Sensitive content
This media may contain sensitive content.
Loading video...
Video Failed to Load
Two muscle studs. One beefy bottom. Made for tag-teaming.💪💦 🍆Adam Russo 🍆TonyBeast 🧸 🍑Billy Bones (London, Sept 17-23) Berlin Sept 24-30
55,395 views • 1 month ago •via X (Twitter)
0 Comments
No comments available
Comments from the original post will appear here
Related Videos
0:25
Sensitive content
Such a good boi aren’t you!? Stay on the ground and be a cheeky for me yeah 😈💦👀👟 Billy Bones (London, Sept 17-23) Berlin Sept 24-30 Feel those hot Nikes up close😜 🔥☄️ RT & SEND subs 🤑💰💸 #sub #findom #master #cashfag #cashmaster #tribute #paypig #alpha #nikes #feet #gay #cashdrain #finsub #lick #sniff #worship #moneyslave #fag #slave #pay #kink #goon #send #pay #obey #serve
Master Nikelad1995 (10k+) 😵💫🤑
29,887 views • 2 months ago
![Explainer Video of all the Settings of Hiddentrades - Hidden Liquidity Finder It's quite a long one (44 minutes), but wanted to touch on everything important! If you have any questions or suggestions let me know! Giving away 3 Beta access: just Like, RT and Comment! Overview with timestamps: Core Detection & Filtering [00:30] History: Controls how many past candles the script checks (capped at 10,000). Lowering this number significantly speeds up the script's loading time. [01:32] BB Formation Window: Sets how many candles into the future the script will look to see if an order block converts into a breaker block. [02:25] Partial Mitigation: Defines the percentage an order block can be pierced by price action before the setup is considered fully mitigated and invalidated. [04:48] Use ICT OBs: Enforces stricter ICT (Inner Circle Trader) rules. It requires the displacement to have three consecutive confirmation candles moving in the opposite direction. [06:48] Allow Two Candle Impulse: A modifier for the ICT setting that reduces the required displacement confirmation from three candles down to two. [07:39] Only Show Multi-TFs: Cleans up the chart by hiding single breaker blocks, displaying only the stronger multi-timeframe setups. [08:13] Early BB Detection: Allows breaker blocks to confirm and form directly on the next candle after the displacement, helping you spot setups earlier. Order Block & Gap Configuration [09:30] Strict OB Filter: Makes pivot order blocks stricter by validating the height of the specific displacement candles surrounding the block. [11:24] Requires Same Direction Candle: Ensures the candle immediately preceding the order block is moving in the same direction, creating a true pivot point. [12:18] Use Wicks for Pivot Detection: Uses the extremes of the candle wicks to calculate the pivot low/high, rather than relying strictly on the candle bodies. [13:16] OB Candle Color Filter: Adds a strict color requirement to the candles forming the order block to ensure it perfectly aligns with traditional definitions. [14:08] OB Side Mode (Pivot vs. Chain): Allows the script to follow a continuous chain of fair value gaps (FVGs) rather than strictly requiring a perfect pivot setup. [16:13] Displacement Candle Setup: Explains how the script handles displacement candles that open directly inside the order block (common with market gaps). [17:17] Allow Gaps to Create a Breaker Block: Very useful for trading stocks with pre/post-market price jumps, allowing the physical price gap itself to serve as a valid trigger. [18:42] Body Size Filtering: Allows you to set a minimum percentage size for order blocks, with separate inputs for lower, middle, and higher timeframes. Time Frame (TF) Selection Modes [20:07] Auto Mode: Automatically searches up to 20 timeframes above and 20 timeframes below your current chart to find setups. [22:10] Auto From Selected: Uses the auto-search logic, but strictly limits the search to the specific timeframes you have manually checked off. [23:49] Selected Only: Scans exactly the timeframes you select, regardless of the timeframe you are currently viewing on the chart. Visual & Chart Display Settings [25:04] Merge Nearby Breaker Blocks into Clusters: Combines multiple overlapping or nearby zones into one clean, unified cluster zone. [27:27] Show Order Blocks Inside Cluster: Hides isolated single order blocks, only visualizing those that contribute to a larger multi-timeframe cluster. [29:03] Distance Filter: Instantly hides any setups that fall outside a set percentage range (e.g., 10%) from the current market price. [30:01] Show Forming Breaker Blocks: Displays upcoming setups in a faint gray color before the final confirming candle has officially closed. [31:52] Hide Mitigated BB Always: Automatically removes breaker blocks from your screen the moment price action fully mitigates them. [34:10] Show Recently Deleted Breaker Blocks: Keeps failed or mitigated blocks visible in gray for the last 240 candles — an excellent feature for backtesting. [36:03] Text Offsets: Adjusts the padding of the text labels so they sit cleanly away from the chart blocks without overlapping. [37:39] Merge Overlapping Labels: Intelligently combines the text labels of two nearby breaker blocks into a single centered tag to prevent visual clutter. [39:23] Colors and Opacity: Customizes block colors and automatically fades the opacity of lower-timeframe blocks while keeping higher-timeframe blocks solid and prominent. [41:32] Bold Text Label: A simple toggle to bold the chart labels for easier reading.](https://image.24vids.com/tw-2064354929352028612/amplify_video_thumb/2064354230358712320/img/8lWn5XoKZqfVO9L9.jpg)
![An interview by VERY DARK AND CORRUPT Wall Street Journal aired today [1] WSJ's terrible "journalists" (and I use that term lightly) made many false statements about Sarepta's worthless, dangerous drug and Vinay Prasad's firing [1,2] I explain how the FDA sausage is made in excruciating detail Buckle up To get readers up to speed -> In June, corrupt pharma company Sarepta Therapeutics paid $40,000 to lobbying group Michael Best Strategies (MBS) to deal with a problem [3] -> MBS had recently hired Chris LaCivita, who had close connections with "MAGA" influencer Laura Loomer [4] -> With stock down 88%, Sarepta needed to sell their very bad, very dangerous drug or the company would go bankrupt [5] -> After several deaths from the drug this year, FDA official Vinay Prasad said "no way" and kicked the drug to the curb [2,6] -> Sarepta panicked and paid MBS (we believe) to deal with Prasad [3,4] -> If this story is right, LaCivita recruited Laura Loomer to take down Prasad [4,7] -> Loomer said she was defending Trump, but she was lying [7] -> She was defending taxpayer-funded payouts to a worthless, corrupt company [7] -> Laura Loomer so brave A history of bad drugs and regulatory failure -> This is one of the worst pharma scandals in American history and corrupt mainstream media isn't covering it -> Sarepta has a very long, troubled history [8] -> For more than a decade, every major Sarepta FDA drug approval has required INTENSE political intervention [8,9] -> Scientists at FDA have been repeatedly overruled [8,9] -> Many scientists have resigned, very publicly, over these POLITICAL decisions, some writing scathing public criticisms of these terrible decisions [10,11] -> The most recent resignation by Vinay Prasad is not something new; it follows in a long tradition [2,10] -> In fact, standards have dramatically deteriorated since the first controversies about the company's drugs in the 2010s [8,9] -> Prasad was trying to hold the line in the face of rapidly deteriorating standards at the agency [2,6] -> For that, pharma launched a coup--a literal coup of a drug regulator [4,6] -> This is unprecedented -> Banana republic sht, unbelievably corrupt 2016: first Sarepta drug approval and the "highly unusual" decision -> The first Sarepta drug approved by FDA was called Exondys 51 [8] -> This drug was for patients with mutations in dystrophin, a muscle protein [8] -> This is a debilitating and fatal disease affecting children [8] -> Exondys 51 increased dystrophin by 0.2% of normal levels [8,12] -> Unsurprisingly, there was no good evidence the drug worked [8,12] -> Why would it? It increases the protein from zero to 1/500th of normal levels -> One reviewer wrote: "I can find no precedent of an accelerated approval for a marketing application where the effect size on the surrogate endpoint is as small as 0.3%." [12] -> The study submitted by the company included no proper control group [12] -> The techniques used were so bad not even a first-year PhD student would do a study that way -> This the level of work you would expect from a mediocre undergraduate with no guidance -> It's almost like it was so bad on purpose -> (Narrator: it was on purpose) -> Nerd time: -> One reviewer wrote: "The Western blots submitted by the applicant for Study 201 were oversaturated, unreliable, and uninterpretable." [12] -> Another wrote: "Because CDER also determined that the conditions under which the original IHC analysis was performed were inadequate, including that the reader was not masked to sequence and time, the Center requested a re-reading of the stored images by three masked pathologists under different conditions. The IHC results from the reread were not nearly as favorable, as compared to the initial IHC results reported by Sarepta." [12] -> "The lack of concordance between the IHC and the Western Blot results is 'striking'" [12] -> "Study 201/202 had fundamental flaws, including baseline biopsies from external controls who could differ in unknown ways from study subjects, Week 180 biopsies from different muscles than baseline, and potential protein degradation in stored baseline samples." [12] -> And on and on. -> FDA commissioner Robert Califf wrote at the time: the submitted study was "characterized by major flaws in the clinical study design" and "Blinded experts assembled by the FDA fundamentally debunked this study, which has yet to be retracted and continues to be cited" [9,12] -> That's right, the FDA commissioner expressed dismay that the study that the company used to gain approval hadn't yet been retracted, it was so bad [9] -> Senior FDA official Janet Woodcock decided to approve before scientific review team had even voted [9,12] -> Woodcock be like: yeah i'm going to decide before you guys can because i know what you're going to say lol -> Despite external intense pressure, FDA scientists voted against Exondys 51's efficacy [9,12] -> They then voted against its accelerated approval [9,12] -> The review team filed an appeal with FDA commissioner after "passionate" disagreement with Woodcock [9,12] -> One reviewer called Woodcock's decision "unprecedented" [12] -> In a 126-page report, FDA commissioner Califf called Woodcock's decision "highly unusual" [9] -> The FDA board wrote: "[Woodcock's] involvement here appears to have upended the typical review and decision-making process. ... Care should be taken to avoid the appearance of interfering with the integrity of scientific reviews at the lower levels of a Center." [9] -> Again, the data were unbelievably bad, literally every technique in the study was inappropriately used [12] -> I would fire an undergraduate student who did science like this, immediately -> FDA's chief scientist accused Sarepta of "serious irresponsibility" for selectively publishing only some of the data [9] -> Even Woodcock, who approved the drug, called the research "seriously deficient" [12] -> Yes, even the person who approved the drug over the heads of FDA's scientists said the research was horrible [12] -> Still, FDA tried to bury their heads in the sand and beg that, basically, Sarepta pretty please do a better job next time -> FDA commissioner: "The utmost attention should be paid to optimizing the methodological rigor of [future] trial[s]" [9] -> FDA also demanded a clinical trial "to verify the benefit" of the drug [8] -> Welp, this was in 2016 [8] -> The trial results are supposed to be available in 2026, maybe [13] -> Or maybe later, depending on how much money needs to be made first -> As an article published in Nature three years later despaired of the decision: "The approval was conditional on the company agreeing to conduct a two-year post-approval trial to show Exondys 51’s efficacy. But by August 2019, the company had yet to begin such a trial and in the meantime had profited from sales of $300 million in 2018." [13] -> If it sounds like Sarepta used political pressure to get its drug approved and then tried to avoid actually publishing the study showing it didn't work, it sounds that way because that's exactly what happened [13] -> FDA commissioner after deferring to Woodcock: "I am confident this unique situation will not set a general precedent for drug approvals under the accelerated approval pathway, as the statute and regulations are clear each situation must be evaluated on its own merits based on the totality of data and information." [9] -> This statement was profoundly naive, and the historical record bears this out [8,14] -> Three FDA scientists resigned, including the lead reviewer of the drug, understanding the grave implications of the collapse of scientific standards and where they would lead [10,11] -> One was John K. Jenkins, M.D. Director, Office of New Drugs Center for Drug Evaluation and Research/FDA [10] -> In a presentation given just before his resignation, he wrote: -> "Path taken by Sarepta NOT a good model for other development programs" [10] -> Crucially: -> "Upholding statutory standards for approval in face of hopes and desires of patients, families, sponsors, and investors is a very difficult job" [10] -> "Personal attacks on FDA reviewers creates an atmosphere of distrust and isolation rather than collaboration" [10] This brings us to WHY Sarepta's drug was approved Facebook FDA -> So why did the drug get approved? -> Basically, Sarepta propagandized extremely desperate patients [9,15] -> They used miraculous snake oil promises and patients believed them -> Remember that this is life or death for patients, and they are extremely vulnerable -> Sarepta also professionally trained some patients to give testimonials to FDA and congress [15] -> The patients then went to congressmen who don't have time to understand the science [15] -> They gave emotional stories to congressmen [15] -> The result: -> Letter from 109 House members [15] -> Letter from 24 Senate members [15] -> And a media circus documented in the New York Times [16] -> Patients screaming at scientists during meetings [9] -> 2,792 emails written to FDA urging approval [12] -> One of them: "Dear Dr. califf: How is it that everyone in and around DMD understands this simple Idea and the science geniuses at FDA don't? You stupid fckers are costing each and every DMD kids days of their lives with your Moronic Dystrophin dance. Time to get a fcking clue" [12] -> Upon approval, a journalist for Reuters wrote: "owing to pressure from patient advocates, the U.S. Food and Drug Administration on Monday approved a treatment for Duchenne muscular dystrophy even though an outside panel of experts and the agency's own reviewers questioned the drug's efficacy" [17] -> A commentary in Nature Medicine was also published called "Railroading at the FDA" [9] -> Its author wrote: "In the words of one FDA committee member, Exondys lowers the agency's evidentiary standard for drug effectiveness 'to an unprecedented nadir.'" [9] -> A highly critical commentary was also published in Science, titled "Sarepta gets an approval - Unfortunately" [18] -> The article's author pharma veteran Derek Lowe wrote: "The company... called up Duchenne-affected boys and their families to plead with the FDA, and won over Janet Woodcock, and that appears to be enough. Is this going to be the new way to get a drug approved? Run a trial in a dozen people, generate unconvincing data, and then lobby Janet Woodcock? I share the worries that this might open the floodgates, because after all, Sarepta got their drug through." [18] -> One FDA reviewer ended in an equally grim note: ". Approval of this NDA would send the signal that political pressure and even intimidation – not science – guides FDA decisions, with extremely negative consequences. The public is well aware of this development program: the meager size of the study population, the marginal (at best) effect size, the Division’s dim view of the efficacy data, and the robust activism of some members of the DMD community. Many would be amazed at an approval action, because other DMD drugs, recently turned down for approval, appeared to provide stronger evidence of efficacy. ...The ramifications here are profound. The public will perceive that it was their unprecedented lobbying efforts that made the difference and earned eteplirsen its accelerated approval. For the future, this will have the effect of strongly encouraging public activism and intimidation as a substitute for data, which is one of the worst possible consequences for communities with rare diseases. This type of activism is not what was envisioned for patient-focused drug development." [12] -> A new era was born -> Activism had replaced data -> Facebook had fried people's brains -> And now Facebook-fried brains had fried FDA too -> FDA's credibility as a regulatory agency would now be hollowed out -> FDA's Facebook age had begun -> But the worst was yet to come Sarepta approvals: 2016 to present -> Three more drugs were approved from Sarepta on the same shoddy basis, proving Califf's promises that Exondys 51 was an isolated case empty [8,14] -> But things would take a turn for the worse with Sarepta's newest drug Elevidys in 2024 [19] -> At last a rigorous clinical trial looking at actual clinical outcomes was published [19,20] -> All would be put to rest -> At long last the issue could be resolved with HARD CLINICAL DATA -> There was only one problem -> The trial failed to show any benefit according to the primary outcome [19,20] -> The surrogate biomarker of micro-dystrophin meant absolutely nothing; it wasn't actually helping patients [19,20] -> What did FDA scientists do? They voted against approval. Of course [19] -> How could they not? The drug didn't actually work in the clinical trial [19] -> It's the only thing that made sense, since FDA is a scientific agency -> AND THEY WERE OVERRULED AGAIN BY PETER MARKS [19] -> YES THAT'S RIGHT, OVERRULED YET AGAIN -> PHARMA WINS AGAIN -> HAHAHAHAHAHA PHARMA ALWAYS WINS YOU FOOLS -> What happened is that Marks crossed his eyes somewhat, trying to make the words on the page blurry -> He prayed really hard, "my god please give me a sign, something, anything, I need this for my career" -> lzzosolsolzzolzozlslzolosllslozllzlzl -> Marks was trying really hard to see SOMETHING, come on come on, give me SOMETHIGN he said -> And he said: wait, look, there are these secondary, exploratory endpoints and a two of them look pretty good, I'LL APPROVE [19,20] -> AHAHAHHAHAHA YES PHAMRA WINS AGAIN -> And Marks said, "Thank you pharma go- I mean god, not pharma god, why did I just say that, FCK" -> The trial was explicitly designed for what Marks did NOT to happen [20] -> Once the primary endpoint was not met, the secondary endpoints couldn't even be statistically tested [20] -> And the trial explicitly said that they could not be interpreted the way Marks interpreted them [20] -> They were not adjusted for multiplicity and they were, like expression of dystrophin, simply bad endpoints [20] -> These two secondary endpoints were time to rise from lying on the floor and the 10-meter walk/run tests [20] -> Subjects who received the Elevidys performed, on average, about 0.5 seconds better than placebo recipients on these tasks [20] -> However several facts must be borne in mind when interpreting these: -> 1. At the time of testing, patients receiving the drug were receiving more corticosteroids than placebo patients, biasing the results [20] -> 2. Blinding might have been broken because those receiving the drug experienced lots of nausea and vomiting from the drug (~70%) [20] -> 3. These differences were tiny and may be attributable to chance, since the natural course of the disease varies widely [20] -> Marks knows this but who cares? Pharma I mean Facebook needed to be placated Elevidys: the drug -> To understand why this is so messed up, one must understand a few things -> On a Bayesian basis, one must assume that Elevidys is harmful until proven otherwise, for two reasons: -> 1. All drugs are potentially "toxic", but some toxins heal: by default you must assume it is a toxin that does not heal because this is what is actually usually the case; you need evidence that it actually heals -> 2. Elevidys IN PARTICULAR must be assumed to be harmful until proven otherwise because of the very nature of the drug -> Let's do a breakdown of the basic science of Elevidys that supports this (Bayesian) hypothesis: -> Gene therapy that permanently integrates into human genome [21] -> Meant to replace dystrophin, the protein that these patients cannot produce themselves [21] -> Preferentially targets muscle but gets expressed everywhere [21] -> Killed three people this year [6,21] -> Costs $3.2 million per injection [21] -> Truncated version of the protein it is supposed to replace [21] -> 3X shorter than the real protein [21] -> Has to be truncated because the technology cannot create the full protein [21] -> Because it's an abnormal protein, it's foreign, so immune system attacks it [21] -> Patients injected with drug are basically given an autoimmune disease [21] -> Patients have to be given anti-inflammatories to fight the disease that the drug causes [21] -> Causes terrible muscle inflammation [21] -> Inflames the heart, heart walls thicken because of the inflammation [21] -> Blows up the liver, causes acute liver injury and death [21] Drug should actually be assumed harmful, not beneficial -> Given all of the above, since the drug failed to meet its primary endpoint, it should actually be considered harmful by default, not beneficial [19,20] -> In other words, what we would actually expect if we added more patients and did an even larger study... -> Is that the drug would do worse than placebo, i.e., patients taking the drug would do worse than those taking placebo -> Why isn't this the default interpretation? -> They are reading the study with an intervention bias -> An intervention bias is natural, which is why "do no harm" is such a central tenet of medicine -> If I may put forward a thesis: most of Vinay Prasad's 500+-paper body of work has been dedicated to demonstrating the "do no harm" principle empirically [22] -> Rose-colored glasses study interpreters are simply not applying this principle properly and are thus failing scientifically in the most fundamental way -> Incomprehensible -> Back in 2016, scientists were adamant that the approval of Sarepta's first drug indicated the profound deterioration of scientific standards [8,9] -> But this latest approval is even worse: actual clinical data is now being overruled -> No standards at all are being enforced anymore; anything can now be approved based on any evidence whatsoever -> What Vinay was trying to do was simply to stop the unrelenting downslide -> And his firing punctuated that downslide for what it was The WSJ segment -> When Elevidys was approved, former FDA chief scientist and one of the original reviewers of Sarepta's first drug Luciana Borio said: -> "I don’t know what to say. Peter Marks makes a mockery of scientific reasoning and approval standards that have served patients well over decades. This type of action also promotes the growing mistrust in scientific institutions like the FDA." [23] -> To return to this video, these two WSJ reporters show an incredible level of ignorance and arrogance -> Finley says that the drug is "clearly" beneficial by misreading the secondary endpoints, just like Marks did -> An FDA memo from last year says about these endpoints: "Under these circumstances, they are misleading and cannot guide any stakeholders—including patients, family members and caregivers, and prescribers—in making informed decisions about the potential benefit of treatment with ELEVIDYS." [20] -> It really doesn't get any clearer than that -> But these two journalists are overruling the actual scientists, just like Marks did -> One of the most incredible comments during this interview was the complaint that "90% of clinical trials fail", as if that's bad thing [1] -> It's actually a good thing; most drugs suck; failing in clinical trial actually allows us to use only the drugs that don't suck -> These people don't understand the most fundamental purpose of the clinical trial -> They think clinical trials failing is a bad thing, as if it means that patients now won't get to use a useful drug -> No, it's a good thing, because it means that patients won't be exposed unnecessarily to a useless drug that might harm them -> The level of ignorance really is unbelievable -> What's worse is that these "journalists" defend their decision -> But what they did is exploit social media hysteria caused by Laura Loomer [1,7] -> Following up on her heels with editorials, using her as pharma attack dog [1,4] -> This is a huge blow to WSJ's credibility, and they know it -> Unbelievably shameful Where do we go from here? -> The Vinay Prasad firing creates a serious crisis of credibility at FDA [2,6] -> Up to this point, we could call these approvals a difference of opinion, but as we've seen, that's a huge stretch -> But any illusion of that is now shattered: the firing shows that drug regulation is explicitly political -> Janet Woodcock: approve, keep job -> Peter Marks: approve, keep job -> Vinay Prasad: block, transparently fired -> Make a decision that is anti-pharma and lose your job: that's the message -> Who can trust any decision at FDA anymore? -> RFK Jr. and Marty Makary both stand behind Vinay Prasad [24] -> Trump went along with lockdowns, he went along with mask mandates, he went along with all of the Covid pseudoscience that he now decries -> He should reverse course and not go along with this -> Trump has created a profound crisis of credibility at FDA and needs to fix it](https://image.24vids.com/tw-1952294845248114806/amplify_video_thumb/1952292444621451264/img/SPXp7ZpvQZ3FQV7_.jpg)
![The most epic 13 minute AI rant I've heard in 2026 PS: My parent's heard this when I was playing it in the car and thought Jason ✨👾SaaStr.Ai✨ Lemkin went OFF like Stephen A Smith does on first take PPS: Full transcript below [17:00] Harry Stebbings: I I just wanted to ask Jason, if the people that we want are fundamentally different, the developers that we used to hire, we don't because AI writes the code for us. The marketers we don't want, the sales people we don't want—who who do we want genuinely? Like what is the attractive profile? Because your Anthropic’s and your OpenAIs are hiring, so so what are the people that we want in the companies of the future? [17:18] Jason Lemkin: Look, I know it sounds trite, but but the answer is simple. It's just the expression each year changes. We want folks that are genuinely AI fluent. It's pretty simple. Now you know, maybe last year we called them prompt engineers, right? That used to be a job. I don't know if you remember that actually used to be the hottest job on planet earth. Now no one needs a prompt engineer because it's pretty easy to prompt all these tools. That job died. Okay. Um and now we need go-to-market engineers. Um I think that job's going to die. We need—everyone needs so many forward deployed engineers. Like you can't hire enough forward deployed engineers. But uh you know um but Palantir just announced in whatever their their big their big event—they've gotten their deployment times down over 90% with forward deployed engineers. So that may become—so the this wave of disruption for the titles and the specificity, it's also exhaustingly accelerating. But it's really simple. You meet anyone for any role—sales, marketing, engineering, product, QA—they're they're either they're either they can't keep all of the ways they use AI to accelerate their job from spewing out of their mouth, or they're staring at you. It's there's nowhere in the middle. Like, and the person that comes in and says—it's it's it sounds Captain Obvious—but like, you know, you just had the whatever from Lovable, the the marketing head that was super popular on the show, right? She's just spewing AI-native insights into Lovable, right? It's not that complicated. You hire her, Elena, or whatever it is. You just hire her. It doesn't matter whether she's still in college or a junior or a senior or a middler, a left or right. And honestly, if you interview people, I would say of all even of the best startups I've invested in, maybe 30% of the management team meets this standard at best. 30%. Maybe less. And of the interviews I do in general, it's single-digit percents. It's just and in in that sense, it's the same as ever. Like you either lower the bar in hiring or you hire someone that's actually great. And someone that's actually great is so far ahead of you in how to apply to to employ the efficiencies of AI in their role, your jaw falls on the table. The difference is we used to need warm bodies. That's what's changing. We used to need warm bodies to answer the call, to do QA, to do code review, to to get the blue pixel to go from the upper left to the lower right. You laugh, but you need you literally needed to brute force this with humans. With AI, every day that goes by, the AI—you do not need brute force human beings on your team. And that's another reason they're shrinking. Why are all these new companies so efficient? They're just not brute forcing things with humans. They're just not. They're choosing not to. And so these team—all the brute forcers out there—everyone talks about how bloated teams got in 2021. I don't agree with that. I think they got as big as they needed to be when growth was high and you needed humans to do everything. All you look at these teams that that doubled—well if growth continued at 60% like the rate in early 2021 for 5 years or can help me do the math and every single thing a software company did required a human. You were understaffed by your 2021 headcount. You'd be sitting here in 2026. You every office in SoMa would be triple packed and you there wouldn't be enough humans to staff your company. It's just the world changed. [20:33] Harry Stebbings: Jason, you live on the bleeding edge. I think me and Rory see that and I think the world sees that when they hear you every week in terms of how you run SaaS. For all of the CEOs and execs who listen to the show, what would you advise them in terms of determining whether someone is AI fluent when they meet them for jobs, for talent? [20:51] Jason Lemkin: Here's I realized I was just asked this. I just did a review with a super fast startup growing just crossing 100 million and I was asked this question. And one of my favorite executives, I thought his answer was pretty dated and because he gave me an answer that was about 6 months old. The answer 6 months old is: "I look for folks in my team, I look for you know at what tools they play with." Okay, that was a great answer in like summer of 2025. Okay, I tried Lovable last week. Okay, the answer in 2026 is: "What commercial AI tool have you brought into your organization this month?" That's the test. Anyone that is on the bleeding edge that you would want to hire—now there are so many great products in the market. Okay, there is no excuse in any role to have not brought one tool a month into your organization. Okay, there—now there's going to be better and better tools and better and better products as the year goes on. What's the one you did? And you will see folks with their deer in the headlights to this question. What what sales tool? What marketing tool? What product tool? What engineering tool? What did you bring in? Why did you pick it? How does it working? Because if you're at remotely at the cutting edge, you're all over this. You're looking for the next agentic tools that will radically improve how you do business. This is—you think everyone thinks SaaS is at the bleeding edge, right? You know, you know, all we do is we're just looking for the tools and trying them. Okay? Okay, we're one year ahead of everybody else because we did the simplest thing in the world. Like we tried the tools early and we trained them. We trained them for a month. Okay, I'll give you—want hear a horrible example from this week? Super hot AI company valued at 6 billion. Okay, I'm not going to name it. Um, this week yesterday told us we had to quadruple what we spent on their product. Okay, their agent told us, right? And why did this happen? Okay. Well, at this $6 billion company, no one had trained the agent on its pricing properly. No one had tested it. They said, "Well, well, we've been in beta." And we said, "Well, when did the beta launch? A year ago." Okay, these are people asleep at at the wheel. You want somebody who the instant this comes up, they exactly know what the issue is. And "Hey, when I was at Lovable Replit, we trained the agent. This is how we did it. I brought in this tool. I brought in this tool that that Rory invested in last week. It solved all these issues." That's what you want to hear. And if they haven't brought in a tool in the last 30 days, at least deeply evaluated it. I don't really care whether they bought it, but gone so far down the funnel they can tell you—pick whatever tool: Fixie, Regie, GC, AIGC—I don't care how you went through it, you looked at it, you can tell me the eight ways it would improve the productivity of your business and three you didn't. Just don't hire that person because they're going to run your company to the ground. This is the job today. The job today is not to screw around on ChatGPT and to be a prompt engineer. The job today is to bring the best AI and agentic products into your organization and leverage all the hard work that the engineers have done building those products. That's your job. You don't have to screw around. You don't have to be a prompt engineer anymore. You have to be an agent deployment expert. A—this is the new job we're making up today. An Agentic Deployment Expert. That's your job from C-level to junior. Agentic Deployment Expert. Don't hire anybody else. You're going to regret it. They're going to stare at the camera. He's good. Stare at the camera. He's honorable. We could probably just I could slip away, get a coffee, and come back. No. And I I sound exasperated, Rory. And I—but the reason I am is I can just see I can see my best companies doing it. And I can see some companies I've invested in not doing it. And I want to cry. I just want to cry when they have no ADs on their team. I just—like you're flushing your years of your life down the toilet by not approaching your how you're building this company this way. [24:33] Rory: Yes. And at the risk of being positive, it's worth pointing out two things he didn't say. Well, something implicit why he said—Jason didn't do the only hire, you know, he didn't commit the um employment law, I think it's a civil penalty of saying only employ people below X who get the new new thing because he implicitly said anyone can do it provided you're willing to learn. And I think that's the big aha that's one of the positive statements to make here right? Look and I think it applies—I'm always wary of being "Hey, coming across, hey this this is the things that you all have to do." I think it applies to everyone including investors right? I mean I will say I have found that unless you're willing to invest the time learning these tools you actually shouldn't be investing in them. One of my partners Andy had this expression: "You know, if you decide you want to stop learning new things you probably should retire within 6 to 12 months and never write another check again." Maybe that's down to 3 to 6 months at this stage, right? And I think, you know, it's— [25:27] Harry Stebbings: Yeah, I actually I actually had a meeting with mine and Jason's biggest investor the other day and I—pretend he's not here—I said I think he's the most equipped investor for this generation of investing because I don't think anyone quite sits at the bleeding edge like he does on the investor side. [25:42] Harry Stebbings: Why in terms of using the equip stuff? Yeah. Yeah. In terms of using the stuff, understanding understanding bottlenecks, constraints. For sure. [25:51] Jason Lemkin: But can I just add one point? We can just cuz it's so important if it helps people. Okay, we are—and thank you Harry. We're going through these phases. Okay, and when AI started to blow up for real for us, uh call it early 2024, right? Maybe late '23, I wasn't equipped. It was too technical. I wasn't going to go in and figure out—I wasn't smart enough to figure out how to deal with a massively hallucinating LLM API and turn that and turn that into something magical. Kudos to investors and others that that got it in early '23, '22. I mean I remember I—I guess it was maybe SaaStr Annual '23. I was with David Sacks and I did a Q&A and I said, "How you thinking about AI at Craft?" He's like, "Well we're all in. We want 80% of '23 of investments to be AI." I'm like, "Great but like show me the show me the great ones in market." He's like, "They're all prototypes. We're all they're all they're all proof of concepts but we're all in anyway." That's where you kind of had to be in '23 if you weren't investing at like the LLM level. Okay, I wasn't smart enough. Then we went through this weird-ass prompt engineer era where like you you could torture these products to do something good, right? But you had to torture them. You had to like craft these crazy things that made no sense. Now we are in the era where mere ordinarily smart generalists can make these tools do magical things. And literally I go to these meetings and people be like, "I don't know how to like this is so scary. I don't know how to do this." And we show them our backends. Do you know how to do a workflow generator? Do you know how to do a a decision tree? Like we've been building these since software in the '90s. Okay, if you—I can show you all of our agents. The how they work is novel. They do have to be trained. You can't be lazy and have these agents work. But honestly, the the UI, the UX, the way we interact with them, it's just software. And so my point is: Pick yourself off the ground. This is your time now. If you felt lost in AI era, if you felt like you're behind, you don't understand what all these people are saying on X and Twitter and their Claude and and their and talking about all the 4.6 point Nano point and it's over—like you just it's not your world. This is your time. This is your time for the generalist that knows how to use software tools really really well. And I—this is my last point but it's so important. If ever in your recent life—and this is why you could be all you need to be is young at heart to Rory's point—if in the last three to five years you have successfully deployed a piece of enterprise software of any sort you yourself, not some agency you hired, but if you have deployed it, you can deploy any agentic tool. Any. And you can become the hero in your company and you can become the hero in your functional area. But I watch folks—I'm literally helping a company now that they're adding hundreds of sales folks this year with a new pre-IPO COO—he's not hasn't brought in a single tool, totally scared of it. Okay, it's not that hard. Did you use SalesLoft? Did you use Outreach? Did you use HubSpot? Do you know these tools? If you can deploy these tools, you can deploy a world-changing AI agent. And so this is the time for people like the folks that that were shut out of the AI revolution right now. The generalist folks that are not that know how to deploy software that don't even know how to build software. Like vibe coding for me was folks who knew how to build software, but you didn't have to be an engineer. Now, you just need to know how to deploy software to win with AI agents. That's all you need to know. So many people have these skills and they're petrified of AI. "How did you do that? How did you deploy an AI BDR?" Well, we bought a piece of software, we figured out how it worked for a day, we set it up in an afternoon, and then and then we did spend 30 months training it, which you didn't do with this old software because in the old days, we just had to manually upload all the data, right? And there was no training. The the only non-intuitive part is training these things. And it's it's it's just work. So that's why when I see folks on the management team not doing this, there's no excuse. You do not need to be technical to win with AI agents in Q2 of '26. You do not need to be even 1% technical. Not at all. So it's your time. Or you're going to get laid off. Or you're going to get laid off because you're not going to matter.](https://image.24vids.com/tw-2036795243681423518/amplify_video_thumb/2036794127560613888/img/52_cdbzzL6_foTSI.jpg)