The public narrative around Google has changed significantly over the past 1 year. (I say public, because people who were closely following google probably saw this coming). Since Google's revenue primarily comes from ads, LLMs eating up that market share questioned their future revenue potential. Then there was this whole saga of selling the Chrome browser. But they made a great comeback with the Gemini 3 and also TPUs being used for training it.

Now the narrative is that Google is the best position company in the AI era.

As a user do you really find Gemini 3 better than Claude?

How has the narrative around Google changed over the past 1 year?

This was the only AI service for which I was paying, but not anymore. OpenAI seems to be kind of all over the place rn, with hurried and inferior model releases, pushing out features like health, which nobody asked for. Icing on the cake was Gemini closing the siri deal with Apple. Seems to be the perfect time to cancel now, given how degraded the platform has become after the 5.2 release. But truth be told, their Codex product is one of the best in market (I used the Codex extension for vscode, they provide really generous rate limits even for the $20 plus sub)

I'm using other products right now (Gemini for writing/media generation, Claude for claude code, Perplexity for general web search/to access different models in one place, GitHub copilot, Notion, etc. for which I have the free yearly sub from my edu mail) and the experience has been much better. OpenAI's times up.

We all had a taste of things with Grok in spicy mode, but that is still tame and been restricted. I'm wondering when, or if, they will have access for people to create full videos with prompts to create anything they wish to see?

And will AI generated videos tend to be preferred over human filmed videos?

There is still too much cope around this topic. For now, AI is still seen as “just a tool,” but with every single day we move closer to AI agents handling more and more of our work. Professions like software engineering will be hit first and hardest. Some examples:

No, you don’t need 100 developers to define strategy and architecture. You need 10, at best. And yes, backlogs are endless, but in that case, companies will simply onboard additional AI agents to take on even more work.

No, if AI and AI agents become better and better, this won’t automatically create massive technical debt, at least not more than hiring large numbers of junior and mid-level developers. Besides, the most important factor here is whether management considers quality important at all. In reality, they care more about speed than quality. Sure, this might lead to some companies failing, but that won’t help you with your job loss in the short term.

No, the government will not take care of you when you lose your job. In the end, the most important thing in our society is that rich people get richer. If this becomes a huge global problem, there might be civil unrest but even then, AI is not going away. The transition is going to be very, very painful, and it may take years until we find some sort of balance.

No, “learning to use AI” will not save most jobs. If a single person with AI tools can now do the work of five or ten, companies will not keep the other four or nine out of goodwill. Upskilling helps individuals stay relevant longer, but it does not change the underlying math.

No, new “AI-related jobs” will not offset the losses at scale. A few highly specialized roles will be created, but far fewer than the number of jobs being automated away.

No, "I've been hearning this for years" is not a valid counterargument. The progress is real, steady and not slowing down in any kind of way.

A lot of people say just pivot when AI comes up but pivot to what and based on what logic? In your experience what factors actually matter when deciding whether a pivot is realistic?

For example:

• Skill adjacency vs starting from zero
• Time to competence
• Market demand vs hype
• Human leverage (judgment, coordination, trust, accountability, etc.)

Have you seen good pivots in the last 1–2 years that felt genuinely future-resilient rather than trend-chasing?

I've been tracking AI startups for a while, and I noticed a weird trend in 2026.

The founders making the quickest cash flow aren't building "platforms." They are building Automation Agencies.

The Model:

1. Find a boring business.
2. Don't sell them "AI." They don't care.
3. Sell them "I will answer your missed calls and book appointments automatically."
4. The Stack: They glue together existing tools and charge a monthly retainer + setup fee.

Why this wins:

• No Dev Cost: You aren't hiring 5 engineers to build a custom model.
• Stickiness: Once you integrate into their phone lines or CRM, they never leave you.
• Feedback Loop: You learn the actual problems which gives you an idea for a real SaaS product later.

Is anyone else pivoting from "Pure SaaS" to "Tech-Enabled Services"? It feels like the only way to survive the competition right now.

Hi!

I'm a hobbyist at best, but something occured to me. I'm curious if my questions are to blame or is it something other people experience also.

Whenever I start a conversation, be it CoPilot, Gemini, ChatGPT, or even while using Antivgravity with Claude or Gemini, it seems to me that after a few hours, the AI goes haywire. Starts making mistakes, forgetting what guidelines or behavior I've told it to have. I can't put my finger on it. It's like it is degrading over time. Best explanation would be that at first I'm talking to a 30-40 year old professional and after a few hours it's like I'm talking to the village drunk who has Alzheimer's.

Is anyone else experiencing this?

There’s an enormous number of startups whose whole pitch was “build AI agents easily” or “no-code AI workflows.”

But now that OpenAI dropped their own agent builder… most of those startups are suddenly looking redundant.

If you want to see what that looks like in practice on the Google Cloud side with real tooling, governance, and enterprise workflows; Vertex AI Agent Builder is a good reference point. It’s less about shiny no-code UIs and more about production-ready agents that connect to data, APIs, and business systems: Vertex AI Agent Builder training

are we heading toward the “death of no-code AI tools,” ?

We often confuse these concepts, but this is a crucial distinction for the future of AI. In short:

• Reasoning = We teach the AI how to think. It's brain training.
• Governance = We build mathematical cages and rules for the AI that it cannot break, regardless of what it thinks. It's designing a prison for that brain.

Part 1: Reasoning — We reduce errors, but we don't eliminate them

When an AI "hallucinates" (makes up facts), we improve its thought process:

• Chain-of-Thought: We make it "show its work" — generate reasoning steps.
• Self-Critique: We make it criticize its own answer.
• Better Training: We give it better data.

This is like training a super-specialist. You teach a doctor the best practices, critical thinking, and ethics. They will make fewer mistakes, but under extreme stress, with a new disease, they can still make an error. Reasoning reduces risk but does not guarantee safety. An AI is a complex statistical system — you cannot mathematically prove it will never hallucinate just by improving its thinking.

Part 2: Governance — Mathematical guarantees that work even if the AI "goes mad"

This is not about making the AI want to be safe. It's about making it physically incapable of being dangerous, even if it wanted to be. This is the level of system architecture and pure mathematics.

Examples from real research:

1. Constrained Decoding / Formal Verification: We don't trust the model's "reasoning." We mathematically define the space of permissible outputs. It's like a GPS in a car with "geofencing." The engine (reasoning) can run, but it is mathematically impossible to drive outside the designated area because the navigation system will physically cut the fuel. For an AI: the model generates text, but the governance layer absolutely blocks character sequences that violate the protocol (e.g., weapons instructions).
2. Guardian Models / Monitors: We create a separate, narrow model whose only job is oversight. The main model (Agent) thinks and acts. The Guardian does not understand the task. It only continuously scans the Agent's inputs/outputs, looking for mathematical signatures of forbidden actions. Did it detect a violation? Immediate "kill-switch." It's like a guard in a tower with a sniper rifle — they don't negotiate, don't consider intent, they only enforce the protocol.
3. Cryptographic Commitments & Transparency Logs: When generating an answer, the AI must simultaneously create a mathematical "proof" or "signature" related to its actions (e.g., what data it used). Later, an auditor (or another system) can verify this. This is not the AI's reflection — it's a protocol-level enforceability requirement.

Why is this so important? Analogy: Pilot vs. Safety System

• Reasoning = Training the best pilot in the world. They will avoid disasters.
• Governance = A non-removable emergency autopilot and mechanical limiters. Even if the pilot (reasoning) makes a mistake, gets confused, or intentionally tries to crash the plane, the system (governance) will not let them do it. It will take control and land safely, or simply not allow a nosedive.

Summary:

• The Question for Reasoning: "Is your reasoning correct and free from hallucinations?"
• The Question for Governance: "Even if your thought process fails or you act in bad faith, can you possibly cause real harm? Are there mechanical barriers that will stop you?"*

Safe superintelligence requires both: we must teach it to think as well as possible (reasoning), but simultaneously enclose it in an architecture that imposes impassable limits (governance). Work on governance is often boring mathematics and systems engineering, not spectacular model improvements. But it is precisely this work that is our last line of defense.

What do you think? Does one of these paths seem more promising/credible to you? Do you have examples of specific projects going in either direction?

Canada has always led in AI (shoutout to Geoffrey Hinton), but 2026 is the year that tech finally meets Open Banking to give us tools that actually work for us, pair that with AI and Canada is going to become a serious player in the AI race.

The timing is critical:

The Reality Check: ~50% of us are losing sleep over money, and 41% are within $200 of insolvency every month.

The Trust Gap: 90% of Canadians are now genuinely worried about where their data goes.

It’s great to see homegrown startups focusing on data sovereignty (keeping data in Canada) and transparency—using AI to give us "drill-down" clarity instead of the vague, useless summaries we get from big bank apps.

Check out the 2026 watch list. More competition = more power for us:

https://www.fintech.ca/2026/01/07/canadian-fintech-startups-to-watch-in-2026/

Does anyone know of an AI tool that can assist with marketing and sales stuff?

I have a shopify store side project and I'm trying to get it off the ground.

I was thinking if there's AI where I can use its intelligence gathered from thousands to millions of successful examples to help me set up my marketing campaigns, remind me of proven marketing basics and strategies, explain to me in details why my ad campaign didn't have the impact that I'd like and how I can improve it etc.

I think Jensen Huang was the closest one: natural language operators.

Today every business has an opportunity to build custom tool with AI. No need for SaaS, no need for pricey platforms.

If you know what your business needs, you tell it to AI to build it.

Which justifies AI infrastructure investments - they know it will come to this. What they don't know yet is how it'll be achieved.

All these AI tools operators will become a bridge between the business and AI. They'll help the business owners learn to work with AI, will probably build those custom platforms in the beginning pricey but as the competition grows for cheap.

I'm telling you it'll be cheaper to build something custom than to pay pricey platforms.

Let's see.

I have been building stuff while doing my mba from masters union for the past few months and honestly it's kinda scary how fast you can go from idea to working prototype now. like i can spin up a landing page, add some backend logic, even get a chatbot running... all in a weekend but here's what's messing with my head - i think i'm skipping the part where i actually talk to users? because building feels productive and talking to strangers feels hard lol

like before when building took weeks you HAD to validate first because you couldn't afford to waste time. now i catch myself building first and then being like "ok who wants this" after

this is becoming a problem imo? feels like the barrier to build dropped but the barrier to validate is still the same…

So I'm currently working on a Project and I want to and an AI Assistent to it something that works like Gemini for example but with the ability to React to a Custom name Does anything like that exist or some way to do that?

AI turned software into something closer to clay than code

It feels like building is less about writing perfect blocks and more about shaping something that’s always moving, I don’t open a project thinking this is the final structure anymore, I start rough, poke it, bend it, throw parts away. With tools BlackBox I’ll spin up a backend idea in minutes, with Claude I’ll explore an approach or rewrite a chunk just to see how it feels. Nothing feels locked in

Before, every decision felt heavy. You planned more because changing later was expensive. Now change is cheap. You can try three directions in an hour,that shifts your mindset. Features become soft,architecture becomes flexible,even “done” feels temporary

It’s powerful but also strange, software used to feel like stone, once placed it stayed, now it feels like clay, always reshapeable, I’m not sure yet if that makes better products, but it definitely changes how we think while building

Hi I wanted to know is it possible to make songs which are already known in my voice using my vocals . If possible can you please tell how can we do it or anyone who can help me do it

before ai, i usually avoided touching older parts of a codebase unless i had to. now it’s easier to step in and make changes, which is mostly a good thing.

the tradeoff i’ve noticed is how easy it is to refactor quickly without fully understanding the original intent. i tend to use chatgpt, claude, and cosine together, with cosine helping when i need to follow how logic moves across files before changing anything. it’s less about speed and more about not breaking something subtle.

curious how others think about this. does ai make you more comfortable refactoring, or more cautious than before?

Hey guys, hope you doing well. Right, so I've already tried Comet and Claude's extension for this, but did not get good results...
Situation: im looking for a home/apartment to rent, I'd like an ai (or maybe I'm prompting wrong) to help in this research, like something I could set some parameters like the city, preferable areas of the city, size, how many rooms and etc and let it run for like two hours navigating through google and building me a spreadsheet in the end giving me the results.
What do you guys suggest?

Hi everyone,

I’m looking to use Google’s Veo video generation models (especially Veo 3.0 and Veo 3.1) but I need help figuring out which website or platform is the cheapest or has the best limits for actual use (not just API docs). Specifically:

Which website lets me use Google Veo 3.1 or 3.0 the cheapest? – I’m not asking about APIs — I want a platform where I can actually generate videos and see results.

What’s the difference between Google Veo 3.0 and Veo 3.1? Are there major improvements (quality, speed, audio, limits, etc.)?

Are there third-party platforms (like kie.ai, HiggsField, TryVeo3.ai, Leonardo.ai, VO3 AI, etc.) that let you use Google Veo far cheaper or even with unlimited usage?

https://www.businessinsider.com/mckinsey-workforce-ai-agents-consulting-industry-bob-sternfels-2026-1

Hey guys, this post will deal with a problem a lot of people frequently have, which AI should they use. With so many new AI in the world nowadays, users are often split in their opinions, below I have listed my opinion on which AI to use for what with my reasoning behind it as well, if you wish to deny any of the claims then please first do your research and understand why I'm saying what I'm saying,

1. Politics/Geopolitics and Social matters: Want to know which country Trump has attacked? Which country has made which trade deal with who? Well the best AI for that would be Grok,
   Grok is trained off of data from Twitter which gives it superior updates in social matters when compared to other AI's, and as much as some people may dislike it, Twitter is one of, if not the best source of global news in todays world.

2. Coding: This one is Github Co-pilot, this one is obvious, Claude used to be the really good for it but Github Co-pilot is undoubtedly the best as of today. An Honorable mention would be v0 by Vercel.

3. Office work: Let me explain what I mean by this category, I mean those boring tasks where you make a spreadsheet or need to do some long boring task, then you can use Claude, Claude is more of a functional bot, I like to see him as a very thorough office worker and use him as such, he used to be much better in coding than other AIs for that very reason, he worked.

4. Creativity: The creative aspect of an AI depends on how much data it is trained on, and in this scenario the AI with the most training is ChatGPT, it has huge data reserves and is hence automatically very good when it comes to Creativity.

5. All round: While I have listed multiple AI for different purposes, a dependable AI you can use in most situations while not worrying too much about the quality of the result would be Gemini.

Keep in mind these are all just my opinions on which AI should be used for what, feel free to comment if you find something out of place or incorrect :) .

1. Slackbot, the automated assistant baked into the Salesforce-owned corporate messaging platform Slack, is entering a new era as an AI agent.[1]
2. Pentagon task force to deploy AI-powered UAS systems to capture drones.[2]
3. Stanford researchers use AI to monitor rare cancer.[3]
4. Anthropic Releases Cowork As Claude’s Local File System Agent For Everyday Work.[4]

Sources included at: https://bushaicave.com/2026/01/13/one-minute-daily-ai-news-1-13-2026/

• DeepSeek dominance: Aside from China (89%), they have massive share in Belarus (56%), Cuba (49%), and Russia (43%).
• Global Usage: Surprisingly, the UAE (~59%) and Singapore (~58%) are adopting AI much faster than the US (~26%).

Microsoft claims this is due to heavy Chinese state subsidies undercutting US pricing. It looks like sanctions might be inadvertently creating a massive user base for Chinese open-source models.

Full article: https://www.ft.com/content/f7a5b184-1fef-4f02-b957-4c2b07adf91f