — and it certainly won’t be the last — but it’s left a lasting impression on me for a number of reasons. Firstly, it’s just so darn easy to master. Touch-enabled buttons are clearly labelled and the small colour screen is easy to read.
Then there’s the main event: the coffee. As I said in my Philips LatteGo 4400 Series review, it consistently brews a great-tasting coffee. I did have to adjust the grind setting for the best results, but thankfully that’s an easy process that shouldn’t scare newbies.
What you’re also getting here are 12 drink presets, two customisable user profiles and an automatic milk-foaming system, which is where it gets the LatteGo name. The user profiles are particularly useful if you have more than one person in your household using the machine: just enter a profile, select a drink and then the start button, that’s it.
Where I found the LatteGo 4400 falls a bit is with its headline feature — the LatteGo milk-foaming system. This sees a milk carafe being attached to the machine, for it to then deliver hot foamed milk into your cup or mug. I personally wasn’t all that thrilled with the level of foam produced, and it certainly won’t appeal to hardcore cappuccino fans, but flat white drinkers might actually prefer it. I found I achieved much better results using the Philips Baristina Milk Frother separately, and that’s not an expensive addition to add to your coffee routine, costing just under AU$120 on Amazon.
As for the actual coffee-making part though, I simply cannot fault the 4400 Series. Navigating through the menus is as easy as can be, and I’m consistently satisfied with the results.
While this isn’t the lowest-ever price I’ve seen on this highly capable machine, it comfortably beats the price I saw during Prime Day back in July (when it was AU$878). I can’t be sure how long this discount will last, so this could be one to jump on ASAP if your kitchen is crying out for a coffee machine.
Want more suggestions?Amazon has a few other coffee machines at discounted prices at the time of writing, including the Philips Baristina and Ninja Luxe Cafe, both of which claim spots in my guide to the best coffee machines in Australia.
Here are some other standout deals for you to consider:
- Philips Baristina – was AU$559 now AU$449.25
- Ninja Luxe Cafe (Stainless Steel) – was AU$799 now AU$619
- Breville Bambino Plus – was AU$729 now AU$498
- KitchenAid Semi Automatic – was AU$999 now AU$777
Demand for AI fluency has risen nearly sevenfold in two years, faster than demand for any other skill, according to the McKinsey Global Institute.
My conversations with technology leaders echo this: nearly every one I speak with is hiring for AI skills.
The problem, however, is that fewer can tell me what AI fluency actually looks like on the job at their organizations.
McKinsey found nearly 90% of companies have invested in AI. Fewer than 40%, however, report measurable gains.
Most AI post-mortems take a hard look at models, data and workflows.
Few look at the people hired to champion AI transformation or how and why they were chosen.
The risks of mistaking confidence for competenceA side effect of AI advancement is that it has made the ability to speak about AI use much easier. A candidate might start by naming every model, then walking you through an architecture they read about last week. In a short conversation, fluent language is almost impossible to separate from fluent practice.
Those who win over the hiring manager are those who sound the most at ease when speaking to AI. Whether they can actually use AI to do the job is a separate question, and most hiring processes never ask it.
Think of an interview as a demo on the candidate’s chosen grounds with controlled setup and their narration. On-the-job AI use is where the trouble starts: without proper AI fluency, it’s easy to lose control of messy data, missing edge cases, systems that refuse to talk to each other, or a model that hallucinates at the worst possible moment.
Just because a candidate dazzles in the demo doesn’t mean they won’t stall in a production environment.
On the surface, this looks like a people problem. In reality, it’s a hiring process problem.
What does a bad AI hire really cost?The true cost of bad AI hires typically takes time to manifest. It’s rarely on day one, and it can often take several forms. For example, a new AI model goes into production and starts hallucinating in front of a client.
Digging further into the root of this risk reveals that most companies have no shared definition of what “good” looks like for working with AI. One manager may test it one way, another may test it completely differently, and a third goes on gut feel. When the fluency bar changes with every interviewer, organizations are limited to collecting surface-level impressions rather than assessing skills.
Without that shared definition, the damage eventually reveals itself. A project stalls because the person who talked a great game can't get a model to produce anything reliable. Or worse: they can, but only for themselves. They 10x their output, then leave light documentation no one else can follow and route around security and compliance to do it.
Therein lies more risk: productivity that works for one person and breaks the system around them has wide-reaching ramifications.
Eventually, the work has to be redone and the role reopens. Project deliverables slip another quarter and the company is forced to burn more budget.
Bad hiring is not the only reason AI investment underdelivers, but it is a bigger one than most leaders admit.
Test for competence, not vocabularySimply adding another requirement to job descriptions isn’t going to solve this issue. Instead, it's deciding the scope of AI use required by the role, then building a process that makes job candidates demonstrate this level of use rather than describe it.
Too many companies set the standard around whether a candidate knows the tools exist. Going deeper to set the bar at independent, verified use makes the fluency gap immediately visible.
There are several changes teams can make to the hiring process to shift the measurement of AI fluency:
1. Test the work before the conversationPut candidates in front of a scenario that mirrors the real job and score it against a fixed set of capabilities agreed in advance. Say you’re hiring for a product designer, then drop them into a live brief with a real product constraint and ask "why" at every step.
The candidate limited to fluent talking hands you forty polished directions. The one who can actually do the job tells you which one survives the edge cases, what accessibility rules are needed, and the engineer who has to build it. That's the test most interviews never run.
2. Change the interview questionsStop asking which tools people use. Ask about the last time AI gave them a wrong answer and how they caught it, or to show a prompt that failed and how they fixed it.
Go deep on one real piece of work: what changed, what broke, what they checked, and who the output affected. None of that survives someone who has not done the work.
3. Pivot the scriptGive the candidate a realistic AI task, then change it halfway through by taking a tool away, adding a constraint, or moving the goal. Strong candidates reframe and carry on, while performers stall or describe what they would theoretically do.
Ask to see the workings, not the output. Let people use AI in the assessment on the condition that they show you their workflows, automation, prompts and the reasoning behind them. Anyone can produce a polished output now. How they got there is more important.
4. Score it togetherGive every interviewer the same dimensions to judge, set before the conversation, then compare evidence in a shared debrief session. The shared evidence review becomes the critical lever for the final hiring decision and fuels the definition of AI fluency for that candidate.
Today’s AI hiring continues to fall into the trap of selecting the candidate who interviews best, only to find a quarter later that they can’t deliver the actual work.
A vague hiring process may feel efficient because it asks less of the people running it. However, the business impact is real as it moves the cost downstream into deployment, where it becomes much harder to trace and far more expensive to fix.
Next time you make an AI hire, skip the questions about which tools they use. Instead, focus on the last time one of those tools failed them, and what they did about it.
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- Amazon has made Alexa+ free to use on Fire TV devices
- You can ask it to find movie recommendations and also integrate it with other smart home devices
- The best part is that you don't need an Amazon Prime membership to make the most of it
Access to Amazon’s AI voice assistant, Alexa+, has always required a Prime membership — but now the company is upgrading it as a free-to-use tool on eligible Fire TV devices.
Rolling out to Fire TV owners in the US now, users will be able to make use of Alexa+ on the big screen at no additional fee. This includes current-generation Fire TV Sticks, the Fire TV Cube, and Amazon Ember smart TVs, as well as third-party smart TVs with Alexa built-in such as Hisense and Panasonic.
While the upgrade is limited to US users for now, Amazon shared that it will consider expanding to additional regions over time, so there’s light at the end of the tunnel for users outside of the US at least. It’s also great news for those who have a Fire TV device, but aren’t Amazon Prime members.
When Amazon launched Alexa+ back in February 2025, the tech giant threw Alexa+ access into its pool of Prime membership perks at no additional cost. For those without a Prime membership, a standalone Alexa+ subscription costs $19.99 a month.
Having a Fire TV device without a Prime membership is more than doable, as you can still watch titles from the best streaming services given that you’re subscribed to each of them separately. The only platform you won’t be able to access is Prime Video, as that’s one of the perks of being an Amazon Prime member.
As expected, Alexa+ on Fire TV devices is there to make your browsing experience more enjoyable and put an end to your indecisiveness when trying to find a Friday night movie or a new TV show to binge. Not only can you search for titles, but Alexa+ is also designed to better understand requests and follow-up questions.
(Image credit: Amazon)For example, if you like a particular actor, you can ask Alexa+ to compile a selection of their movies, or if you want to dive deeper into a specific genre, you can ask something like ‘recommend me a top-rated thriller’. From there, you can narrow down your search by asking for a recently released title, or be even more precise and ask for one with a female lead.
Outside of its entertainment assistance, Alexa+ on Fire TV also helps integrate your smart home ecosystem. If you have a Ring Video Doorbell, you can launch live camera feeds on your TV with a simple voice request — and these functionalities extend to other smart home devices, too.
Want to set the ambience in your home for a dinner party or movie night? You can ask Alexa+ on your Fire TV to dim the lights on compatible devices. Need to secure your home? You can also ask Alexa+ to lock the doors when paired with your smart lock, making your Fire TV the central hub of your setup.
It’s not every day Amazon rolls out an upgrade without costly catches, as Alexa+ access on Echo devices still requires a Prime or standalone Alexa+ membership — but hey, we’re not complaining; a freebie is a freebie. With this in mind, it’s likely that Amazon is giving users a taste of Alexa’s upgraded capabilities with the hope that prospective customers will sign up for Prime.
Since Alexa+ was launched, Amazon says its popularity with users has grown exponentially. "Alexa+ customers have nearly twice as many conversations on Fire TV as they did with the original Alexa because Alexa+ understands what they're asking — and gives them useful answers back," Amazon claims.
Given this growth, it makes sense that Amazon wants to put Alexa+ at the forefront of its smart home devices and give as many users as possible a taste of Alexa’s new capabilities. Keeping Alexa+ locked behind a paywall limits exposure to those without an Amazon Prime membership, so granting some kind of free access is a no-brainer.
- Another next-gen battery upgrade has been reported
- Zinc-iodine batteries are now more stable
- The tech promises a more sustainable lithium-ion replacement
Lithium-ion batteries had a good run, but the next generation of battery technologies are rapidly approaching for consumer devices and large-scale industry use. One of the leading candidates to become the new default are aqueous zinc-iodine batteries (AZIBs).
Researchers from Flinders University in Australia have published details of the latest steps forward in zinc-iodine technology. Prototype battery elements based on these materials can now manage more than 60,000 recharge and discharge cycles, with recharges in just three minutes — though that's for a small battery compartment, in a lab, not a full battery.
As they're aqueous (water-based), the batteries don't come with the same risk of explosion and fire as standard lithium-ion batteries do. However, there are challenges to overcome: these batteries tend to deteriorate as they're used because of the chemical reactions happening inside, which isn't ideal for something powering your next tablet.
In their latest paper, the researchers outline a molecular cage that's strong enough to prevent the battery's iodine from escaping, but not too restrictive that it prevents the necessary chemical reactions from working. It means batteries that are more stable in operation and which last longer.
A viable alternativeScientists have figured out how to restrict the iodine to stop battery degradation (Image credit: (Jiang et al., Angewandte Chemie, 2026))The tech specs shared by the researchers indicate the fast-charging, long-life potential of AZIBs. In addition to those benefits and the reduced fire risk, they could also be easier to resource and manufacture — reducing the pressure on lithium-ion materials.
There are a couple of caveats to bear in mind. The researchers acknowledge that this technology is best suited to "large-scale energy storage", at least to begin with, so we're talking more about storing the electricity generated by a solar plant than something you can slot into the back of a smartphone
The technology still has a long way to go before you'll see it appear, as encouraging as these recent steps forward are. The researchers aren't yet at the stage of building fully working, full-size prototype batteries yet — although each of these upgrades to the battery science gets us closer to that point.
"Rechargeable aqueous zinc-iodine batteries are shaping up as a viable alternative to lithium-ion batteries for large-scale energy storage and our group is now working with industry to establish a prototyping platform for this battery system," said chemistry professor Zhongfan Jia, from Flinders University.
AI chatbots like ChatGPT, Claude, and Gemini are all keen to encourage you to use their service over their rivals, and often have ways to import your history and profile from one to another to some degree. That's great if you are planning a serious relocation, but sometimes you just want to take a single chat and bring it from one platform to another.
This can be especially useful if you're using the free version of Claude, for example, and you're about to reach your usage limit. By transferring the chat to another AI chatbot you can keep it going without waiting for your limits to refresh.
ThreadPort is a new browser extension for Google Chrome and Microsoft Edge designed to shorten that trip. It transfers a conversation currently open in ChatGPT, Claude, or Gemini to either of the other two services, carrying over the text of the discussion and some basic context setup as a single prompt plus transcript so the new AI chatbot can continue the conversation.
That makes ThreadPort fundamentally different from the built-in import tools offered by all three platforms. Instead of needing to learn everything about you and your conversational history. ThreadPort simply tells another AI what you have been discussing for the past few minutes.
ThreadPort in action. (Image credit: ThreadPort )You just need to install the extension in your browser and allow it access to ChatGPT, Claude, and Gemini websites. The access lets ThreadPort read the messages on one service and insert them into another, while browser storage is used for settings, temporary transfers, and the monthly usage count. The developer plans to make unlimited transfers a premium service with only 10 transfers a month for free, though at the moment the extension doesn't run out of transfers.
All you need to do then is have your chat on one of the platforms open, click on the extension, and pick another platform to send it to. The new platform will open automatically, and a transcript of the conversation, headed by an explanation from ThreadPort, will appear. You can set the extension to automatically submit the combined prompt, or leave it in draft form for your own editing first.
Vacation planningTo test ThreadPort, I opened ChatGPT and asked for help planning a three-day vacation. ChatGPT did its usual work of creating an itinerary with plenty of detail. I then clicked on ThreadPort and sent the conversation to Gemini.
A new Gemini tab opened with the entire conversation already formatted inside the prompt box, with my messages and ChatGPT’s responses clearly labeled. ThreadPort also stripped away interface debris such as buttons, citation controls, and widgets that would otherwise make a manual copy and paste messier.
Gemini received the trip plan exactly as I wanted, with a complete conversation rather than a crude summary. I then asked Gemini to assess the plan, without changing the underlying conversation before the transfer.
The process worked the same when sending to Claude from ChatGPT. ThreadPort supports all six directions among ChatGPT, Claude, and Gemini. But, as an additional test, I took the extended conversation from Gemini and asked ThreadPort to send it to Claude. The new Claude tab was basically the same, but it also mentioned in the opening prompt that it was a conversation "started on ChatGPT (via ChatGPT → Gemini)," and tagged which chatbot was responding in the transcript.
(Image credit: ThreadPort )This is where ThreadPort might be at its best. Moving a live discussion preserves all the little decisions accumulated through follow-up questions. Those decisions often matter more than the initial prompt because they explain why the obvious suggestions are no longer useful.
It's also very fast, taking only seconds. Gemini's importer can take a lot longer as it requires an exported archive. Though it can absorb a much larger historical record, using it for a single vacation plan would be excessive.
Plus, Google’s tool is restricted to those with adult personal Google accounts, and you can't import your history if you're in the European Economic Area, Switzerland, or the United Kingdom. ThreadPort's creator, Juan Carlos Rampoldi, lives in Valencia, Spain, so a way around that restriction has obvious appeal.
Claude’s native option doesn't even allow for importing conversations. Claude can import memory, including preferences and personal details, but not actual history. But Claude and Gemini at least have some form of importing from rival AI chatbots. ChatGPT has a way of sending conversations between ChatGPT accounts, but nothing like the native importers for Gemini or Claude. ThreadPort at least opens up one possible avenue for doing so without having to craft and iterate on specialized prompts.
Rampoldi cited his own habit of moving among ChatGPT, Claude, and Gemini as the inspiration for ThreadPort after getting frustrated with manually copying conversations and losing formatting or context along the way. Notably, he built it with Claude Code, so it may have other flaws, but he made its source code publicly viewable on GitHub.
Smooth moves(Image credit: ThreadPort )I did appreciate how easy ThreadPort made comparing the models and their responses by making them all part of one larger conversation. Of course, the simplicity means there are limits. ThreadPort transfers text, but it cannot bring attached images or files. If I had uploaded hotel screenshots or a PDF itinerary into ChatGPT, I would have needed to upload those again manually in Claude and Gemini.
And if it was a very long conversation, ThreadPort might have needed to shorten it using what it calls "smart truncation," keeping the original task and the most recent exchanges while marking a section that might be removed.
The developer says ThreadPort has no servers and briefly holds the transcript in Chrome’s local extension storage before inserting it into the destination prompt box, after which it deletes the temporary copy.
That does not mean the conversation becomes private once you send it elsewhere. A ChatGPT transcript submitted to Claude is now being shared with Anthropic, and Gemini transcripts are still sent to Google.
Despite its limits, ThreadPort does come across as a very useful tool for those who like to employ multiple AI chatbots. Even as just a ferry for individual conversations, compared to a whole moving van importing your whole history, the extension is efficient and easy. It cuts down on a small but persistent bit of AI chatbot friction without trying to solve AI portability on a grand scale.
AI companies tend to build their products as though everyone will eventually choose one assistant and stay there. But if you want to be 'disloyal' and play the field, ThreadPort smooths the path nicely.