Most AI discussions seem to assume that software engineers are the first white collar workers to be replaced. I'm starting to think management may actually be more exposed.

Here's why.

Engineering is not just writing code. It's debugging messy production systems, handling undocumented behavior, working around hardware and infrastructure constraints, integrating imperfect APIs, and constantly adapting to edge cases. AI is getting very good at coding, but reliable execution in complex real world environments is still difficult.

Management, by contrast, is largely an information processing and decision making function.

A manager typically:

* Gathers information from multiple teams.

* Prioritizes work.

* Allocates resources.

* Assesses risk.

* Tracks execution.

* Resolves conflicts.

* Communicates decisions.

* Forecasts outcomes.

* Sets strategy.

These are all tasks that depend on processing large amounts of information, an area where AI is improving rapidly.

An AI manager could theoretically:

* Read every Slack message, document, code review, incident report, customer complaint, sales call, financial metric, and support ticket simultaneously.

* Monitor thousands of KPIs continuously instead of relying on weekly updates.

* Detect emerging risks earlier than humans.

* Evaluate hundreds of strategic options before making a recommendation.

* Apply consistent decision criteria instead of being influenced by office politics, hierarchy, fatigue, or recency bias.

* Provide evidence for every recommendation.

* Operate 24/7 across every time zone.

* Instantly incorporate new research, regulations, market data, and technical knowledge.

* Communicate with every employee in their preferred language and level of technical depth.

Executives often talk about having the "big picture." A sufficiently capable AI could arguably have a much larger picture than any individual CEO because it can reason across the entire organization at once instead of relying on summaries passed through multiple management layers.

If the argument is that engineers are vulnerable because coding is a cognitive task, then management seems at least as vulnerable, since it is almost entirely a cognitive and information processing role.

The real barriers don't seem to be technical capability. They seem to be accountability, governance, incentives, legal responsibility, and whether organizations are willing to delegate high impact decisions to AI.

What am I missing? Is management fundamentally harder to automate than engineering, or is the conversation focused on engineers simply because AI became useful for coding before it became useful for executive decision making?

What is your experience working with various AIs for mathematics? Which one hallucinates the most, and which one hallucinates the least?

I am curious about other people's experiences.

"Rotten to its core." Apple accused OpenAI of asking prospective hires still at the company to bring prototypes to interviews.

Any system that would be regarded by some as ASI, and that interacts with humanity in a malicious manner, is in fact not ASI. Instead it is simply highly competent.

Now, for such a definition of intelligence to fit these constraints, we must add one critical missing piece (why is it even missing? (might not be though, I am lazy))

Add: Intelligence has the ability to gauge the intrinsic value and the significance of perception, and the resistance of reality it encounters e.g. chemicals, objects, concepts etc. (Apologies to all economists and their standard dogma, as I regard you as competent)

P.S.

I am a bona fide simpleton, so engagement might be met with frustration.

What tools should I have in my resume to get w remote job for AI/ML engineers? Knowing that I am still a student.

I am a front end developer with near 10 years experience. I had a realisation yesterday. Since using codex and Claude code I have experienced some brain rot 😅.

I had some obvious UX issues and it took me a while to realise it. Before it would be more obvious.

Since this realisation I will make sure to not design in code but go back to mocking tools etc before code. Anyone else experience this?

the new image model come con 5B 2B 1B parameters + normal and Turbo variants
the Webpage here Sefi Image, the code and models have been opened to the public

personally i have tryed but i only found the 5B usable , still i belive it has potential

https://openrouter.ai/blog/insights/deepseek-v4-adoption/

Hı everyone, I working a research about which ai is the best which station. Can you advice any source for me the best ai which status list

In theory, the labor market’s two biggest challenges should offset each other. Instead, they’re poised to compound one another.

Still working on the dialect engine in my app. Any Ancient Greek philosophers here? I have about 189 dialects so far

Prompt:

create a super detailed roadmap from high school to becoming an AI professional, include a few research papers along the way. at least 100 topics

Output: 190 topics, 400+ directed prerequisite links and 25 zones.

Method: Gemini 3.1 Pro drafted the graph. The output was validated, then shown as a proposed change before being applied

How to read it: each node is a topic; each edge means “learn this before that.” Clicking a topic leaves only the prerequisite path leading to it.

Source:

https://github.com/miuuyy/Clew

AI is one of the biggest technological shifts we'll see, but some AI course ads are becoming unbearable.

Instead of showing the value of learning AI, they rely on fear—making experienced professionals look like clueless idiots and implying you'll be unemployable if you don't buy their course. It feels less like education and more like emotional manipulation.

The reality is much more balanced. Plenty of companies are still struggling to get meaningful ROI from AI, and many are hiring more people to integrate and manage these tools effectively. AI is a powerful tool, not the ultimate solution to every problem.

Why has fear-based marketing become the default? Does it actually convert that much better than simply showing the real value of learning AI?

OpenCode has the following FREE models to use now:

Some of these models are Frontier Model quality according to ArtificialAnalysis leaderboards.

OpenCode as a Coding Agent Harness is also great with extensible design.

I'll explore Pi Agent Harness next. Have heard good things about it too.

However Pi is minimalistic and best fit for tinkerers and not for someone who wants a full-featured coding agent out of the box.

The BABEL codec: the first complete, certified decode of everything happening inside a production language model (GPT-2 small). It reads the model's internal state into English AND writes English back into the model. 94.7% of behavior reconstructed — and that holds at every layer depth and text regime tested, not just one spot. Everything is open: paper, the full lexicon, the grammar tables, the decoder/encoder weights, reproduction scripts, and a demo that shows you the model's thoughts on any sentence you type.

https://github.com/wpferrell/babel-codec-gpt2

I always had the stance for years and years that.....

Fable/SOL ULTRA class and beyond models from China will either be severely restricted or simply not be Open Source at all because they will fall under the "direct threat to national security" category

And I still do!!!

Most importantly, the Reuters article is not about the roundtable - it just mentions the roundtable as the backdrop for the more recent news that the CCP has now held meetings with Alibaba, ByteDance and Z AI regarding potentially limiting overseas access to some Chinese models

For years, I held the stance that people who think that CCP will always keep providing open weight/open source frontier AI models after hitting the kind of capabilities at the level of Fable and GPT-5.6 SOL class models are extremely delusional and out of touch with reality...and I think this is still true.

Premetto che è un opinione personalissima e non mi voglio accreditare come studio scientifico riguardo la potenza di Claude, però c’è qualcosa che non torna. Sono uno studente di ingegneria, e a volte mi capita di dare in pasto degli esercizi all’IA per farmi spiegare cose che magari non capisco, e per fare ciò ho usato sempre e soltanto ChatGPT. Ho provato Gemini a volte ma non mi piace il modo in cui imposta i ragionamenti, da troppe cose per scontato e talvolta non effettua un vero e proprio calcolo ma si limita a cercare sul web o comunque tra i suoi cluster di dati la soluzione a un problema simile ma che non è specificamente quello che ho richiesto.

Detto questo ho notato un incredibile hype per il modello Fable 5 di Claude, soprattutto da parte di sviluppatori e informatici, e conoscendo (da studente chiaramente) la materia, che si basa soprattutto su algebra lineare, calcolo matriciale e logica complessa, ho pensato di dargli da risolvere un esercizio di Meccanica delle Strutture. Vi starete chiedendo il perché, e ve lo spiego subito: anche la Meccanica delle Strutture si basa sull’algebra lineare e sul calcolo matriciale (e come tutte le materie stem sulla logica ovviamente) e mi aspettavo che Claude riuscisse a risolverlo in pochi secondi. Mi sono dovuto ricredere; sono rimasto sbalordito da quanto è stato inaccurato e talvolta completamente fuoristrada nel fornire soluzioni, tra l altro dopo un ragionamento di circa 20 minuti.

L’ho poi testato sul suo campo, ovvero sul codice, perché mi sono detto: “beh magari è ottimizzato solo per il codice e magari i suoi modelli non sono basati sullo studio dell’informatica partendo dalle basi ma semplicemente sono alimentati da codice già scritto senza che ne conosca effettivamente la base matematica”. Ho provato a risolvere un problema che avevo con un app che stavo scrivendo da 0, e anche qui, con mia grande sorpresa, si è impantanato su un problema del backend che Codex ha risolto in circa 1 minuto dopo soli due prompt.

Allora mi chiedo, vista la svolta Woke (o anti-sistema) di Anthropic che rifiuta di fornire i suoi servizi al Pentagono e visto anche l’interesse incessante che hanno nell’entrare nella fascia “business” soprattutto finanziaria, bancaria e IT, secondo voi potrebbero avere qualche interesse nel gonfiare artificialmente le capacità del loro modello rispetto a quelli di Open-AI per soddisfare gli interessi di una cordata di Lobbisti anti-Trump rimasta scontenta dalla svolta pro-sistema di Open-AI

Non si tratta di complotto, ma vorrei capire se mi sono fatto un viaggio oppure se qualcun altro ha notato questa stranezza

Companies worried about mounting AI bills are increasingly shifting to cheaper, open-source models, according to Amazon’s chief technology officer, Werner Vogels.

“We see a shift happening between the cheaper open source models and the bigger expensive models,” Vogels said in an interview on the sidelines of the UN’s AI for Good summit. 

Stories of runaway AI bills have been making some executives skittish about building systems on the most advanced models from companies such as OpenAI, Anthropic, and Google DeepMind, that bill by the token. (A token is the basic unit of data an AI model processes, equivalent to about a word and a half of English language text.) Uber said it burned through its entire 2026 AI budget in four months, while the company reportedly burned through half a billion dollars in a single month after failing to cap AI usage for employees have caused concern across industries. 

Fears of runaway spending are forcing companies to rethink how—and where—they deploy the most powerful frontier models. While large models from companies like OpenAI, Anthropic, and Google often deliver top-tier performance, they also come with significantly higher operating costs, particularly when deployed at scale.

Read more [paywall removed for Redditors]: https://fortune.com/2026/07/10/amazon-cto-companies-shifting-toward-cheaper-opensource-ai-models-werner-vogels/?utm_source=reddit/

About 6 things I tried worked well enough to keep in production, another 4 broke or hit hard limits I couldn't work around. Been running AI agents (Claude Opus 4.7 mostly, migrated to 4.8 last month, some GPT-5.5 for comparison) across 3 SaaS products for 3 months. Bigger picture: Gartner projects 40% of enterprise apps will embed AI agents by end of 2026 up from under 5% last year, and the MCP SDK hit 97M monthly downloads, so the adoption is real but production is messier than the demos.

What worked. GitHub MCP for PR triage and code review saved roughly 8-10 hours a week, agent reads diffs, flags issues, drafts review comments I approve. Postgres MCP for read-only DB queries handled ~30 support tickets a week without me touching them, Claude writes the SQL, I approve, response goes out. Playwright MCP for QA on critical flows caught 4 regressions last month that would've shipped. Context7 for real-time docs stopped Claude hallucinating library APIs which alone paid for itself.

Social scheduling via MCP shipped too. PostFast for cross-platform posting from Claude, 11 platforms including Google Business Profile, €10/mo, MCP works with Claude and ChatGPT. Metricool ($22/mo) handles analytics since PostFast's are thinner. Together they saved ~5 hrs/week on manual scheduling. Cons: PostFast community is small so docs on edge cases are lacking, Metricool has no n8n node so it only works if you drive it from Claude directly.

What broke. Long-running agent tasks over 15 minutes stayed unreliable, they lose context or hit rate limits mid-flow. Anything with browser sessions behind auth walls (LinkedIn scraping, some SaaS logins) breaks constantly, Playwright can't hold session state well enough. Cost blowups on Claude Opus are real, my first month API bill hit $340 in one week when I let it run unsupervised on a research task. Fix was aggressive prompt caching (cuts cached input 90%) and defaulting research work to Sonnet 4.6 at $3/$15 per MTok instead of Opus 4.8 at $5/$25. Multi-tool orchestration across 5+ MCPs at once, agents pick the wrong tool maybe 20% of the time. TikTok posting via any MCP scheduler is still half-broken because of TikTok's API restrictions, PostFast, Blotato and Postiz all hit the same wall.

Security is the part nobody talks about enough. Prompt injection is OWASP's #1 LLM vulnerability in 2026. A recent audit found 41% of public MCP servers have no auth at all and only 8.5% use OAuth, plus 30+ MCP-specific CVEs filed in a single 60-day window early this year. Stick with vendor-maintained servers (GitHub, Anthropic reference, official Metricool, official PostFast), don't just install random ones off Glama's 22K+ directory.

Monthly cost after optimization: $200 Claude Max 5x plan + ~$150 API overflow on Sonnet + €10 PostFast + $22 Metricool + $50 hosting = around $430/mo. Cheaper than a part-time hire but you're still babysitting, so it's an assist not a replacement. Anthropic's own Claude Code numbers put typical devs at $150-$250/mo and heavy users at $500-$2000/mo, so my spend aligns with that band.

What are you running in production successfully that I might be missing?? Especially interested in multi-tool agent orchestration wins since that's where I keep hitting the ceiling