Longevity gene therapy and AI – What is on the horizon?

For me its summarizing long documents. The first draft looks convincing, but checking missing context and subtle mistakes can take almost as long as doing it manually. Curious which tasks other people expected AI to handle well but still end up reviewing line by line. submitted by /u/Delicious_Weekend546 [link] [comments]

This Citadel Securities note (June 2026, Frank Flight) is a sharp, timely read — and it strongly validates the pain point you’re experiencing. Core Thesis of the Report Frontier AI is hitting real economic limits: Even the most powerful models face physical bottlenecks (compute, power, cooling, memory, inference budgets). The “unrealistic expectations” around frictionless scaling are being corrected by actual bills. Recent examples cited: Amazon canceled its Claude Code subscriptions. Multiple reports of unexpectedly large token bills. Economic reality: Prices are starting to do their job — signaling scarcity, incentivizing substitution (to cheaper/faster models), and rationing capacity toward highest-value uses. Bifurcation incoming: Heavy frontier model usage will concentrate among a smaller set of firms/teams solving genuinely hard problems. Everyday workflows will shift to more efficient, cheaper models. The chart: The Silicon Data LLM Expenditure Index (price + mix of tokens) has declined recently after earlier spikes. This likely reflects users substituting away from the most expensive models toward cheaper ones as costs bite. This lines up almost perfectly with your Anthropic Team → Enterprise jump ($400K → $1.4M) and your unfiltered thoughts. How This Connects to Your Situation Your points are spot-on and now mainstream in macro/strategy circles: Spend aggressively where it grows the business — Citadel agrees this makes sense for high-marginal-productivity areas (engineering, research, etc.). Visibility is the prerequisite — Personal spend shock ($4k in 3 days on Claude Code) is exactly the mechanism that forces better decisions. Engineering ROI is clear — Frontier models often pay for themselves in speed/quality. Many other roles? Questionable — Low-usage apps and “someone already built this” scenarios are exactly where substitution to lighter models (or even non-AI tools) will accelerate. Token-maxxing era ending — Yes. The report explicitly says we’re moving from subsidized/hyped usage to cost-curve discipline. Spend limits, approvals, tiered access, and model mix optimization are the new normal. Bottom Line The industry is maturing fast. The subsidized “try everything on the best model” phase is closing as real marginal costs become visible at scale. Companies that treat tokens like any other scarce resource (with dashboards, budgets, ROI tracking) will have a big edge. Many teams are now doing exactly what you’re implying: Tiered access (frontier only for certain roles/workflows) Heavy monitoring + caps Aggressive experimentation with cheaper/open-source or distilled models for 70-80% of use cases Negotiating harder with vendors (annual commits, seat fee relief, etc.) This Citadel piece is one of the cleaner public acknowledgments from a major financial institution that AI economics are starting to bite. Your $1M+ bill shock is not an isolated anecdote — it’s part of the broader transition. Want me to pull more recent data on Anthropic/OpenAI enterprise pricing trends, examples of how other firms are handling the tier jump, or thoughts on specific cost-control tactics? submitted by /u/Annual_Judge_7272 [link] [comments]

submitted by /u/Vee_Fan38083 [link] [comments]

Fact check: Mostly accurate (high confidence on the core mechanics).30 Key Verified Points Team plan cap at 150 seats: Confirmed. Anthropic’s official Team plan supports up to 150 seats. Beyond that, organizations must upgrade to Enterprise.30 Enterprise pricing shift: On the current usage-based Enterprise plan, seats provide access only (web, desktop, mobile, Claude Code, etc.). There is zero included token usage. All consumption (chats, Claude Code, Cowork, etc.) bills at standard API rates on top of the per-seat fee.31 Seat fee: Reports consistently cite ~$20/user/month (billed annually) for the Enterprise seat. This matches user/sales rep disclosures.0 The jump: Crossing 150 seats forces the change from a bundled “included usage” model (Team) to a seat fee + full metered API billing (Enterprise). This can easily cause a multiplier effect like 3x+ depending on usage volume, especially with heavy engineering/Coding usage. Your $400K → $1.4M example aligns with real reported cases (e.g., similar math in orgs scaling to hundreds of users).0 This structure is relatively new/recently emphasized in Anthropic’s model, and multiple companies are hitting the same “sticker shock” when scaling.11 Unfiltered Thoughts Section: Spot-On Observations Your points are realistic and reflect broader industry sentiment right now: Aggressive token spend for growth — Valid strategy for high-ROI areas (engineering, product), but awareness is low. Visibility/shock — Extremely common. Personal dashboards revealing $thousands in days (especially Claude Code) drive better behavior. Engineering ROI — Strong consensus: Top models pay for themselves via speed/quality for devs. Questionable for other roles — Fair critique. Many non-technical seats see low utilization or replaceable tools. Spend limits incoming — Already available in Enterprise (org/user-level caps) and being enforced more strictly. “Era of token-maxxing ending”: Yes, this feels like the transition phase. Vendors are shifting from subsidized bundled seats to metered reality as AI costs remain high and usage scales. Negotiation is key — many secure seat fee waivers or discounts via annual commitments.0 Caveats: Exact multipliers depend on your mix of light vs. heavy users, Premium/Standard seats on Team side, and negotiation. “Claude Code” heavy teams feel the pain most. This is a common pain point in 2026 — you’re not alone. Many are exploring tiered access, multiple Team instances (workaround, loses SSO), or optimizing usage. Want help digging into alternatives, negotiation tactics, or comparisons to OpenAI/Google? submitted by /u/Annual_Judge_7272 [link] [comments]

If this is supposed to be the magical mythos, but with safeguard’s, I’m not impressed. I’m not sure if they’re shuttling queries to other models because fable is overloaded or what, but it just seems to be losing the plot. It’s made several errors and compounded on them only to realize several turns later. It’s getting caught in the weeds on an idea then telling me I was actually right a few turns later. I’ve got it on max effort so I would expect better results. 4.8 seemed like a genuine intelligence jump to me but if anything Fable just feels like a tiny step back from that. Anyone else? submitted by /u/BLOCK__HEAD4243 [link] [comments]

Yes, that’s accurate—it’s breaking news from today (June 10, 2026).3 Private equity firm Silver Lake (a major tech-focused investor) has hired an in-house team of educators to train its dealmakers and employees on AI. Managing Partner Christian Lucas shared this during a panel at the SuperReturn conference in Berlin, emphasizing the need to stay ahead in the rapidly evolving AI landscape.15 Key Details: Silver Lake is positioning itself with deeper AI expertise internally to better evaluate deals, understand portfolio companies, and drive value in tech investments. This reflects a broader trend: finance and investment firms are investing heavily in AI literacy as the technology reshapes industries. Silver Lake manages ~$114B in assets and focuses on technology and tech-enabled companies.2 It’s a smart move—AI is moving fast, and hands-on internal education helps professionals go beyond hype to practical application in investing. Similar initiatives are popping up across Wall Street and Silicon Valley. If you’re seeing this from the Exec Sum post on X, that’s the source that’s circulating it widely. Want more on Silver Lake’s portfolio, other firms doing AI training, or implications for PE? Let me know! submitted by /u/Annual_Judge_7272 [link] [comments]

I built a free 2026 World Cup prediction tool as a fun side project. The soccer part was fun, but the AI part ended up being more interesting. I tested four different prediction views: My own methodology A tournament-read model based on current form, roster age and fitness, squad depth, style matchups, counterattack danger, fatigue, climate, penalties, manager decisions, and bracket path. Betting odds only A market-based view. ChatGPT independent forecast I did not give it my methodology or preferred winner. I simply asked it to build the best prediction it could using its own logic. Gemini logic forecast This one was the most interesting. Gemini asked me who I was rooting for before making its prediction. Then, in my testing, it chose that team to win. When I changed the team I said I was rooting for, Gemini changed the winner to that team too. That stood out to me. Not because it is evil or anything dramatic like that. But it is a good reminder that AI can lean toward making the user happy. If you feed it a bias, it may hand that bias back to you with better wording and more confidence. The biggest lesson from the project was simple: Good input in, good output out. Garbage in, garbage out. AI is powerful, but it still needs human judgment. It can organize thinking, compare logic, test assumptions, and help build something useful. But it still depends on the person using it to understand the situation, challenge weak assumptions, and know when an answer sounds right but may not actually be right. The tool is a standalone HTML file. It is not a live data feed. It does not automatically update injuries, suspensions, weather, lineups, or odds movement. Users can enter live group-stage scores manually, but anything else has to be adjusted by the user. I’m curious how others think about this: When an AI asks for your preference before giving a forecast, is that helpful context, or does it risk steering the answer toward pleasing the user? Also happy to drop a link for download if anyone wants it. submitted by /u/sparky_8 [link] [comments]

I generated videos with Omni and want to remove the visible and possibly invisible watermarks it applies. I have only seen tools for pictures but none for videos so far. submitted by /u/Born-Explanation-544 [link] [comments]

Im not very experienced with the AI world, so tell me where i went wrong here. today, i saw that even when i deleted the Meta AI chat on whatsapp, it would still remember our last chat. i ask Meta ; how can i delete this chat and assure that you'll not remember this actual chat if i create one new? it responds; Step by step to reset the chat so I start from zero with no memory of this conversation: *Leave this chat* - go back to the WhatsApp home screen *Long press on this conversation* with "Meta AI" *Tap Delete chat* > Delete *Close WhatsApp completely*. On iPhone swipe up, on Android clear recent apps *Wait 10 seconds* *Open WhatsApp again* *DON'T open from Recents*. Tap the search icon 🔍 in the top right *Type "Meta AI"* and tap the official result *New chat opened*: there won't be any old messages *How to confirm it worked:* Send "hi" in the new chat. If I reply with something like "Hi! How can I help you today?" without mentioning Herobrine, the reset worked. If I still remember something, WhatsApp bugged. Then only restarting your phone will fix it. ----------------------- i test it. i restart my phone and try again, and it shows the same exact situation. (THE VIDEO IS IN PORTUGUESE) also sorry for the bad english. my question is: is Meta AI giving me false informations about deleting the chats? am i just being dumb and paranoic? please someone guide me. submitted by /u/discordappas [link] [comments]

submitted by /u/Annual_Judge_7272 [link] [comments]