Don’t try this at home (source: RecipeNinja)
⇡#This vibe is bad, no good.
Vibe coding — programming almost by intuition, which involves consistent, iterative consultations with a suitable AI bot until the program text created with significant participation of the generative model works approximately as the operator originally intended — is becoming more and more widespread. It is not surprising: experts from The Wall Street Journal note that even in the US, IT employees no longer feel like a chosen caste, as they did just a couple of years ago — against the backdrop of ongoing staff cuts even in the largest specialized companies, tightening requirements for employees (including forced return from remote work so beloved by coders) and the disappearance of all sorts of pleasant bonuses in the workplace — such as the abolition of the dress code that irritates zoomers or the issuance of free memberships to luxury gyms. So if any person who is more or less familiar with the term “programming” can now, after consulting for an hour (okay, even half a working day) with Claude or ChatGPT, present their manager with a properly functioning code, which, in addition, the same AI has run through again just in case for hidden bugs and potential information security vulnerabilities, is that so bad? The author of the term vibe coding, Andrej Karpathy, former head of the Autopilot Vision division at Tesla and one of the co-founders of OpenAI, described this method more than enthusiastically – as “a new kind of programming: you completely surrender to sensations, revel in exponential functions (in the original – “you embrace exponentials”, whatever that means) and forget aboutthat the code exists at all.” Isn’t that great? Yes, if you ignore the fact that AI, according to Microsoft experts, correctly corrects errors in code in no more than half of all cases – after all, people aren’t perfect either, right?
From the employer’s point of view, by the way, the situation is almost ideal: costs are minimized, the result is obvious, and if there are errors in the freshly generated code, then running it through the same (or an alternative, to be sure) system to eliminate them, and more than once, if necessary, will still be cheaper than maintaining live highly qualified employees. However, skilled programmers themselves are sounding the alarm – and, objectively speaking, not at all (only) because vibe coders, arm in arm with their virtual consultants, are taking away their honestly earned bread. The fact is that the AI assistant – in terms familiar to interpersonal communication – is extremely simple-minded: if you do not clearly and exhaustively indicate to it the boundary conditions in which to operate, the naive code written by the machine has every chance, to put it mildly, to surprise its end user. An example cited by 404 Media journalists is the rather popular RecipeNinja.AI app, created using the vibe coding method, for generating recipes based on a voice request. It would seem, well, what could go wrong here? Would the AI agent suggest adding some nonsense like pineapples to the pizza? Not at all; the instructions issued by the bot are quite reasonable – the only problem is that the conscientious AI interprets the concept of “recipe” too broadly; presumably, because the vibe coder did not set the corresponding restrictions in the conditions of the problem. And therefore the app readily provides useful recommendations for making ice cream with cyanide, natural cocaine (based on the assumption that the person asking already has some Erythróxylum cóca leaves on hand) or an atomic bomb (well, yes, inThe initial ingredients listed are not the most accessible to the average person: uranium-235, plutonium-210 and beryllium mirrors for focusing neutron fluxes, but still!)
In short, vibe coding is undoubtedly becoming part of the programming culture: it really does allow you to get quite effective prototypes of useful, in-demand programs from scratch in a matter of hours. But the value of a real programmer is even higher – one who is able to critically study the creation of artificial intelligence and correct it in order to identify and take into account, if possible, all the practical subtleties that are obvious to a person, but incomprehensible (for now?) to a machine. Here is what one seasoned programmer wrote in the comments to the news about the carefree prescription bot: “Aha; so, AI will easily write exactly what the customer needs – as soon as the latter is able to clearly, distinctly and consistently state all his requirements. Well, great – I will definitely not be left without work!”
Source: AI generation based on the FLUX.1 model
⇡#I can dig!
Diamonds in Minecraft are one of the most coveted resources: without armor, tools, and weapons created from them, it is, strictly speaking, not worth going into the portals leading beyond the ordinary, “upper” world. But finding diamonds in the thickness of the rock — despite the fact that they appear there during procedural generation according to a very specific algorithm — is an extremely difficult task. More precisely, it seems difficult to a living player: an AI named Dreamer, as evidenced by a publication in the scientific journal Nature Briefing, after appropriate training perfectly learned to solve this problem — and the training did not at all imply familiarization of the generative model with the original algorithm. The developers of Dreamer from the Californian branch of Google DeepMind taught, in their words, the AI model “to navigate the physical environment of the virtual world and improve its skills over time,” using positive reinforcement for finding diamonds underground. As a result, the system developed an optimal strategy without any additional hints – and successfully implemented it (the comparison was made with inveterate Minecraft players who did not have AI support).
And this, as many top managers (and not just industry experts) already believe, is a clear demonstration of the inevitability of the imminent displacement of live programmers from the profession, if not complete, then more than noticeable. In this case, we are not talking about vibe coding, where AI acts as a referent or consultant, but about the actual replacement of leather bags with smart bots in the overwhelming majority of cases. As Kevin Scott, Microsoft’s CTO, stated in early April, everything is moving towards the fact that 95% of the code on the planet will be written by AI, and current programmers will inevitably have to master other professions, and partly fundamentally change the very approach to creating programs, turning from “input masters” into “prompt masters and AI orchestrators”. At the same time, Mr. Scott stipulated that in their current state, smart bots are “horribly resource-intensive” due to the limitations imposed by the von Neumann architecture of modern computing systems on large and intensive operations in computer memory. In other words, the de facto forecast of 95% of the code created by AI refers to the bright future when generative models will not spend up to two-thirds of the energy they consume during operation on transferring data between processors and RAM (and only a third on the calculations themselves). But it is still difficult to say when exactly this wonderful future will arrive.
The 3.8 million member r/changemyview community was seriously shocked by a real-life experiment by Swiss researchers (source: Reddit screenshot)
⇡#AI came from an unexpected place
«“Stupid bots will never be able to successfully pretend to be humans — certainly not when talking to me!” — a fair share of regulars of one of the largest online discussion platforms, Reddit, have probably entertained such thoughts. But no: a group of researchers from the University of Zurich has proven, by introducing undercover bots into the popular community r/changemyview, that AI is perfectly capable of conducting purely human discussions, and even with a psychological slant; regularly earning karma on the site, collecting thousands of comments, forming a circle of devoted subscribers, etc. Strictly speaking, successful performance under a human mask is not an “individual” achievement of a particular model: the experimenters used GPT-4o, Claude 3.5 Sonnet, and Llama 3.1-405B to create replies; in addition, they manually edited posts that clearly revealed their generative nature. However, this was enough to enrage both Reddit admins and users, to the point that the experiment, declared “illegal and unethical,” could result in legal prosecution for its initiators. And they only wanted to draw public attention to the fact that AI bots are great for influencing the views of live interlocutors — and therefore can (and probably will!) be used by various unscrupulous characters. Which, in general, was to be expected: when did such characters refuse to use the freshest and juiciest fruits of technical progress?
Source: AI generation based on the FLUX.1 model
⇡#Bromance is over – there will be more models
In early April, Microsoft AI CEO Mustafa Suleyman (one of the co-founders of Google DeepMind, hired by the Redmond company specifically for the independent development of the AI direction) confirmed that the IT giant – although it continues to collaborate with OpenAI in the direction of artificial intelligence, including making multi-billion dollar investments in the project – is increasingly focused on developing its own generative models. Since then, the long-standing rift in relations between the two companies, clearly targeting the same part of an extremely profitable market, became even more noticeable by the end of the month: as noted in The Wall Street Journal, the heads of these organizations – Sam Altman and Satya Nadella – are increasingly diverging on issues of shared computing resources, access to models, as well as the very capabilities that new generations of AI should (or should not) be endowed with.
Thus, Altman, who once called the relationship between OpenAI and Microsoft “the best example of partnership in the IT industry” (in the original, using an even stronger and more expressive term, “bromance”), has repeatedly expressed confidence that his company is literally on the verge of producing artificial intelligence that is essentially indistinguishable from human intelligence. Nadella, however, as recently as February of this year, described such assessments as “nonsensical benchmark hacking,” meaning that the development of AI as a commercial product should not be tied in any way to the goal of creating strong AI — artificial general intelligence, AGI — that is abstracted from the subject tasks. The Wall Street Journal emphasizes that the end of the generative “bromance” is not expected to be painless — both companies have something to hurt each other with: Microsoft is capable of blocking OpenAI’s refusal of its current status as a non-profit organization through the courts, while the latter, in turn, is quite capable of prohibiting the former from using its most advanced technologies — such as GPT-4.1, officially presented in April.
Warning diagram: how malicious actors can use AI models’ hallucinatory generated package names to compromise user code (source: University of Texas at San Antonio)
⇡#Consistency is a sign of mastery
Or at least clear evidence of the presence of some structural pattern, which after a certain time becomes simply unreasonable to ignore. For years, experts have been emphasizing that generative models are good in every way, and the larger they become, the better they are overall — but there is a nuance: they do not stop hallucinating. The very principle of their operation does not imply continuous verification of the generated answers with reality (see the notorious incident with the bears in space), so at the basic level, without abandoning the dense multi-layer neural networks that underlie them, nothing can be fixed here. It is clear that it is possible to attach an additional verification circuit to a ready-made model — especially since such “add-ons” are already used to filter out unwanted output — but this implies additional labor costs and expenses, plus it increases the likelihood of errors. Socket experts directly warn about such an extremely unpleasant, especially if it is actively mastered by attackers, scheme as “hallucinatory capture” (slopsquatting) – when something that does not correspond to reality is so sincerely presented by AI at face value that the user who has addressed the generative model with a request does not even suspect a substitution. So the OpenAI o3 and o4-mini models announced in mid-April have shown an increased tendency to hallucinations – greater than their direct predecessors: an alarming signal!
Meanwhile, users of more conventional AI models continue to suffer from hallucinations: a recent study by researchers from the University of Texas at San Antonio, Virginia Tech and State University, and the University of Oklahoma demonstrated a frighteningly high level of inundation of code generated by smart bots with references to non-existent software libraries. Having examined 576,000 code samples created by 16 popular large language models (LLMs), the researchers found that the name of almost every fifth (19.7%) third-party package referenced in these samples turned out to be the product of AI hallucinations. Interestingly, commercial models performed significantly better — they suggested code calling non-existent libraries in only 5% of cases, compared to 22% for models with open scales. It turns out that additional verification of output, which developers who provide access to AI bots exclusively via API resort to for legal and ethical reasons, is justified in practical terms. Although it is not cheap: the reasoning model OpenAI o3 can spend up to $30,000 to solve just one problem, a significant portion of these funds is probably spent not on the inference itself, but on checking the results obtained. It is especially sad, however, that 43% of the names of external packages generated by hallucinations turned out to be the same for different models, which opens up simply boundless prospects for attackers who decide to place malicious libraries with such AI bots, for example, on GitHub in advance, which are highly reproducible.names. We are, of course, not inclined to admit that BYAM deliberately hallucinates references to packages with the same names in order to quietly place these very packages with a double bottom on GitHub over time and take over the world with their help, ha-ha-ha!
⇡#Old Crisis of New Oil
In April, a number of leading representatives of the American media industry, including The New York Times, The Washington Post and The Guardian, launched a high-profile campaign Support Responsible AI, calling on the country’s government to “immediately stop the theft” of their content by generative model developers. Interestingly, this campaign was launched in response to an earlier request to the same authorities — this time from OpenAI and Google — to allow the use of copyrighted materials for training AI. It’s all about money, of course: media people themselves are not afraid to use generative models in their work (although, for example, Microsoft recently resorted to AI in the creation of a new commercial, and no one really noticed it), but since the revenue from online advertising flows to the developers of smart tools from traditional media services, the owners of the latter are upset by this, and therefore they are trying to force AI companies to share the benefits of models trained on classical data (the same copyrighted books, films, paintings, etc.)
So where can poor developers of generative models go to get legally clean data? Creating them with AI is a dead-end option; the hallucinations that will inevitably arise in the process will pollute the synthetic information array and make the output of the next-generation models trained on it even less reliable than the current ones. However, one can appeal directly to people: for example, Apple plans to analyze data, including correspondence between users, on the devices of its hardware and software ecosystem, all with the same good purpose – to ultimately provide these very users with AI that is truly useful to them (and at the same time reduce the gap with competitors in this area, which is already frighteningly large at the moment). True, the gap may increase if OpenAI quickly implements its intention to create its own social network, announced in April – again, in order to feed the content generated by users to its new models for the purpose of their further improvement.
Indeed, who else but generative AI can answer impartially and with all the appropriate references which coffee machine under $200 makes a drink “with a taste as close to real Italian as possible”? Well, until advertisers start taking this channel of influence on consumers seriously? (source: OpenAI)
⇡#Pay for what your agent bought
After the Covid crisis and the subsequent, all-too-sluggish recovery of the global economy, it became obvious that people began to spend less on things that, on reflection, they could easily do without. For example, in 2015, the average lifespan of a smartphone before replacing it with a new one was 2.4 years (this is for the world as a whole; in economically more developed regions, phones were replaced much more often), and in 2022 it jumped to 3.7 years. The overall pace of sales of consumer goods, not only IT-oriented ones, in recent years has clearly lagged behind the expectations of the merchants offering them – so maybe it makes sense to turn to almighty AI here too? Probably, this is exactly what they thought at Mastercard when, in cooperation with Microsoft and a number of other leading IT companies, they began working on empowering AI agents to make purchases on behalf of the owner of a bank card – but without his direct participation. At least, for now we are talking about such an agent searching for a certain product (which the user did not directly order, but the AI has somehow concluded that it is necessary to purchase), after which the person is offered several options to make a purchase – and after its confirmation, the transaction is made. It seems to be a simple and logical option (finally, “smart” refrigerators, with the support of AI agents of payment systems, will begin to replenish their stocks of ice cream, dumplings, roach and other vital products themselves, without distracting the owner of the house with such trifles), but its implementation threatens a serious shake-up of the entire online advertising market, for example – it is obvious that suppliers of goods will now be more interested in interacting directly withbanks and the same payment systems than with marketplaces and especially with classic retail. It is quite possible that if events develop the way Mastercard wants (and OipenAI, by the way, has already upgraded the ChatGPT search subsystem, enriching it with direct shopping capabilities in a number of countries – with the demonstration of product images and links to suitable marketplaces), e-commerce will never be the same again. At least, Financial Times experts do not rule out that the usual search engine optimization methods “for people” will soon give way to AI-oriented online brand promotion.
However, AI agents have other concerns, too — for example, working with Photoshop instead of a live user who does not want (or does not know how) to select and use the necessary tools of this not-so-easy-to-learn graphic editor; this functionality will become available in the coming months. For communication with a person, a text input field is offered, in which you can give a command to perform some action with an image — or ask the bot to explain in detail how to do it manually. Another use for AI agents was found by the online dating service Tinder, which (albeit temporarily and only for a limited circle of users) offered to flirt with generative AI bots based on GPT-4o. And with the most noble goal: to allegedly instill in the younger generation (which is used to closing itself off to its gadgets and does not particularly like personal communication) a taste for casual, mutually pleasant chatter with potential objects of romantic interest. The app — or rather, a mini-game in it with the simple name Game Game — not only allows you to have a frivolous dialogue with the AI, but also awards points for lines that the same generative model recognizes as “charming” or “playful,” and deducts points for “cheeky” or “strange” comments. And to make such AI agents (not necessarily aimed at sensual communication) more realistic, a new development of the Character.AI platform, currently in closed beta testing, will come in handy — a generative model for creating video avatars based on static images, AvatarFX.
At this Arizona factory, TSMC will produce import-substituted AI chips for Nvidia — if all goes according to plan, of course (source: TSMC)
⇡#So, are we growing or falling?
Is investing in AI a smart investment or a risky gamble with a dubious asset? This question would have sounded strange a year or two ago — the hype around generative models since the fall of 2022 seems to be evolving only upwards; the stock market one for sure. However, in April 2025 — after the current White House administration finally introduced the long-promised increased import tax rates, especially from China — the situation began to develop in different directions. First, the capitalization of leading American IT companies — and all of them are directly related to the AI direction in one way or another — shrank sharply, by about $2 trillion; then, as soon as Donald Trump suspended the introduction of “tariffs from hell” for 90 days, it instantly recouped one and a half trillion dollars. Strictly speaking, under normal conditions, seasoned traders consider this kind of volatility to be a sign of an artificially inflated asset and are extremely wary of long-term operations with it, but stock market capitalization (calculated based on the current stock price) is one thing, and backing up this asset with long-term investments is quite another. And it seems that the AI industry is doing just fine with them: Google announced in April its readiness to invest $75 billion in expanding the AI-oriented capacities of its data centers, AWS is building a similar data center in Indiana with the energy consumption of 1.5 million households, etc. If such large players continue to confidently invest huge sums in generative models and in the hardware for their operation on an ever larger scale, can anything go wrong?
Perhaps, IT industry experts interviewed by The Financial Times believe, if President Trump’s tariff policy continues to damage long-term plans to localize IT production, including microprocessor production, and to further develop the AI direction in the United States. The issue is not so much the size of the additional tax that American IT companies are forced to pay when importing individual components or entire assembled devices manufactured abroad: it is clear that intensive investments in import substitution (this year alone, Microsoft, Google, Amazon, Meta* and others plan to invest $300 billion in computing infrastructure for AI, and Taiwan’s TSMC will spend almost $200 billion in the coming years to build chipmaking facilities in America) will sooner or later lead to the fact that importing IT equipment from abroad will become more expensive than producing it locally, and then the current titanic investments will have to start paying off. The problem is the fundamental unpredictability of Trump’s tariff policy – and this uncertainty, experts warn, could become the most serious obstacle to the almost indisputable American superiority in the field of AI. Since uncertainty is high, it is necessary to hedge, i.e. stabilize part of your assets, say, in gold, instead of investing them in enterprises that seem risky – which means that the pace of development of these very enterprises is objectively slowing down: they attract less funds than they expected. And for now, alas, investors remain a certain skepticism regarding the development of AI, despite the unambiguous efforts of AmericanIT giants dispel it with practical steps: if the market really has few doubts about the steadfastness of the policies of Amazon, Google and other AI leaders who are actively throwing their own money around, then the antics of the current head of the White House are making a lot of people nervous.
Google Ironwood is mounted on specialized boards in servers, with four processors on each (source: Google)
⇡#«There is never too much iron
Nvidia is the undisputed leader in the supply of server accelerators for AI computing (according to IDC data for Q4 2024, published in March, it is more than 90% in quantitative terms worldwide), but other players are increasingly beginning to lay claim to their share of this most delicious pie in financial terms. And first of all, the BNM developers themselves, running them on their own servers and providing access to them to everyone via API: it is clear that purchasing AI accelerators at cost directly from the chipmaker is much more profitable than competing for them – offered essentially by the only supplier in the world – with competitors, outbidding each other and at the same time increasing the market capitalization of this very supplier. And so it should come as no surprise that in April Google demonstrated the seventh generation of its own AI chips — the Ironwood tensor processing unit (TPU), optimized for generative model inference and intended for use in cloud systems of two configurations: in servers with 256 processors each, or in clusters containing a total of 9,216 TPU units. According to the developer, the peak computing power of an individual Ironwood reaches 4,614 Tflops, and a full cluster of such processors — 42.5 Eflops. The direct manufacturer of the new product is not disclosed — it is quite possible that its production is not carried out at TSMC factories at all, which are already loaded with orders from AMD, Apple, Nvidia and other American chip developers for several quarters in advance.
Under the leadership of the new CEO, Lip-Bu Tan, Intel is also changing its approach to AI accelerators. From now on, the company is going to sell customers not so much the chips themselves, which direct customers then use at their own discretion (in particular, building server systems on their basis and implementing them with a much higher margin), but ready-made AI servers complete with all the software necessary for their optimal operation, as the same Nvidia does, for example. Having set this ambitious task for itself, Intel is ready to further develop the AI accelerator direction on its own (and not by acquiring and integrating third-party companies into its structure, as was the case before). Undoubtedly, the leading American chipmaker will have a hard time in a new market (if we take into account again the desire of Amazon, Google and other hyperscalers to focus on TPUs of internal development), but on its side there is its own production base and a good engineering school – such a combination of very successful factors for the company would be a sin not to use. Moreover, Huawei in China is preparing for practical testing of its newest, not yet launched into production, AI accelerator Ascend 910D in the servers of its partners…
Dynamics of requests for bandwidth allocation for multimedia content on Wikimedia Projects servers (source: Wikimedia)
⇡#«The Internet is too small to be shared with leather bags”
Web crawlers are an old invention: software bots that methodically visit various websites — and not only them, FTP servers, for example — to scan their contents for subsequent indexing by search engines. As of the end of last year, researchers from Incapsula claim, almost 62% of all web traffic in the world was generated by various bots, including good old web crawlers, but mostly newfangled messengers of generative models, constantly collecting information for them. And the situation with AI crawlers, alas, is becoming increasingly unpleasant: according to the Wikimedia Foundation, from January 2024 to April 2025, the volume of web traffic generated by bots downloading multimedia content increased by 50%. Classic web crawlers have no use for audio and video files, but AI models find them valuable as carriers of easily extractable information. As a result, today 65% of Wikimedia’s most expensive traffic (namely multimedia – it has to be returned on request, including from a bot, with minimal delay to avoid interruptions) goes to AI crawlers, not to people searching for information the old-fashioned way, manually.
But at one time, Synopsys Design Vision software, which allowed manual design of 45-nm chips with a minimal level of automation, was launched on rather modest PCs (source: Cornell University)
⇡#Speed is dangerous – and useful
Classic passwords are not in favor today: more and more sites offer users to switch to passphrases, two-factor authentication, security keys (passkey) or biometric login. And in general, this is justified: thanks to the wide availability of rented cloud hardware for running generative models, hackers, according to experts from the HotHardware portal, have already learned to crack passwords in a few days if they are really complex, and almost instantly – if they represent widely known combinations of characters. At the same time, a password that has leaked into a database circulating on the Internet, and even its hash, can be cracked with significantly lower costs.
Of course, AI accelerates not only malicious, but also quite respectable software activity, such as the design of microcircuits. Modern chips, in which transistors are already counted in tens of billions, are simply impossible to draw on a drawing board manually, and therefore more than half of the microcircuits that are developed today with the expectation of manufacturing according to production standards of “28 nm” and less, are already designed with the involvement of AI tools. The platforms for the automation of electronic design of the world leaders in this field, the companies Synopsys and Cadence, which previously relied on algorithmic automation tools, are now supplied with integrated AI. As a result, the total time spent on creating a new project from scratch is reduced compared to the previous time, sometimes tenfold, and during automated optimization, the performance of individual functional blocks increases up to 60%, and energy consumption decreases up to 38%.
Source: AI generation based on the FLUX.1 model
⇡#Some leave, some stay
In March 2025, when ChatGPT introduced the ability to create images directly during a dialogue with a bot, the number of active users of this service in a week approached one billion, as OpenAI representatives reported in April. In addition, among non-gaming smartphone applications, ChatGPT firmly holds the global mark of primacy, and the total number of its installations has reached 46 million. At the same time, one of the projects competing with GPT – an AI lab within Meta* that develops, in particular, a series of generative models with open scales Llama – is slowly dying, according to insiders cited by Fortune: employees are leaving it, and the company’s management is increasingly paying attention to commercial generative models. Gemini, the Google-backed AI project, is also not doing well: in April it became known that only 350 million unique users access this model per month (and only 35 million daily), which is significantly less than the number of people interested in both ChatGPT and Meta* AI. However, the leader of the global smart bot segment, ChatGPT, is not without sin: after switching to GPT-4o as the main model, it became “overly flattering and annoyingly obsequious” – as Sam Altman himself assessed, by the way – so the company had to quickly roll back the update. It is funny that the users of the smart bot themselves do not forget to say “thank you” and “please” to it – words that are completely uninformative in terms of the formulation of the request, but are still processed by the system and therefore cost OpenAI millions of dollars. Perhaps, at the very moment when AI will be able to clearly draw the line between sycophancy andpoliteness, and will he be ready to seriously claim the high title of “strong” – AGI?
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* Included in the list of public associations and religious organizations in respect of which the court made a decision to liquidate or prohibit activities that has entered into legal force on the grounds provided for by the Federal Law of July 25, 2002 No. 114-FZ “On Combating Extremist Activities”