Podcasts about ai ai

現在人人都在用 AI，為什麼有人能靠它解決棘手的工作問題，有些人卻總是只能得到空泛的答案？這集 Bryan 邀請台灣首位密涅瓦大學（Minerva University）決策科學碩士李佳達，從企業導入、主管管理到日常工作案例，聊聊如何找到真正的痛點、定義問題、問出好問題，讓 AI 成為你的思考夥伴，協助我們看見盲點，做出更好的判斷。 ​ 【本集節目由 師父線上課程平台 贊助播出】 這集我們邀請到台灣第一位取得密涅瓦大學決策科學碩士的李佳達老師 台大法律、交大科法所、哈佛訪問學者，做過法務長、行政院機要，經歷企業上市、國際併購等重大決策。 ​ 為什麼在 AI 時代，真正值錢的不是你學了多少工具，而是底層的思考邏輯？ 透過上百場一對一諮詢，佳達老師看到大家共同的痛點，帶你少走彎路 ​

「DIME電子版増刊号を配信中、特集は酷暑対策、白物家電、ガジェットまでメリハリ視点で選ぶ「夏のベストバイ」」　物価高が続く中で「できるだけ満足度の高いものを」「同じ性能なら安いものを」と考えている人は多いだろう。今号はそんな視点をふまえてのベストバイ大特集！　今夏におすすめの酷暑対策ギアを始め、家電、AV機器など〝PREMIUM〟もしくは〝BEST VALUE〟な製品を厳選してご紹介。さらに投資初心者のためのAI銘柄分析、DIME40周年スペシャルコラボ企画として『機動警察パトレイバーEZY』のオリジナルグッズも遂に発売開始します！dマガジンはコチラ単号での販売もしております↓一点豪華主義のPREMIUMか？　高コスパなBEST VALUEか？酷暑対策、白物家電、ガジェットまで、メリハリ視点で選ぶ！夏のベストバイ55物価高が続く中で「できるだけ満足度の高いものを」「同じ性能なら安いものを」と考えている人は多いだろう。そんな視点をふまえて、今夏におすすめの〝PREMIUM〟または〝BEST VALUE〟な製品を厳選してご紹介！株価にも業績にも期待大！投資初心者のためのAI銘柄入門日経平均株価が7万円を超えた要因のひとつが「AI関連企業の急伸」であることは間違いない。AIは単純なブームではない。日本を引っ張る新しい業界の中心企業18社を総まとめ。新作『機動警察パトレイバー EZY File 2』公開直前、32年ぶりの読み切り漫画でも話題沸騰！令和のパトレイバー旋風に迫る！1988年の誕生以来、人気を博してきた『機動警察パトレイバー』。今夏には劇場版最新作も公開予定で、今年はかつてない盛り上がりの年といえそうだ。2026年下半期はこのキーワードに注目！DIMEが選ぶトレンドワードBEST122026年下半期、私たちのライフスタイルやビジネスシーンはどのような変化を迎えるのでしょうか。本特集では、DIME編集部が選んだ注目すべき「12のキーワード」から、これからのトレンドを読み解くヒントを探ります！dマガジンはコチラ単号での販売もしております↓構成／DIME編集部

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I paid $2.40 per lead and I didn't create a single ad. Claude AI built my entire ad campaign: the images, the carousels, the video clips, the copy. I approved, rejected, and directed. That's it.In this video, I show you the real results from 5 days of running ads that Claude AI created for me, 288 leads at $2.40 per lead on cold audiences and $5.62 on warm, and the exact workflow I used: feeding Claude my own content, letting it build the campaign, and staying in the director's chair while the machine executes.You'll learn:• How I used Claude AI to turn my own content (webinars, transcripts, videos) into a complete ad campaign• The approve/reject workflow that keeps YOU in control as the director• Why warm audiences cost more than cold ($5.62 vs $2.40) and why they're worth it• How I'm winning back the email subscribers who unsubscribed from my list• Why "I don't know how" is no longer a real obstacle in the era of AIAI is not the opportunity. AI transformation is. I am opening up the way I engineer, architect, design, build, and operate with AI inside my own businesses so you can see what is possible and build with me.Join here

「楽天グループが攻撃型ドローン国内導入へドイツ企業と提携へ　9月末までに実証実験行い防衛省への導入を支援」　楽天グループが、ドイツ企業と提携し、AIを搭載した攻撃型ドローンの国内導入の支援に取り組むことがわかりました。楽天が提携するのはAIを使った、攻撃など向けの自律型ドローンを開発・製造するドイツのスタートアップ企業「ヘルシング」です。ドローンは、陸上から遠隔操作し、沿岸部に近づく対象物などを阻止する目的で使われることが想定されています。楽天が、日本でのドローン販売の窓口となって、9月末までに実証実験を行い、防衛省への導入を支援します。楽天はこれまでドローンを使って、外壁や屋根を調査するサービスなども展開していて、防衛産業の需要の取り込みを狙う考えです。

"If you're not completely sure about what you want to try to achieve, how will you know you ever achieved it?" - Joseph Postiglione, Sr., Author, Protecting Expected Outcomes in the Age of AI AI investments are accelerating, but many organizations are still struggling to turn promising pilots into the business outcomes they expected. The technology may work as intended, but the original business case can quickly get lost once implementation begins. In this episode, Philip Ideson welcomes back Joseph Postiglione, author of Protecting Expected Outcomes in the Age of AI: Why AI Implementation Must Evolve - And the Emergence of Commercial Control. Drawing on his experience in management consulting and procurement, Joseph explains why organizations need to be more intentional about defining, monitoring, and protecting the outcomes behind their AI investments. Joseph introduces the concept of "commercial control" as a complement to traditional technology implementation methodologies. He and Philip explore what it takes to build stronger business cases, why efficiency gains need a clear plan for how freed capacity will create value, and how AI itself could help organizations identify when expected outcomes begin to drift. They also discuss an important opportunity for procurement: bringing their commercial expertise into enterprise-wide AI investments and helping the business connect technology decisions to measurable results. In this episode, Joseph discusses how to: -Define expected outcomes before an AI implementation begins -Build business cases that go beyond savings and efficiency metrics -Monitor performance and identify why expected outcomes are drifting -Use commercial control to create earlier opportunities to intervene and adjust -Position procurement as a valuable contributor to enterprise technology investments -Turn stronger AI implementation discipline into a source of competitive advantage   Links: Joseph Postiglione on LinkedIn: https://www.linkedin.com/in/joepostiglione/  Subscribe to the AOP Newsletter: https://resources.artofprocurement.com/art-of-procurement-podcast-subscribe  Subscribe to Art of Procurement on YouTube: https://www.youtube.com/@ArtofProcurement   

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在人类视线之外，海洋深处正进行着一场“无人值守”的工程作业。跨洋通信、海上风电、油气开采等领域，都离不开一群鲜少被看见的“深海工人”——工作级深海机器人。它们潜入数千米深海，在极端高压、低温和黑暗环境中，完成探测、定位、安装与设备维护。但长期以来，受限于水下通信能力，这些机器人仍需要通过缆线连接水面进行远程操控，庞大的支持船舶也让深海项目成本居高不下。如今，AI正在改变这一模式：让机器人具备自主感知、决策和协作能力，并实现长期驻留海底，未来协助人类探索海洋中尚未被发现的未知世界与资源。本期节目，我们邀请到深海智人创始人马亦鸣与纪源资本投资副总裁吕一然，拆解工作级深海机器人的技术与商业逻辑。面对不同深海应用场景，如何找到一套通用的底层技术？海洋装备市场长期被海外巨头占据，本土初创企业怎样建立全球竞争力？AI给水下机器人带来了哪些突破点与新机会？当深海智能化浪潮开启，创业者如何判断机会窗口、提前布局下一代产业基础设施？02:23 工作级水下机器人04:59 不同应用场景，90%模块相通09:42 从能源到海底数据，深海需求上涨13:54 深海机器人是如何运作的？16:00 打破客户偏见、错位竞争21:28 AI如何颠覆水下机器人作业？27:59 什么决定了水下机器人的价格？30:47 全球市场的布局规划34:19 海洋尖端装备创业契机41:14 “让问题暴露出来”44:23 评论区抽取5名听众送出深海智人精美周边《创业内幕》粉丝群已经开通，在这里，你可以跟节目制作人/主持人直接沟通，也可以第一时间了解到纪源资本线下活动动态，见到纪源资本的投资人，结交其他互联网圈子里的小伙伴。 入群方式：1）添加微信号“JiyuanFans”为好友，并在好友请求中标注“创业” 2）把你的全名和职称发给创业小助手；如果您想约访谈，请添加小助手微信，并附上访谈嘉宾简介，小助手将帮您对接。

[고나리브리핑] 당원명부가 함부로 쓰인다는 것, 상위3%가 강제야근한다는 것의 의미 - 유승균 PD, 박유진 PD, 정은정 농촌사회학자[지극히 정치적인 AI] 증거를 인멸한 AI, 구속수사도 못할텐데 - 송경호 ETRI인공지능안전연구소 선임연구원최소한의 의사소통〈XSFM 서포터즈 멤버십〉https://xsfm.benecent.org/ARS 1522-5637xsmall 바로가기: https://xsmall.co.kr/제보 메일: xsfm25@gmail.com광고 문의: xsfmmall@gmail.com※ 영상의 무단 사용을 금지합니다. 〈그것은 알기 싫다 NEWS BY DAYLIGHT〉 방송 화면 인용 시, XSFM 출처를 표기해주세요.#당원명부유출 #OpenAI#Anthropic #Bernie Sanders #박유진 #정은정 #송경호 #ETRI #그것은알기싫다NewsByDaylight #그것은알기싫다 #그알싫 #NBD #그알싫NBD #NewByDaylight #뉴스바이데이라이트 #뉴바데 #유승균 #XSFM

In this first episode of AI & the Church, Michael Whittle — VP of AI at Subsplash and founder of Pulpit AI — introduces the series and lays out the convictions behind it. He shares the story of building Pulpit AI, the surprising backlash from pastors and seminary professors when it launched in 2023, and how dramatically the conversation has shifted since then.This episode answers questions like:• Should the church use AI in ministry?• How are pastors actually using AI today?• Is AI a threat to the calling of the pastor?• What is the difference between what AI can do and what only a pastor can do?• How should church leaders think about AI tools for sermon prep, discipleship, and communication?

AI业内人士Henry田认为应该让AI的能力在可控、可观察、可撤销的边界内逐步去释放，才能改善网络安全。 点击音频收听详细采访

【AI數位轉型圈】 以管理為導向的數位轉型共學平台 邀請貴單位加入我們的行列， 掌握AI應用的核心策略，加速組織數位轉型。   【AI數位轉型圈】透過全年度精心策劃的實體培訓交流課程與線上學習講座，從單點突破，走向集體協作，為組織梳理AI治理框架，帶走第一手轉型實戰解方。 敬邀企業、醫界、學界攜手共學，掌握AI時代的企業競爭力！ 歡迎加入

AI 越聰明，耗電量越大。當全球數據中心加速擴張，澳洲可能成為這場 AI 革命的最大受惠者？

【大人學的職涯升級系統】14 個決策工具 × 6 週實作計畫，陪你一步步完成自己的職涯地圖→ 更多課程資訊：https://hi.sat.cool/76Knp課程 4 大亮點➊ 找方向｜下一步往哪走？盤點經驗、能力與優勢，看清自己的位置與更多發展可能➋ 做選擇｜留下、轉職還是跨領域？運用集點卡、開地圖等決策工具，把模糊的焦慮拆成具體選項，不再只憑感覺決定➌ 升能力｜下一階段該投資什麼？透過技能樹找到值得深耕的能力，把有限時間投入真正能累積市場價值的地方➍ 迎未來｜AI 時代怎麼保持競爭力？拆解 AI 焦慮背後的 7 種依賴，找對能力投資方向，用 AI 放大個人價值 即日起至 9/15 前，早鳥限時優惠低於 5 折！結帳輸入折扣碼「77career」 可以再折 350 元—------------------Hiho～大家好，我是志祺，這集來聊聊的來賓是⋯⋯ 大人學 Bryan！這集你會聽到⋯⋯→ 通常煩惱最多的上班族，是什麼樣的背景？→ 為什麼明明工作很努力，內心卻還是不踏實？→ 職場上，有沒有常見的「無效努力」？→ 我們在職場上要怎麼「合理地表現自己」？→ 怎麼判斷，現在的工作，自己有在慢慢成長，還是在原地打轉？→ 怎麼判斷轉職的時機？→ 怎麼看待AI帶來的擔憂？＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿＿

中美科技戰的打法正在翻轉。中國為打贏AI戰，開始捨棄長久以來的國家補貼模式，改向28兆美元的股債市場找錢。北京究竟為AI產業開了多少新綠燈？急奔資本市場的中國AI新創，真的能靠這波新模式彎道超車美國嗎？ 文：田孟心 製作團隊：莊志偉、張雅媛、鄭子鴻 ＊閱讀零時差，點這看全文

中美AI模型實力差距越來越小，除了硬體建設，中國更大力推進AI技術落地到生活各層面。從社群媒體上看到的大量AI生成微短劇，到自動駕駛車和人形機器人，從消費市場到藍領領域全面擴散。 此舉卻有反效果，儘管中國預計在2050年前勞動人口將減少25%，但AI自動化導致的職位消失速度，恐怕遠超人口縮減。《經濟學人》點出，隨著青年失業率已達15%、超過3億人從事零工經濟，AI急速替代人力將引發勞動需求劇降，迫使政府在促進創新與維持社會穩定間，陷入兩難。 【聽完這集你會知道】 25:25｜蘇丹內戰引發地緣災難與人道危機 蘇丹內戰進入第四年，首都喀土穆淪為廢墟，讓1400萬人流離失所。隨戰火波及紅海航運，中東與軍閥勢力面臨更高代價，此刻正迎來停火談判的最後窗口。 28:15｜AI晶片熱潮翻轉韓國經濟與社會 半導體巨頭獲利暴增使工程師躍升新富階級，但股市波動劇烈，讓許多散戶損失慘重。韓國政府試圖靠成立未來應對基金以及改進稅制，嘗試在保護產業競爭力的同時，也能造福全民。 39:45｜選民厭惡兩黨鬥爭，第三勢力崛起？ 美國長期的兩黨政治正在迎來崩解，民眾對兩黨不滿達到新高，無黨籍候選人在蒙大拿與內布拉斯加州強勢崛起。如果民主黨願意退選避免瓜分選票，中間派與民粹獨立陣營有望改寫國會版圖。 01:04:50｜年輕股神滿街跑背後警示 美國一位25歲的年輕人，成立了一家名為「Situational Awareness」的AI 避險基金，在沒有經驗的狀況下，憑著高槓桿，2026上半年回報率高達439%，卻在七月暴跌 67% 並被迫折價清算。這個故事背後，警示著神童神話、少年股神等敘事被投資人盲目追捧的危險性。 主持人：天下雜誌副總編輯 黃亦筠 主講人：金庫資本管理合夥人兼總經理 丁學文 製作團隊：錢玉紘、莊志偉、邱宇豪 ＊延伸閱讀｜中國AI股掀吸金狂潮：https://www.cw.com.tw/article/5142271 ＊天下學習一週年，填問卷抽好書：https://forms.gle/oHEGabfvhyifhqdbA ＊訂閱天下全閱讀：https://bit.ly/3STpEpV ＊意見信箱：bill@cw.com.tw -- Hosting provided by SoundOn

Everyone has read about how much productivity AI can have for federal applications. That allure can lead you into a situation where you may not be as prudent as you should be. Today, we sat down with Jamie Holcomb, COO of Electrosoft, and Steve Riley, VP and Field CTO of Netskope, to better understand how to balance the promise of AIAI with practical application in the federal government. Steve Riley has looked at federal technology projects, and he thinks that the government must get out of their pilots and into operations, measuring results and impact that is meaningful to the taxpayer. They highlight the rapid advancements in AI, noting that government agencies struggle to keep pace with commercial innovation. Riley thinks agents are great for gathering, assimilating, and assessing information.  It is probably not time for them to have independent agency. A survey reveals that 78% of federal leaders believe AI tools should be managed like users or devices within zero trust frameworks. Only 31% of federal IT leaders have high visibility into public AI tools. Because of Jamie Holcomb's experience at the United States Patent and Trademark Office, he thinks success comes down to action, not architecture.  From his view, agencies need timely intelligence connected directly to operational decisions. The conversation emphasizes the need for federal agencies to balance innovation with security, particularly in predictive AI and zero trust strategies, and to prioritize AI discovery and integration.  

歡迎收聽股癌，我是謝孟恭戒菸就如同投資，需要放棄短線刺激與快感，以追求長期的健康複利。身為過來人，我蠻鼓勵大家可以嘗試戒菸輔助品。尼古清戒菸口含錠即將上市，使用簡單零門檻，戒菸新手也容易接受，讓你不被菸癮打擾，專注生活每一刻。嘉安家護股份有限公司 台北市中山區民生東路三段2號10樓尼古清戒菸口含錠2毫克衛部藥輸第029054號北市衛藥廣字第 115070153號 ——以上廣告由 Firstory 與【月城南廣告】共同執行—— 日本自衛隊計畫要靠國產AI提升戰力，但問題來了，AI有了但無人機卻不夠多。目前日本積極發展無人戰力，卻卡在量產和供應鏈兩大難題，要如何補上這個缺口成了防衛轉型的一大考驗。加入會員，支持節目： https://globalhashtagnews.firstory.io/join留言告訴我你對這一集的想法： https://open.firstory.me/user/cku2d315gwbbo0947nezjmg86/commentsYT收看《寰宇全視界》

【今回の紹介記事】仕事のデキなさがすぐにバレる…｢AIで劇的な効率化に成功する人｣と｢AIを使うほど損する人｣の根本的違いhttps://president.jp/articles/-/116274?ref=otb_podcast ▼番組へのご意見・リクエストはこちらからメール：podcast@president.co.jpフォーム：https://forms.gle/DNYwbzLVrPX84M4d8See omnystudio.com/listener for privacy information.

AI 如何改寫體育歷史？從幾毫米越位判決，到球員受傷前一刻預測，AI 正無聲無息，改寫運動場上的遊戲規則。今集《潮玩科技》探討前所未有的運動界大變局。

《AI分身｜打造替你工作的高效自動化團隊》

博幼基金會相信教育能打破貧窮世襲，帶著教育與陪伴到偏鄉，透過免費課後輔導與生活支持服務，長期陪伴偏遠地區孩子穩定學習。 「期待有一天，教育能終止貧窮世襲，弭平城鄉及貧富差距，讓每個孩子都有選擇未來的能力。」 捐款連結▶️ https://fstry.pse.is/9f679b —— 以上為 FMTaiwan 與 Firstory Podcast 廣告 —— 亞洲股市6日，受美股半導體重挫拖累，韓股重挫逾 4%、日股盤中也大跌逾千點，反映市場對，科技巨頭龐大AI資本支出，的獲利能力產生疑慮。而在科技股遭拋售之際，資金開始轉向非AI曝險標的，前兩年相對冷清的，印度股市因而成為熱門避險選擇，外資於7月重回淨買入，Google、微軟等科技巨頭，也計劃在印度投資數百億美元，建設科技基礎設施。加入會員，支持節目： https://globalhashtagnews.firstory.io/join留言告訴我你對這一集的想法： https://open.firstory.me/user/cku2d315gwbbo0947nezjmg86/commentsYT收看《寰宇全視界》

很多好學生進了職場才發現，人生忽然沒有標準答案了， 大人學共同創辦人Bryan，長年陪伴上班族做職涯選擇， 他發現，職場痛苦很大一部分來自「被動」， 總是跟著走，卻忘了問自己到底想過什麼樣的人生， 職場真正的升級，是從NPC變成玩家，拿回人生主動權．．． / 【大人學的職涯升級系統 】 建立一套判斷框架，做出更好的職涯選擇

Чи забере AI нашу роботу? Питання банальне. Як і стандартна відповідь, яка нервує ще більше: AI не забере вашу роботу, її заберуть люди, які користуються AI. Насправді світ складніший, ніж заголовки YouTube-відео, і правильних відповідей станом на зараз, скоріш за все, немає. Але є правильніше питання: як використовувати AI, щоб робити свою поточну, наступну або майбутню роботу краще? У цьому епізоді розбираю ідеї з курсу MasterClass "AI Strategy at Work": аудит своєї ролі через AI, чому смак стає головним скілом, готовність команди для менеджменту і бонусом метод Андрея Карпати.У цьому епізоді:- Generation T і три письмові питання для старту- Сигнали: патентні бази, arXiv і Google Scholar замість стрічки новин- Аудит своєї роботи: три категорії задач і ~45% часу- Нескінченна фокус-група з 15 персон перед важливим рішенням- Смак, письмо як мислення і проблема AI-слопу- Для менеджменту: чотири кроки готовності команди і AI paradox з HBR- Бонус: метод Андрея Карпати для кращих відповідей від AI

当越来越多商业空间面临空置、同质化和流量焦虑，一个空间还能创造什么新的价值？本期「贝望录」，我们来到上海前滩的彼屯，与两位联合创始人王晨歌、李逸飞聊了聊这个正在发生的商业实验：如何通过一个物理空间，连接内容、教育、社区和人与人的关系。彼屯诞生于彼山咨询对于未来商业形态的思考，创始团队希望探索一种“between”的状态—连接线上与线下、内容与体验、个人与社区。在这里，有播客录制空间、咖啡和食品零售、艺术展览、工作坊、课程以及各种社区活动，但它真正想打造的，并不是一个简单的复合型商业空间，而是一套围绕“共创”的社区生态。节目中，两位创始人分享了彼屯从文化空间探索，到AI时代教育产品设计的过程。他们如何筛选老师、设计课程？为什么课程不是传统知识传授，而强调30%的理论和70%的实践？为什么在AI让获取知识越来越容易的时代，人与人的线下互动反而变得更加重要？同时，我们也聊到了商业模式的问题：空间、课程、活动和社群如何形成收入结构？文化和商业如何平衡？一个社区型商业项目如何实现规模化复制？从一个400多平方米的空间出发，彼屯希望探索一种未来城市社区的新可能：让空间不只是承载消费，而成为人与内容、人与机会重新连接的地方。【本节目由Withinlink碚曦投资协作体出品】【嘉宾】王晨歌彼山创始人、彼屯between创始人李逸飞彼屯between合伙人【主持】李倩玲 Bessie Lee广告营销行业资深从业者，商业观察者【本期内容提要】00:12为什么想聊彼屯这个有意思的商业空间01:35从彼山到彼屯,“between”代表连接线上线下、内容与商业之间的新状态03:59逸飞的教育理念与彼山团队在地社区想法的融合促成了现在的彼屯05:50为什么选择播客作为连接用户的入口？08:24为什么选择前滩，而不是传统上海中心区域？12:33 一个空间里有哪些可能？让我们一起声音漫游彼屯17:04 彼屯如何通过课程、展览、活动不断实验，并寻找可持续商业模式？20:38 彼屯课程体系为什么不是老师带课，而是主动寻找内容创造者一起共创？25:44 从丰富课程到聚焦AI，彼屯为何把AI时代技能教育作为自己的主要方向？28:58 一周有15-20场活动的彼屯是如何运营内容生态的？33:07 为什么AI时代线下学习仍然重要？40:19 播客空间如何成为商业模式和内容生态的一部分？41:52 收入结构与商业化探索的关系，咖啡、课程、空间哪个更重要？45:28 内容IP如何复制？从单点空间到更多城以及未来规模化运营的想象52:21 AI时代教育的新方向也许是从标准答案到自主探索01:02:36 创业者最大的挑战是要从概念到真实发生的故事01:07:02 五年后的彼屯是什么样？【后期制作】Jean【收听方式】推荐您使用Apple Podcast、小宇宙APP、喜马拉雅FM、荔枝播客、网易云音乐、QQ音乐、Spotify或任意泛用型播客客户端订阅收听《贝望录》。【互动方式】微博：@贝望录微信公众号：贝望录+商务合作：beiwanglu@withinlink.com

【2026HBR管理年會】  尋找新經典．未來領導力 HBR 繁中版 20 週年鉅獻，全天論壇規格全面升級！  邀請您親臨現場，掌握變局時代的關鍵領導力。 

博幼基金會相信教育能打破貧窮世襲，帶著教育與陪伴到偏鄉，透過免費課後輔導與生活支持服務，長期陪伴偏遠地區孩子穩定學習。 「期待有一天，教育能終止貧窮世襲，弭平城鄉及貧富差距，讓每個孩子都有選擇未來的能力。」 捐款連結▶️ https://fstry.pse.is/9f679m —— 以上為 FMTaiwan 與 Firstory Podcast 廣告 —— 吉時保： https://fstry.pse.is/9ep3le 免指定車牌、車型，用車前1小時投保，手機投保5分鐘新安東京海上產險｜0800-369-168｜104台北市中山區南京東路三段130號8-13樓 —— 以上為 Firstory Podcast 廣告 —— **英特爾先進封裝布局傳捷報。外媒報導，英特爾的EMIB-T先進封裝技術良率已突破九成，預計明年開始量產，成本只要台積電（2330）CoWoS的一半，轟動業界，並成功拿下聯發科訂單，博通、Meta等大咖也正評估採用。 **台股7月回檔逾3006點，月跌6.5%，創史上月跌點第2大，主因受到韓股熔斷、美伊衝突再起與Fed升息預期等，大盤加速趕底，第2隻腳成形，法人看好AI基本面依然穩固，實體AI應用商機擴大，8月推5大選股策略，包括AI算力、先進封裝、光通訊、實體AI商機和旺季暨殖利率股等，點名台積電、中砂、全新、研華、寶雅等20檔買進。 **美日罕見聯手干預匯市之際，美國財政部被揭露並非採取傳統的「賣美元、買日圓」方式，而是透過出售歐元換進日圓，協助日本推升疲弱日圓。分析師認為，此舉可避免釋放美元走弱訊號，同時市場也開始關注南韓、歐洲央行是否加入協調行動，若主要經濟體進一步聯手支撐日圓，恐演變成更廣泛的「貨幣協議」。 **中東戰雲密佈之際，美國總統川普（Donald Trump）對伊朗強硬態度出現鬆動，在喊停「二戰以來最猛烈攻擊」之後宣布將與伊朗舉行談判。不過，伊朗公開否認與華盛頓當局進行任何談判。川普今天在白宮表示，如果伊朗不同意達成結束兩國衝突的協議，就會面臨「斬首」，而伊朗德黑蘭當局還有最後一次機會。#寶島全世界 #寶島聯播網 #矢板明夫 #吳嘉隆 #台積電 #AI #英特爾 #intel #CoWos #日圓 #升息 #匯市 #川普 #期中選舉加入會員，支持節目： https://clw4248xv113d01wg7s4h2xnq.firstory.io/join留言告訴我你對這一集的想法： https://open.firstory.me/user/clw4248xv113d01wg7s4h2xnq/comments Powered by Firstory Hosting

本集《管理進化論》由資深主編邵蓓宣，專訪愛比科技（IPEVO）總經理李信宜，談AI 即時語音辨識技術的第一線導入情境。愛比科技原本以銷售實物攝影機、教學用投影器材為主，如今進攻新的業務領域： AI 語意識別，並進軍商用領域。從艱澀的半導體供應鏈行話，到佛光山的佛經講道，他們如何靠著高達8成以上的專業準確率，讓機密會議與跨國展會的溝通成本大幅降低？1. AI語音辨識也能翻譯佛經？ 通用型 AI 翻譯常在產業行話上栽跟頭。愛比科技替佛光山耗時半年梳理語料，將讓許多艱澀的佛經在講道的場合也能做即時翻譯，並提供精準的語意翻譯。同樣的「客製化微調」技術也應用於半導體大廠，辨識上萬家供應商的艱澀術語，將準確率從一般模型的 30% 飆升至 80% 以上。2. 克服「中文最難翻」魔王關，破解同音異字與跨國口音。中文即時翻譯的挑戰在於充滿同音異字與雙關語，被公認是 AI 語音辨識的最大魔王。愛比科技捨棄傳統的逐字翻譯，改採「語意翻譯」，系統會先消化上下文再產出結果，自動校正同音錯誤。此外，針對商務人士頭痛的印度腔、日本腔、中式英文等口音痛點，系統內建的多元模型也能精準辨識，掃除跨國溝通盲區。3. 聲紋辨識與客製化摘要。一場會議逐字稿，系統除了聽得懂、生成出來的文字準確度高，更要好整理。愛比科技透過軟硬體整合，能分離多達 10 人的發言聲紋。系統還能直接匯入企業慣用的會議紀錄格式來「自動排版」，甚至一鍵生成視覺化心智圖。相較單日動輒數十萬的多語真人翻譯費，成功替企業打造出極致高效的會議流程。 Powered by Firstory Hosting

Когда вообще стоит проводить UX-исследование? А когда оно только тратит время и деньги? В этом выпуске разбираемся, в каких случаях ответ уже можно найти в аналитике или саппорте, когда проблема и так очевидна, а когда ещё слишком рано что-либо исследовать, потому что даже гипотезы решения нет. В гостях – Георгий Крутоус, руководитель UX Research в Muse Group (Ultimate Guitar, MuseScore, MuseHub и Audacity). Конечно, не обошлось без разговора про AI: обсудили AI-персон, AI-модерируемые интервью, AI-репозиторий знаний, который строят в Muse Group, и то, могут ли языковые модели разбирать и агрегировать открытые ответы в опросах.     Avito.Tech.Conf — конференция от AvitoTech для тех, кто управляет сложным. Успейте поймать билет на офлайн: https://clc.to/YvNrNw Реклама. ООО "Авито Тех”, ИНН 9710089440, erid:2SDnjdbfsR5     Также ждем вас, ваши лайки, репосты и комменты в мессенджерах и соцсетях!
 Telegram-чат: https://t.me/podlodka Telegram-канал: https://t.me/podlodkanews Twitter-аккаунт: https://twitter.com/PodcastPodlodka Ведущие в выпуске: Стас Цыганов, Катя Петрова Полезные ссылки: Для консультаций Telegram https://t.me/LimeGK LinkedIn https://www.linkedin.com/in/georgiy-krutous-689217194/

週一天下零時差關注以下財經大事： 一、台股大跌，AI基本面真的轉壞了嗎？ 二、超微首款AI機櫃開始量產，透露什麼市場趨勢？ 三、美伊衝突再起，國際油價怎麼走？ 文：郭家宏、辜樹仁 製作團隊：錢玉紘、鄭子鴻 ＊閱讀零時差，點這看全文

AI 能否真正縮短蛋白質藥物的發現流程？ 本集為《AI 藥物開發系列》的第四集，我們邀請 DeepSeq AI 創辦人 Andrew 張淳傑博士，從生物物理、藥廠研究、新創公司經驗，一路談到如何將自然語言處理的方法應用於蛋白質工程。 Andrew 在密西根大學攻讀生物物理博士，之後進入 Genentech 擔任業界博士後，原本負責以影像技術研究藥物在體內的分布。為了取得能夠辨識特定生物標記的蛋白質，他開始接觸噬菌體展示技術，也注意到一個問題：實驗可以篩選數十億種蛋白質序列，但傳統分析往往只使用最後少數被挑出的候選分子，大量篩選過程中的資訊並未被充分利用。 他因此嘗試將噬菌體展示與次世代定序結合，觀察不同蛋白質序列在每一輪篩選中的變化，再借用自然語言處理的概念，分析胺基酸序列中的規律。這個想法的核心，是把蛋白質視為一種具有「文法」的序列：胺基酸如同字母，排列方式與前後關係可能影響蛋白質的穩定性、結合能力與功能。相較於先預測三維結構、再推論功能的傳統路徑，Andrew 探索的是能否直接從大量實驗產生的序列與功能資料，建立兩者之間的預測模型。 到底 Andrew 是怎麼從技術構想到成立公司呢？他如何在多家新創公司累積產品開發、客戶溝通、團隊管理與系統建置的經驗，最後全職投入自己的公司 DeepSeq AI？ 本集討論： ＊噬菌體展示如何從大量候選序列中篩選蛋白質 ＊早期自然語言處理、詞袋模型與現代蛋白質語言模型的差異 ＊AI 蛋白質公司的商業模式、開源與閉源模型，以及技術護城河可能來自哪裡 ＊大型藥廠、不同階段新創公司與創業加速器，分別能提供哪些經驗   ✨ 生技來一刻感謝國科會與駐波士頓辦事處科技組贊助我們製作節目。我們也歡迎聽眾小額捐款生技來一刻，您的支持能幫助我們製作更優質的節目。 ✨ 節目連結、講者Linkedin連結請見留言處！感謝講者提供詳盡的延伸閱讀連結和文章！  

AI is everywhere, but are property management companies asking the right questions before implementing it? In this episode of the #DoorGrowShow, Jason Hull sits down with Mo Hussain to discuss why successful AI adoption has far less to do with technology and far more to do with operational clarity. Instead of chasing the latest AI tools, Mo introduces his Measure, Map, Automate framework to identify operational bottlenecks, uncover hidden profit leaks, and build workflows that actually improve business performance.  Together, they explore why clean data is the foundation of automation, how undocumented processes create costly inefficiencies, and why AI should enhance human decision-making rather than replace it.   You'll Learn [00:00] Meet Mo Hussain and the Measure, Map, Automate Framework [03:20] Why Most Companies Ask the Wrong AI Questions [08:10] The Role of Clean Data in AI Success [12:45] Mapping Workflows Before Automating Them [15:30] The Process Myth and Better Operational Systems [21:10] Building Accountability Into AI Workflows [25:15] Designing AI Agents That Actually Perform [27:45] Turning Operational Data Into Business Growth [29:15] Final Advice for Property Management Leaders Quotables "AI value really truly is workflow value." Mo Hussain  "AI depends on trusted operational data." Mo Hussain  "The winners are not gonna be the companies that have the most amount of data, but they're the ones that can convert data into consistent operating actions." Mo Hussain  Resources DoorGrow and Scale Mastermind DoorGrow Academy DoorGrow on YouTube DoorGrowClub DoorGrowLive Transcript Jason Hull (00:00) welcome everybody. I'm Jason Hull, the founder and CEO of DoorGrow, the world's leading and most comprehensive coaching and consulting firm for long-term residential property management entrepreneurs. For over a decade and a half, we have brought innovative strategies and optimization to the property management industry.   At DoorGro, we are on a mission to transform property management business owners and their businesses. We want to transform the industry, eliminate the BS, build awareness, change perception, expand the market, and help the best property management entrepreneurs win. Now let's get into the show. And my guest today is Mo Hussain, and we're going to be talking about how property management companies can stop drowning in data and start turning it into real operational growth. In this episode,   Mo is breaking down the measure, map, and automate framework that he has built and approach an approach to uncovering hidden margins, reducing manual oversight, and getting more value out of every door in your portfolio.   right.   is Mo Hussain. Mo, welcome to the show.   Mo Hussein (01:01) Hey Jason, happy to be here.   Jason Hull (01:03) So today we're going to be chatting a little bit about how property management companies can stop drowning in data and start turning it into real operational growth. And Mo's going to break down the measure, map, and automate framework, his approach for uncovering hidden margins, reducing manual oversight, and getting more value out of every door in your portfolio. So cool, measuring is important. We'll get into that. So before we get into that, Mo,   Can you give people a little bit of background on yourself? How did you get into entrepreneurism? How did you get connected to property management? And help everybody understand who Mo is. Yeah.   Mo Hussein (01:42) Yeah.   great question. So I I've been in this industry now for probably coming up on 20 years at this point. I I worked at some of the prop tech and software providers that are prevalent in the space. Namely, I worked at both YARTI App Folio, which are both kind of headquartered in in Santa Barbara. and a little bit over ten years ago, I started a consultancy and accounting CPA practice that specifically focuses on   Jason Hull (01:56) Namely, I worked at both YARDIE and at Folio, which are both kind of headquartered in in Santa Barbara. a little bit over ten years ago, I started a consultancy and accounting TPA practice that specifically focuses   on prop tech and real estate. So we offer consultations with implementations, custom reporting, operationalizing around technology, which is which is now the buzz around kind of AI and automation at this point.   Mo Hussein (02:11) Prop tech and real estate. So we offer consultations with implementations, custom reporting, operationalizing around technology, which is which is now the buzz around kind of AI and automation at this point. and then   we've also built products for the space to help with automations, help with you know accounting compliance and bringing visibility and custom reporting capabilities to operators. So kind of leveraging all the experience.   Jason Hull (02:25) And then we've also built products for the space to help with automations, help with you know, accounting compliance and bringing visibility and custom reporting capabilities to operators. So kind of leveraging all the experience   Mo Hussein (02:40) from working as a consultant and also as an accountant and even working as some of these tech providers now being a actual supplier in the industry.   Jason Hull (02:41) from working as a consultant and also as an accountant and even working as some of these tech providers now being a aqua supplier in the industry. Very cool. Very cool. So you're a little bit nerdy.   Mo Hussein (02:52) A little bit. Data. I love data. Right.   Jason Hull (02:53) Okay, so am I. So am I. All right. So   cool. So let's talk nerdy to me, Mo. All right. So let's let's chat about this. So let's get into it. So t tell us about this. Wha why is this wh how'd you come up with this framework? Why is this important? I love frameworks because frameworks are usually where we take something that we notice a pattern in, there's some complexity involved, and we make it simple. So explain to us.   Mo Hussein (02:59) Yeah.   Jason Hull (03:18) Where does the measure map and automate framework kind of come from?   Mo Hussein (03:22) Right, right. And this is this kind of stems from a conversation you probably have with plenty of your your clients and even prospects when you start engaging, you know, the the the very popular question now of how do we use AI? I want to streamline and automate. And it's a very loaded, it's a very loaded, fairly ambiguous question, right? How do we use AI? We want to implement AI into our operations, right?   Jason Hull (03:23) And this is this kind of stems from a conversation you probably have with plenty of your   You know, the the the the very popular question now, how do we use AI? It's a very loaded, fairly ambiguous question, right? How do we use AI? We want to implement AI more.   Mo Hussein (03:48) when conversely, like you know, operators and property managers should be starting with a different qu set of questions, right? Like how like where are we losing things like NOI, margin, time, control, or even consistency, right? AI really only matters when it connects and automation really only matters when it connects to a to a revenue lever or some type of a cost lever or productivity gain or or risk reduction, right?   Jason Hull (03:50) Conversely, like you know, operators, property managers should be starting with a different set of questions, right? Like how like where are we losing things like NOI, margin, time, control, or even consistency, right? AI really only matters when it connects in automation really only matters when it connects to a to a revenue lever or some type of a cost lever, productivity gain or or risk reduction,   right? Yeah. there's there's a couple   Mo Hussein (04:14) and there's there's a couple of key components   Jason Hull (04:16) key components in even conversations that you've probably even had with with property managers today is that firstly like you know operators today they already have a lot of data. They probably have access to a lot of different data sets across, you know, operations, but it's probably, you know, disconnected and disjointed and different reports and disconnected systems, hidden in spreadsheets and and dashboards that probably don't drive much much action, right? and everybody wants   Mo Hussein (04:17) in even conversations that you've probably even had with with property managers today is that firstly, like, you know, operators today, they already have a lot of data. They probably have access to a lot of different data sets across, you know, operations, but it's probably, you know, disconnected and disjointed and different reports and disconnected systems hidden in spreadsheets and and dashboards that probably don't drive much much action, right? and everybody wants to   Jason Hull (04:43) to automate and execute   Mo Hussein (04:43) automate and execute an   operational kind of workflow. But the hard part is not whether, you know, AI can really do something, but the hard part is whether a company even knows where value is leaking and who owns that action and and whether these workflows are even clear enough to to be able to automate. And that's kind of the premise of this framework is to kind of measure what that pain is, you know, map that workflow, automate that repetitive work and manage   Jason Hull (04:45) an operational kind of workflow. The hard part is not whether you know AI can really do something, but the hard part is whether a a company even knows where value is leaking and who owns that action and and whether these workflows are even clear enough to to be able to automate. And that's kind of the premise of this framework is to kind of measure what that pain is, you know, map that workflow, automate that repetitive work, and   manage ideally performance through some type of closed loop accountability. We just put an actual word to it, right? A framework to it, I'm sure   Mo Hussein (05:07) ideally performance through some type of a closed loop accountability. We just put an actual word to it and a framework to it, but I'm sure very   similarly to the conversations that you're probably having also even with customers.   Jason Hull (05:15) Very similarly to the conversations that you're probably having also with customers.   Yeah, yeah, got it. Yeah. it's interesting because we're now seeing a lot of these tech companies or tech forward companies that are kind of backtracking on AI a little bit. They were giving out basically blank checks to use AI as much as they could. Some were even creating sort of a contest internally, incentivizing like who could use the most tokens.   Mo Hussein (05:29) Mm.   Right.   You're right.   Jason Hull (05:41) Which is a little bit insane   to just give people a blank check as if that always the more tokens you burn, the more productivity is being created, right?   Mo Hussein (05:51) Right, right, right. And we're seeing, yeah, and you know, as we're seeing these newer models that are coming out, whether it's, you know, through Cloud, Anthropic or even these other these other LLMs, the token utilization is becoming more and more expensive, especially with these newer models. And so now the question of just like, hey, how is that utilization actually translating to actual business value? Right. And this was a question that eventually would have been would have been pushed, right?   Jason Hull (05:54) Yeah and you know.   Of just like, hey, how's that utilization actually translating to actual business value? Right.   Yeah. Yeah. Yeah. I love it. Like how to use AI. Yeah. Bad question. A better question is how do we actually make sure we're creating more profit? How do we actually make sure we are lowering costs? Like   And that's the the idea, they think, well, AI must be so much cheaper than people. And what's interesting, I've also seen some reports lately showing the amount of money these different LLMs are losing right now. They're spending a massive amount of money to deliver AI to us at a super cheap price right now. And but they're losing money. Every time we're chatting, they're losing money.   Mo Hussein (06:47) Mm-hmm.   Right.   Right.   Jason Hull (07:01) And that's that's a wild business model. They're obviously hoping to win some sort of AI race. They're hoping to get us maybe in the future. And there's a lot of talk lately as well of people thinking we gotta shift to local models. Like we gotta I gotta run this AI stuff on my own computer and not be giving all my money to anthropic or open AI you know, open AI or whatever. So okay.   Mo Hussein (07:15) Mm-hmm.   Right, right.   Right.   Jason Hull (07:26) Cool. So let's continue on. Me measure, map and automate. Yeah. Yeah.   Mo Hussein (07:29) Yeah. Yeah. And   by the way, going on your point, Jason, it's you know, you you also, you know, creating automation and leveraging these models locally, it there's definitely value in that. But you know, now more than ever, f you know, teams are kind of distributed, right? And so ideally, if you've built automations and leveraging these L LMs and   Jason Hull (07:35) Yeah, y you also you know   Locally it is definitely that   Teams are kind of distributed, right? Yeah. Ideally, if you've built automations and leveraging these LLMs   and   Mo Hussein (07:52) And things of that sort.   You probably want to have like some type of an interface that's like cloud based, right? Or for folks to be able to kind of collaborate in some type of a ideally like a safe environment, right? and so measure, map and and automate. So you know, there's there's kind of those three components to be able to actually fully ideally leverage leverage AI. But   Jason Hull (07:55) some type of a an interface that's like cloud based, right? Or for folks to be able to kind of collaborate in some type of a ideally like a safe environment, right? yeah. So measure, map and and automate. So you know there's there's kind of those three components to be able to actually fully ideally leverage leverage AI but   there's there's a couple like kind of key core components that feel like   Mo Hussein (08:20) There's there's a couple of like kind of key core components that I feel like   is very important for folks to to really understand before they can they they can even take advantage of of AI, right? so one is you know AI, AI value really truly is workflow value. And so like the most the biggest opportunities when it comes to automation leveraging AI is things that are repetitive.   Jason Hull (08:25) is very important for folks to to really understand before they can they they can take advantage of of AI, right? so one is, you know, a AI AI value really truly is a workflow value. And so like the most   automation leveraging AI as things that are   repetitive, you know, judgment heavy, ideally high volume workflows. Think about things like you know, leasing follow-up, delinquency, turns, maintenance, triage, variance explanations. another another key thing to understand is you know AI depends on trusted ideal operational data. And so if you don't have accurate or clean property unit, resident, vendor,   Mo Hussein (08:44) you know, judgment heavy, ideally high volume workflows. Think about things like you know, leasing follow-up, d delinquency, terms, maintenance triage, variance explanations. another another key thing to understand is, you know, AI depends on trusted ideally operational data. And so if you don't have accurate or clean property unit, resident vendor   payment data and it's and it's inconsistent, you know, AI just   Jason Hull (09:10) payment data and it's and it's inconsistent, you know,   AI just helps accelerate the wrong answer, right? This notion of like hallucinations also kind of exist. and you know insights without ownership is is just is really just theater. And so although AI may identify a problem and recommend an action, things need to be routed, right? And asci you know action needs to be assigned, there needs to be some accountability that gets created there and then a measurement of of of   Mo Hussein (09:13) helps accelerate r the wrong answer, right? And the this notion of like hallucinations also kind of exist. and you know insights without ownership is is just is really just theater. And so although AI may identify a problem and recommend an action, things need to be routed, right? And as I you know action needs to be assigned. There needs to be some accountability that gets created there and then a measurement of of of a   of of a of a result.   Jason Hull (09:40) of of a of a   result. and then lastly like humans humans control still matters, right? Things that have a very high potential opportunity cost. you know, operators should be very careful on how they utilize AI. So, you know, things around fair housing, sensitive sensitive decisions like screenings, evictions, legal communication, you know, employee decisions and maybe even large payment loopholes and so   Mo Hussein (09:42) and then lastly like humans, humans control still matters, right? Things that have a very high potential opportunity cost. you know, operators should be very careful on how they utilize AI. So, you know, things around fair housing, sensitive sensitive decisions like screenings, evictions, legal communication, you know, employee decisions and maybe even large payment approvals. And so   Once we   have these kind of these table stake table stake items, if you will, kind of address, then you know we can move on to kind of you know the our framework of kind of measure, map, and automate. And so in each of these different components have different purposes, you know. The whole point of the measure step is is to quantify where pain exists and to validate kind of being buying versus buy like building. And so you want to ask things like where   Jason Hull (10:09) Once we have these kind of these table stakes stakeheads, if you will, kind of addressed, then you know, we can move on to kind of, you know, the our framework of kind of measure, map, and automate. And so and each of these different components have different purposes, you know. The whole point of the measure step is is to quantify where pain exists and to validate kind of being buying versus buy like building. And so you want to ask things   like where   Mo Hussein (10:36) where   time, where margin, where service quality or accountability is lost today, right? Examples can be things like, you know, days vacant, you know, delinquency rate, maintenance response times. These would be kind of like outputs like invoice coding time, reporting hours, renewal conversions, right?   Jason Hull (10:37) Where time, where margin, where service quality or accountability is lost today, right? Examples can be things like, you know, days vacant, you know, delinquency rate, maintenance response times. These would be kind of like outputs like invoice coding time, reporting hours, renewal conversions, right?   Yeah. Got it. Yeah, that that makes a lot of sense. So you've got to be you have to have good data.   Which the crux of th where their data is all probably housed is inside of their property management software.   Mo Hussein (11:06) Right.   Jason Hull (11:07) And so hopefully that software is kinda tracking some of this stuff. But, you know, everybody's had a CRM that the team didn't put enough notes in. And then it becomes kind of useless, right? So you're like, what happened with Fred on that call earlier, you know, or previously? I I think I think we talked about this. Can't remember. Why aren't you putting in notes? And so then the flaw becomes the human in the loop in a lot of instances. But then you're saying, you know, also humans matter. Like   Mo Hussein (11:14) Right.   Right.   Jason Hull (11:34) Related to fair housing. We've got to have the human in the loop making decisions. I don't think it would go fair very well to be standing in front of a judge and say, Well, the AI messed this up. It wasn't me.   Mo Hussein (11:43) Right. Right.   Right. Yeah, that's very that's very correct. the the the other thing is is that you know software is a tool, right? So they you know, for like that example that you just gave of like, hey, you know, I had a conversation with Freddie or an owner or what have you, and you know, the notes weren't captured. And so if there's if if if if there's there needs to be also a cultural   Jason Hull (11:46) Yeah, that's very that's very   Yeah, they you know, put like that example that you just gave of like, Hey, you know, I had a conversation   And you know, the notes weren't captured. And so if there's if if if if there's there needs to be also   Mo Hussein (12:06) shift within the organization to become more performance kind of driven, right? And using, you know, places of truth. You know, I, you know, we use Salesforce in our own internal kind of CRM and you know, there's this old ad like this old saying of just, you know, hey, if it didn't happen to Salesforce, it didn't happen at all. In other words, if your system of record hasn't been updated and things haven't been added to it   Jason Hull (12:06) cultural shift within the organization to become more performance kind of driven, right? And using, you know, places of truth. You know, I you know, we use Salesforce in our own internal kind of CRM and you know, there's this this old like this old thing of just, you know, hey, if it didn't happen in Salesforce, it didn't happen at all. Right.   Mo Hussein (12:29) to to ensure that it is correct and accurate and up to date, then   Jason Hull (12:29) to it to to ensure that it is correct and accurate and up   to date, then the organization sees it as, you know, as it didn't happen. And somebody, you know, using anecdotal feedback like, well I did this, but I just didn't update this. And so that's it's very important that, you know, whatever system you're using to kind of measure different KPIs and metrics, that that, you know, that behaviors within the organization are shifting towards that. And it's it's something it's a cultural shift that needs to also   Mo Hussein (12:32) the organization sees it as you know as it didn't happen. And somebody, you know, using anecdotal feedback of like, well I did this, but I just didn't update this, it means it didn't happen. And so that's it's very important that, you know, whatever system you're using to kind of measure different KPIs and metrics, that that, you know, that behaviors within the organization are shifting towards that. And it's it's some it's a cultural shift that needs to also cascade   also from from leadership down as well.   Jason Hull (12:57) Cascade also from leadership down.   Yeah, the advantage we have nowadays with all the AI stuff that's come out is now pretty much everything gets transcribed everywhere. So calls get transcribed, notes can be created automatically. You can also go back and ha check the transcription on a call or a zoom call or recording, figure out what happened. So that you know, not leaving notes in the CRM is a little bit less of a problem than it was in the past.   So we've so we've chatted a bit about measure. What is what's important about mapping or map? Yeah. So this is this goes back to my previous point about like you know AI value being it it is workflow value. Yeah so you know you've measured you've identified you know your measurements and KPIs. So whatever those KPIs may be. Next what you need to do is map what the actual   Mo Hussein (13:30) The mapping. Yeah. So this is this goes back to my previous point about like, you know, AI value being it is workflow value. And so, you know, you've measured, you've identified, you know, your measurements and KPIs, you know, days vacant, delinquency, whatever those KPIs may be. Next, what you need to do is map what the actual what   the actual workflows that are happening, not how leadership or staff thinks it's happening.   Jason Hull (13:53) what the actual workflows are happening, not how leadership or staff thinks it's   happening. There's a very key kind of a distinction is that, you know, a lot of operators and teams kind of assume, hey, you know, we have a set process, but it may not be happening the way that they are envisioning or the way that they're assuming that this is happening. Yeah. And and map that entire workflow end to end.   Mo Hussein (13:59) The very key kind of distinction is that, you know, a lot of operators and teams kind of assume, hey, you know, we have a set process, but it may not be happening the way that they are envisioning or the way that they're assuming that this is happening. And and map that entire workflow end to end.   identify what systems are involved, where handoffs occur, where approvals are required.   Jason Hull (14:21) identify what systems are involved, where handoffs occur, where approvals are required,   Mo Hussein (14:27) where judgment calls are are are are kind of made. And so, you know, every company has, you know, things like experienced managers and accountants and maintenance folks and and they usually know what good looks like versus what bad looks like. And so AI here is to help kind of convert that tribal knowledge ideally into a repeatable operating model. And so examples of how that mapping   Jason Hull (14:28) where judgment calls are kind of made. And so every company has you know things like experienced managers and accountants and maintenance folks, and and they usually know what good looks like versus what bad looks like. And so AI here helped kind of convert that tribal knowledge ideally into an overviewable operative model. So examples of   that mapping would be is you know, hey, what is the entire need to lease workflow?   Mo Hussein (14:50) would be is, you know, hey, what is the entire lead to lease workflow? You know,   Jason Hull (14:54) You know, what is the work order to completion, you know? what is our renewal offer to sign and executed actual renewal? And so and actually and again documenting that, a a lot of organizations have some notion of what that workflow kinda looks like. but   Mo Hussein (14:54) What is the work order to completion? You know? what is our renewal offer to signed and executed actual renewal? And so and actually, and again, documenting that. A a lot of organizations have some notion of what that workflow kind of looks like. but you know, they   haven't actually done they may not have documented, or if they did, it's not updated and they have an out of date SOP or a process diagram.   Jason Hull (15:12) you know, they haven't actually gotten any INOT documents in or if they did, it's not updated and they have an out of data so P or a process diagram.   Mo Hussein (15:22) And that's that's and that's that's that's a very important kind of key aspect of kind of this process.   Jason Hull (15:22) and that's that's and that's that's that's a very important kind of key aspect of kind of this process. Yeah, yeah. Well I a lot of times I end up talking with clients and I've noticed kind of this pattern or trend in the industry of I call it the process myth where everybody thinks if we just had better processes   all of our hopes and dreams would come true when it comes to the off side of the business and we would be more profitable. And especially see this in the two to four hundred door range in single family or small multi-residential property management. And so the challenge there is th that it's impossible to create enough processes, KPIs, and systems to make mediocre people be great. But they pe that doesn't stop business owners from trying. They they're like   Mo Hussein (15:59) Right.   Right.   Jason Hull (16:04) They they they wake up in the morning, they're like, I want to play an impossible game today. And they they still try. And I call it the process myth because if you have really great people, even if your processes are garbage, that I've seen these businesses still perform well. But the reverse is not true. You have mediocre people, you could have insane amounts of systems and processes and stuff, and the business still has a lot of headaches and problems.   Mo Hussein (16:16) Mm-hmm.   Jason Hull (16:29) And so I've kind of noticed this pattern. I call it the three levels of process. And level one is documentation. It's just like writing stuff out. But that's kind of like the owner's manual in the glove box of the car. Nobody looks at it, it doesn't get updated. You know, it's like it's it's it's gathering dust, and people don't actually, that's not actually how the processes are run. And over time, things gravitate towards ease or grace or what the flows best for the person doing the job.   Mo Hussein (16:39) Mm-hmm, mm-hmm.   Jason Hull (16:57) Not for what's best for the job sometimes. And so it gravitates a little bit towards chaos or being worse. Then there's this level two, which is checklists. This is where people are using things like Asana or Process Street or Lead Simple or they some sort of checklist space system where now they're verifying the works getting done in a certain way. But checklist has its own problems in that it's very linear and not every process is linear.   Mo Hussein (16:59) Right, right.   Read simple. Mm-hmm.   Mm-hmm.   Jason Hull (17:24) There's decisions   and splits and merges and sting things happening concurrently in property management. And so the challenge with checklist also it can tend to slow things down. It's not as efficient. So the next level and the problem I had with checklist, we used to use process street, is that it it if anytime a process got complicated, I had to build logic and you know, if-then sort of situations into it.   And it usually got to the point where I didn't even understand it. Like a year later, I'm looking at a process. I'm like, I had to retranslate this back into something that made sense to my brain. And I always, and the nerd had to be the one that did all the updates on it because nobody else could understand it. So then we eventually graduated to level three. So level three is visual workflow. This is for humans.   Mo Hussein (18:01) Right.   Mm-hmm.   Jason Hull (18:13) And so, and with with this, my tip to everybody listening, if you have a system, whether it's checklist or it's any of these three levels, you know, documentation, checklist, or visual workflow, that you your first two processes you make as an operator or as a business owner is how to create a process in this system is number one. And number two, how to QA.   Mo Hussein (18:26) Did   Jason Hull (18:37) A process that is made in the system to know it's actually a good one. If you just make those two, you don't have to do any of the other stuff. Everybody else can do it. You just make those two. That's my tip for all you business owners. And now with AI, you can start adding AI. Once you have things visually mapped out, it's you've got the map like you're talking about. Now you can figure out all right, where can AI take over some of this stuff? And where do we still need the human in the loop? Right. So yeah.   Mo Hussein (18:43) Mm.   And automation. Mm-hmm. Yep.   Yeah.   Right,   Jason Hull (19:05) So any tips for those listening to this that are already geeking out with AI, they're doing a little bit of this measuring and mapping and automating. What are some of the biggest challenges you've noticed where this kind of breaks down or people are making mistakes?   Mo Hussein (19:19) It's it's honestly it's the it's the you know, AI value. it's it's a lot of the small individual decisions that are made in a in a repetitive fashion and that that are made a lot that really are gonna unlock like true value for for any operator. And so like, you know, having very clean data, standardized, you know, systems of truth by what we mean by that is that, you know, hey, you know.   Jason Hull (19:20) It's it's honestly it's the it's the you know, AI value it's it's   like true value for for any op   clean data, standardized, you know, systems of truth. But what we mean by that is that, you know, hey,   you know, you know, whatever work order system that you're using, for example, has accurate, you know, work order data. People, you know, you're making a segment for actually closing out the work order when they complete it. Hey, the end of the week, I'm gonna now try to remember what I did earlier in the week.   Mo Hussein (19:47) you know, whatever work order systems that you're using, for example, has accurate, you know, work order data. People, you know, your maintenance technicians are actually closing out the work order when they complete it. Not just, hey, the end the week, I'm gonna now try to remember what I did earlier in the week.   Close it out. So the data is   the data can't be trusted, then AI is just going   Jason Hull (20:04) data the be trusted and AI   Mo Hussein (20:07) to cause additional kind of confusion. And so having accurate systems of record. And I gave that example of of of a work order when a technician kind of closes that, right? the process map, I think the you know, the three buckets are like three level that you kind of gave, I think is a great, great.   Jason Hull (20:20) Yeah, yeah. Yeah, that makes sense.   yeah.   level that you kinda gave I think it's a great,   great anecdote and framing of how processes should be kind of looked at. And and I think one thing that a lot of operators usually tend to overlook or assume is you know how things are being done versus how they actually are being done within the schemes, right? So an owner somebody at some point said, okay hey this is a process we're gonna take and then over time that just kind of got changed.   Mo Hussein (20:29) anecdote and framing of how processes should be kind of looked at. And and I think one thing that a lot of operators usually tend to overlook or assume is you know how things are being done versus how they actually are being done within the teams, right? It's an owner, somebody at some point said, okay, hey, this is the process we're gonna take. And then over time that just kind of got changed. And there   may be, you know, two different property managers   Jason Hull (20:55) And there may be, you know, two different property managers   Mo Hussein (20:58) operating in two different regions in the same company that are doing leasing renewal differently, right? That going back to that point that you mentioned about systematizing and having accountability loops and task base or like checklist items and ensuring that those things are actually done in that same quality and that same fashion is very, very key. And so getting data, like getting the right data, accurate data,   Jason Hull (20:58) operating in two different regions in the same company that are doing these things renewal differently. Right. That point that you mentioned about synthesizing and having accountability loops and task based or like checklist items and ensuring that those things are actually done in that same quality, in that same fashion is very, very key. And so getting data, getting the right data, accurate data   Mo Hussein (21:23) and then also like your process mapping and your   Jason Hull (21:24) And then also like your process mapping   and your processes kind of documented. I think I think the visual representation is a great way to have that. And those are the two key things that ninety percent of folks that are trying to leverage AI and automation and even the folks that are starting to try to jump into this space and try to automate and use AI for these things like usually we're like where where they're really struggling with. got it. Yeah, I think   Mo Hussein (21:26) processes kind of documented. I think I think the visual representation is a great way to have that. Those are the two key things that ninety percent of folks that are trying to leverage AI and automation and even the folks that are starting to try to jump into this space and trying to automate and use AI for these things like usually we're like we're where they're really struggling with.   Jason Hull (21:50) I was just on a webinar recently and they were talking about building AI agents and they were talking about if you want to make really effective AI agents, you need to give them a really good job description, just like a human. And what what's really funny is if you we coach clients on this a lot, but if we tell the clients to to go, we coach clients on   Creating job descriptions. We call our version of them R docs because each section starts with an R, like role, responsibility, et cetera, all the typical stuff. But then we have some additional sections that we found really paramount. So what we'll tell them to do is go ask your team members, give them this framework, and have them create their own R Doc. And then you take a look at this and see if that's what you would have created. Because it's never like what they think their job is. It's usually very different than what the business owner thinks their job is.   Mo Hussein (22:25) Mm-hmm.   Right.   Jason Hull (22:36) And maybe even different what the manager, the ops person thinks the job is, but then you can actually literally get on the same page with them. You can be like negotiate this and be like, this is what we think your priorities should be, and what your outcomes should be, and what we want you to be able to accomplish. And this is helpful for them to know what they're aiming for so that they can please you because your team members want to please you if they're good. But usually there's a big disconnect, like you're saying, between what   Mo Hussein (23:00) Mm-hmm. Mm-hmm.   Jason Hull (23:05) the the employee thinks their j role and job is versus what their manager thinks they should be doing versus what the business owner thinks everybody should be doing. And so nobody's on the same page. And then you're everybody's roles are a little messy. And then you're going, let's give them processes now to work on. And they're not even clear on what their job is or what their role is. Yeah. And so same thing if you were going to build an AI agent and you were like, I want you to try and be good at everything. And then suddenly it's like really   Mo Hussein (23:29) Right.   Jason Hull (23:34) Hallucinating a lot and it's messing everything up and yeah. And it's not a realistic creature, you know, just like some people give create job descriptions that are for like four different personality types. Right. And then they hire somebody that maybe can actually do all four things, and we call those really highly adaptable, weird creatures entrepreneurs. And then they wonder why that property manager left and stole all their clients.   Mo Hussein (23:34) Horrible.   Right.   Yeah.   Jason Hull (23:57) Instead of finding somebody that's like really good at being one thing. Right. Yeah. And that's how you should see Asia.   Mo Hussein (24:00) Right. That that that that role clarity is very, very, very important, right? And that's how you should see agents as well, is that   like, hey, it's like a trained employee. And so you should exp you should expect the same level of, you know, investment involvement, if you will, and trying to and try to help them be the best of like, you know, whether it's a leasing agent, a maintenance coordinator, or whatever that their role may be. And I I think another aspect is and I'm curious how like how   Jason Hull (24:12) you should expect the same level of you know investment involvement if you will and trying to and try to help them be the best of like you know whether it's a leasing agent a maintenance coordinator or whatever that their role may be and I I think another aspect is and I'm curious that   like how you know when you guys are having conversations with clients around role descriptions stuff it's the concept of ownership like hey what you know how to how to align ownership to and lining that up to hopefully the mental business   Mo Hussein (24:28) you know, when you guys having conversations with clients around role descriptions and stuff, it's the concept of ownership. Like, hey, what, you know, how to how to align ownership to and lining that up to hopefully an eventual business outcome or KPI   or something so that, you know, their performance drives also the business performance, right? How have you guys had this conversation or how do you talk about kind of that concept? I can kind of allude to it without kind of explicitly calling it out.   Jason Hull (24:42) Kate guy or something so that you know their performance derives also the business performance, right? Yeah. How do you talk about kind of that concept? You kind of allude to it without kind of explicitly calling   it out. Yeah, I think well, sometimes I'll just totally call a business owner out on things. But I think what I think will be interesting is people are building starting to build agents. I think that they should.   They should have an understanding of personality types. I think they should have an understanding maybe or a conversation with AI about what Myers Briggs type might be good for this agentic role. And because like somebody that's really good at like strategy and the strategist role, which would be like an INTJ in Myers Briggs, might be good at some operational stuff, but they would be really terrible at customer service.   Mo Hussein (25:19) Mm-hmm.   Jason Hull (25:33) Because a lot of INTJs don't even like humans, right? And so they're logical thinkers and they're really judging and they're practical and they're in you know introverted and they're really bad at understanding how the other person feels or even expressing that. And so you're you you don't want to create these try and create AI AI agents that are multiple split personality types, because I don't think they're gonna be as effective. And you can't also, just like you wouldn't want somebody building the process.   QE QA QA of the process. You don't want them both. You don't want AI to be checking itself. Right. Right. The the brain that had problems doing the messing things up, maybe, or didn't do it totally right. You don't want them checking their own work. Right. And so, yeah, so I think this is going to be interesting that people are going to be building agents and they usually think just logically here's the context it needs, here's the role, whatever. But I think also maybe give it the personality that it.   Mo Hussein (26:08) Right, right.   Right.   Jason Hull (26:29) What's the disc assessment for this person, this agent? What's the Myers Briggs type for this agent? And then if especially if they're communicating with humans or doing a task that you want them to be somewhat human like, they're going to be much better at doing this if you give it you create them in the right way. Just an idea.   the other thing to know as a business owner, you need to know who you are so that you can build your dream team around you. So your advisors, whether they're agentic or human, your advisors, your team members, it should be built ultimately around you thriving and being healthy in your own business so that you've got the tea the tools and the resources that fit you. But most business owners make the mistake.   Of trying to build the business around the business and then wonder why they're miserable and why they're kind of a slave to their own business. Right.   Mo Hussein (27:16) Right. Right. Right.   Jason Hull (27:20) So anyway, Mo, measure, map, automate, MMA. Doesn't involve fighting too much. You know, like mixed martial arts. It's a little bit on the, you know, less physical side of things. fun chatting about.   Mo Hussein (27:26) No.   Jason Hull (27:34) all the the AI stuff that's going. How can people anything else that you want to add to our conversation here about yeah this model? And then could you tell us a little bit about what you do and how maybe you help property managers with this stuff? Yeah. Yeah. so I guess just to put it succinct, kind of a a sandwich kind of takeaway. So yeah, operators need to wait for a perfect AI.   Mo Hussein (27:48) Yeah. Yeah. so I guess just to put it succinctly, kind of a a a sandwich kind of takeaway. So, yeah, operators don't need to wait for a perfect AI strategy.   Start by identifying, measuring where value exists, where things are leaking, then mapping workflows and then deciding what can be safely automated and measuring whether those actions improve performance. and so   Jason Hull (28:00) Identifying, measuring where value exists, where things are leaking, then mapping workflows, and then deciding what can be safe and automated, and measuring whether.   Mo Hussein (28:10) like you know, over time we'll see that you know the winners are not gonna be the companies that have the most amount of most amount of data, but they're the ones that can convert data into consistent operating actions across how they've operated every door. if you we help clients with you know putting together SOPs, also mapping their technology needs, where where they're where they're having operational leaks in the business can be   Jason Hull (28:10) So like you know over time we'll see that you know the winners are not gonna be the companies that have the most amount of most amount of data, but they're the ones that can convert data into consistent operating actions across how they've operated every door. if you we help clients with you know putting together SOPs, also mapping their technology needs, where where they're where they're having operational leaks and the business   can be optimized.   Mo Hussein (28:37) Optimized further using   Jason Hull (28:38) Further using technology and automation, we have a platform that we've built, Prop Strata, to actually connect and help with that automation type effort. and we're also we also do a lot of accounting and and CPA work. you can reach us at www.balanceasset solutions.com, and my emails mo at propstrata.com, or you can reach out to our team at info at balance asset.   Mo Hussein (28:39) technology and automation. We have a platform that we've built, Prop Strata, to actually connect and and help with that automation kind of efforts. then we're also we also do a lot of accounting and and CPA work. you can reach us at www.balanceasset solutions.com and and then my email is mo at at propstrata.com or you can reach out to our team at info at balanceasset solutions.com.   Jason Hull (29:03) Cool. So they could take a look at this at propstrata.com.   Mo Hussein (29:07) Correct. W dot propstrata.com.   Jason Hull (29:11) Okay, cool. Very cool. All right. yeah, check that out, everybody. It sounds interesting. All right. Well, Mo, I appreciate you coming out and hanging out with me here on the DoorGro show and sharing everything.   All right.   So if   If you have ever felt stuck or stagnant in your property management business and you want to take it to the next level, reach out to us at doorgrow.com. We are the world's best at creating high-growth property management companies in the single-family residential space or the small multi-space. And if for a free training or how to get unlimited leads for free, text the word leads to 512-648-4608. That's 512-648-4608.   Also, join our free community just for property management business owners at doorgrowclub.com. And if you want tips, tricks, and ideas to learn about our offers, subscribe to our newsletter by going to doorgrow.com slash subscribe. And if you found this even a little bit helpful, don't forget to subscribe and leave us a review on whatever channel you saw or heard this on. We'd really appreciate it. And until next time, remember the slowest path to growth.   is to do it alone. So let's grow together. Bye everyone.

主播：翩翩（中国）+ Maelle（法国） 音乐：Soft Edges, Hard Limits最近梅莉给翩翩发了一个视频，内容非常发人深省（thought-provoking）。视频中AI回答了一个惊人的问题：如何悄无声息地毁掉下一代，却让他们毫无察觉。AI的答案让人联想到古代兵法中的“文伐”——不用武力，而是用文化、经济等方式，让人在舒适中失去警惕。01. Convenience and Short-Term Gratification 便利与短暂满足AI给出的第一个答案是：I wouldn't come with violence. I'd come with convenience.violence：暴力convenience：便利、方便摧毁一个人的方式不是暴力，而是便利。便利让生活更轻松，但心理学家指出：The easier everything becomes, the fewer opportunities we have to practice patience.opportunity：机会patience：耐心一切变得越容易，我们练习耐心的机会就越少。算法（algorithms）已经算准了你的喜好，不停引诱你花钱、花时间。Lure you into buying or consuming.短视频平台的运作方式也是如此，围绕便利来抢夺你的注意力。第二个答案是：I'd give them everything they wanted, and strip (剥夺) them of everything they needed.给他们想要的一切，同时夺走他们需要的一切。想要的东西（wants）是欲望——金钱、物质、名利，带来的只是短暂的快感。而我们真正需要的其实很少。庄子说：“鹪鹩巢于深林，不过一枝；偃鼠饮河，不过满腹。”拥有也是被拥有，多余的只会困住你、消耗你。02.Connection Without Real Connection 连接却没有真实连接第三个答案是：I'd make them feel connected but completely alone.让他们感觉在连接，却完全孤独。Online all day, but missing real human connection.整天在线，却错失了真实的人际连接。一家人坐在一起，每个人却都在玩手机——人在一起，心却没有在一起。梅莉退出社交媒体的原因之一，她不想错过真实的生活，而不是观看别人的生活。第四个答案是：I'd blur(模糊)the lines between truth and opinion until nothing meant anything.模糊真相与观点的界限，直到一切失去意义。自媒体时代最不缺的就是观点——公众号、短视频、直播、AI生成内容，每个人都在表达。当一个人被垃圾信息塞满之后，内心的镜子蒙上灰尘，照不出真实的世界，判断力就会下降03. Self-Worship and Distraction 自我崇拜与分心第五个答案是：To worship (崇拜) self but loathe (厌恶) who they are.崇拜自我，却厌恶真实的自己。社交媒体让人自我很大，但同时又被虚荣和恐惧绑架。建立不了不依赖外界评价的绝对自信，所以很容易讨厌自己。第六个答案是：I'd keep them distracted, numb, scrolling, always scrolling.distracted：分心的numb：麻木的scroll：滑动屏幕、刷手机让他们不断分心、麻木、一直刷、一直刷。这个答案无需多解释，我们都有体会。视频的最后一句话最扎心：The most brilliant part? They'd never know it was me. They'd call it freedom.最绝的是，他们永远不会知道是我，他们会称之为自由。听完这段内容，翩翩分享了自己喜欢的一个词叫“知止”——知道自己的心该停驻在哪里。尽量远离干扰源（no distractions），过简单的生活。梅莉则希望能更好地安排深度安静的阅读时间。没有干扰，只有安静的时光。欢迎在评论区留言：听完今天的分享，你最想改变的一个数字习惯是什么？你平时会主动远离哪些干扰源？也欢迎分享你的“知止”时刻。

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Episode #1409: Ford raises its profit outlook despite slowing sales, GM bets AI will revolutionize vehicle development, and new research shows consumers aren't anti-AI—they just want honesty. Show Notes with links: Ford says fewer sales don't mean weak...

《經濟學人》專訪馬斯克，探討他對科技、AI以及權力的看法。 馬斯克大膽預測， AI 智力將在 5 年內超越人類，10 年內形成毀滅性差距，同時還會讓社會走向極致富裕，讓貨幣與他自己的 7500 億美元財富失效。專訪到最後，連他自己都說，「我正在做的事情太荒謬了，很難相信這一切都是真的。」 然而，《經濟學人》認為，他的觀點存在自相矛盾：一面警告人類將對 AI 無能為力，一面卻繼續瘋狂擴建太空資料中心搶奪主導權；他鼓吹無貨幣烏托邦，卻無法解釋如何維持基礎設施營運。這種將人類命運押注於少數巨頭自律的宿命論，正掩蓋社會應有的監管機制，卻也肯定了馬斯克對科技的前瞻想像。 23:00｜美沙核協議降標準引爆核擴散危機 川普宣佈與沙烏地阿拉伯達成核合作條約，沒有要求沙烏地放棄濃縮核燃料，或是簽署嚴格監督協議。恐引發中東及南韓等盟友的核武競賽，毀壞國際核不擴散條約。 26:55｜高市早苗強硬執政引爆日本政壇反彈 日本首相高市早苗雖握有國會優勢，但推行鷹派國防、社會保守法案與擴張性財政政策，引發金融市場震盪及黨內外強烈反彈，民調支持度顯著下滑，執政面臨嚴重挑戰。 38:55｜中東的冷卻技術攻佔全球避暑商機 為了因應酷熱氣候，波斯灣國家大力發展高效率的區域冷卻系統。面對全球暖化與AI資料中心龐大的散熱需求，中東企業正積極把節能技術出口到印度與歐洲。 54:20｜中國新一代青年億萬富豪超低調 以DeepSeek創辦人梁文鋒為首的中國新一代青年富豪，聚焦於消費與科技領域，管理風格較為溫和、高度依賴海外市場。同時在政治監管與地緣政治風險下，他們極少受訪，選擇保持低調。 製作團隊：錢玉紘、莊志偉、邱宇豪 ＊延伸閱讀｜馬斯克憑什麼總是贏家？：https://www.cw.com.tw/article/5141567 ＊《超級 AI 管理課》輸入【AICEOPD300】再折300元 ：https://hi.cw.com.tw/u/k72qvcw/ ＊訂閱天下全閱讀：https://bit.ly/3STpEpV ＊意見信箱：bill@cw.com.tw -- Hosting provided by SoundOn

Today, Eric explores one of the biggest misconceptions surrounding AI. Stay tuned as he explains why AI will not build your business, and why building a successful business remains entirely your responsibility as an entrepreneur or business owner. Your Vision AI can help you brainstorm, improve your wording, and challenge your thinking, but it cannot define your vision. Only you can decide what kind of company you want to build, what type of freedom you want, and where you want your business to go. Trust As information becomes cheaper, trust becomes more valuable than ever. AI can write proposals, research prospects, and prepare you for meetings, but it cannot build trust, read body language, handle objections, or make someone feel understood. Accountability AI cannot hold you accountable. It can tell you what to do, but it cannot make you do it. It cannot make sales calls, follow up, delegate more, or have uncomfortable conversations. Hard Decisions AI can give you input and help you evaluate your options, but you are the one who must make difficult decisions. Whether you're firing someone, walking away from a client, or making a major investment, leadership requires courage, not computation. Consequences Responsibility can never be outsourced. AI can provide advice, but as a business owner, you are responsible for the decisions you make and the consequences that follow. The Value of AI AI can help you brainstorm, research, create SOPs, write proposals, support your marketing, prepare meetings, role-play sales calls, and accelerate learning. It reduces the value of repetitive work and routine tasks, allowing you to focus on the work only you can do. Summary As AI becomes more capable, your vision, leadership, judgment, relationships, courage, and accountability become even more valuable. Your competitive advantage is not AI—it's how you use it while continuing to lead your business. Connect with Eric Rozenberg On LinkedIn Facebook Instagram Website Listen to The Business of Meetings podcast Subscribe to The Business of Meetings newsletter  

Richelyn Toth is Co-Founder & COO of Worldwide Shipping & Logistics and StarSpree-AI. Worldwide Shipping & Logistics (WSL) and StarSpree-AI together form a unified, AI-powered logistics and trade infrastructure. WSL is a non-asset-based smart shipping platform — 100% operational in the United States — that orchestrates 300+ carriers through one intelligent system without owning trucks or warehouses. StarSpree-AI is the AI-native marketplace built to run on that infrastructure, opening a direct commerce corridor between Filipino MSMEs and buyers at home and abroad. StarSpree-AI drives the commerce; WSL moves the goods. This is The Bridge — from Filipino seller to local and global buyer, on one platform. This episode is recorded live at Yspaces in BGC, Taguig.In this episode:00:00 Introduction01:35 Ano ang Worldwide Shipping & Logistics and StarSpree-AI?13:27 What is the startup solving? 31:32 What are stories behind the startup? 01:01:24 How can listeners find more information?WORLDWIDE SHIPPING LOGISTICSWebsite: https://worldwideshippingandlogistics.comFacebook: https://facebook.com/profile.php?id=61566800318703LinkedIn: https://linkedin.com/company/worldwideshippingandlogisticsSTARSPREE-AIWebsite: https://starspree-ai.comLinkedIn: https://www.linkedin.com/company/112594093THIS EPISODE IS CO-PRODUCED BY:Symph: ⁠⁠⁠⁠⁠⁠https://symph.co⁠⁠⁠⁠⁠⁠OneCFO: ⁠⁠⁠⁠⁠⁠https://onecfoph.co⁠⁠⁠⁠⁠⁠Yspaces: ⁠⁠⁠⁠⁠⁠https://knowyourspaceph.com⁠⁠⁠⁠⁠⁠Kredit Hero: ⁠⁠⁠⁠⁠⁠https://kredithero.com⁠⁠⁠⁠⁠⁠Twala: ⁠⁠⁠⁠⁠⁠https://www.twala.io⁠⁠⁠⁠⁠⁠GigGenius: ⁠⁠⁠⁠⁠⁠https://gig-genius.io⁠⁠⁠⁠⁠⁠SkoolTek by Edfolio: ⁠⁠⁠⁠⁠⁠https://skooltek.co⁠⁠⁠⁠⁠⁠Red Circle Global: ⁠⁠⁠⁠⁠⁠https://www.redcircleglobal.com⁠⁠⁠⁠⁠⁠CHECK OUT OUR PARTNERS:Ask Lex PH Academy: ⁠⁠⁠⁠⁠⁠https://asklexph.com⁠⁠⁠⁠⁠⁠ (5% discount on e-learning courses! Code: ALPHAXSUP)Pahatid PH: ⁠⁠⁠⁠⁠⁠https://pahatid.ph⁠⁠⁠⁠⁠⁠Digital Workforce Group: ⁠⁠⁠⁠⁠⁠https://digitalworkforce.com⁠⁠⁠⁠⁠⁠Nascent Batteries: ⁠⁠⁠⁠⁠⁠https://nascentbatteries.com⁠⁠⁠⁠⁠⁠Level Up Talent Solutions: ⁠⁠⁠⁠⁠⁠https://lvluptalentsolutions.com⁠⁠⁠⁠⁠⁠Agile Data Solutions (Hustle PH): ⁠⁠⁠⁠⁠⁠https://agiledatasolutions.tech⁠⁠⁠⁠⁠⁠CloudCFO: ⁠⁠⁠⁠⁠⁠https://cloudcfo.ph⁠⁠⁠⁠⁠⁠ (Free financial assessment, process onboarding, and 6-month QuickBooks subscription! Mention: Start Up Podcast PH)ArkoTech: ⁠⁠⁠⁠⁠⁠https://arkotechspacesolutions.com⁠⁠⁠⁠⁠⁠DVCode Technologies Inc: ⁠⁠⁠⁠⁠⁠https://dvcode.tech⁠⁠⁠⁠⁠⁠Argum AI: ⁠⁠⁠⁠⁠⁠http://argum.ai⁠⁠⁠⁠⁠⁠PIXEL by Eplayment: ⁠⁠⁠⁠⁠⁠https://pixel.eplayment.co/auth/sign-up?r=PIXELXSUP1⁠⁠⁠⁠⁠⁠ (Sign up using Code: PIXELXSUP1)School of Profits: ⁠⁠⁠⁠⁠⁠https://schoolofprofits.academy⁠⁠⁠⁠⁠⁠Founders Launchpad: ⁠⁠⁠⁠⁠⁠https://founderslaunchpad.vc⁠⁠⁠⁠⁠⁠Hier Business Solutions: ⁠⁠⁠⁠⁠⁠https://hierpayroll.com⁠⁠⁠⁠⁠⁠Smile Checks: ⁠⁠⁠⁠⁠⁠https://getsmilechecks.com⁠⁠⁠⁠⁠⁠Wunderbrand: ⁠⁠⁠⁠⁠⁠https://wunderbrand.com⁠⁠⁠⁠⁠⁠Uplift Code Camp: ⁠⁠⁠⁠⁠⁠https://upliftcodecamp.com⁠⁠⁠⁠⁠⁠ (5% discount on bootcamps and courses! Code: UPLIFTSTARTUPPH)START UP PODCAST PHYouTube: ⁠⁠⁠⁠⁠⁠https://youtube.com/startuppodcastph⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠Facebook: ⁠⁠⁠⁠⁠⁠https://facebook.com/startuppodcastph⁠⁠⁠⁠⁠⁠Patreon: ⁠⁠⁠⁠⁠⁠https://patreon.com/StartUpPodcastPH⁠⁠⁠⁠⁠⁠PIXEL: ⁠⁠⁠⁠⁠⁠https://pixel.eplayment.co/dl/startuppodcastph⁠⁠⁠⁠⁠⁠Website: ⁠⁠⁠⁠⁠⁠https://startupnetwork.ph⁠⁠⁠⁠⁠⁠This episode is edited by the team at: ⁠⁠⁠⁠⁠⁠https://tasharivera.com

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This episode outlines how AI can be utilized within a short-term rental (STR) business, based on insights from Kenny Bedwell.Core Philosophy on AIAI as a Tool: AI is an evolving tool that requires a competent human driver to function effectively.Address Bottlenecks: Rather than building "cool" tech for the sake of it, use AI to solve specific business problems, bottlenecks, or time-consuming manual tasks.Verification is Mandatory: AI is not yet perfect; always verify information, especially when conducting research on regulations or critical business decisions.Practical ApplicationsImage Enhancement: Use AI to edit listing photos—such as adding twilight effects, enhancing night skies, or updating TV screens—to make them more attractive without being deceptive.Guest Communication: AI can assist in drafting responses to guest inquiries or refund requests, and advanced integrations can automate responses based on specific guidelines, such as property guidebooks.Research and Analysis:Regulatory Research: AI provides direct answers to local regulation questions faster than standard search engines.Financial Decisions: AI can process complex data, such as comparing cleaning company quotes or calculating linen rental versus laundry service costs, to help make informed financial choices.Design and Planning: Use AI to generate visual renderings for landscaping or signage, helping to clarify design concepts and communicate ideas to contractors without needing professional design services for every small detail.Implementation AdviceStart Simple: If you are unsure how to use AI, simply ask it how it can help you with your specific problems.Stay Informed: The industry is professionalizing, and AI will likely become ubiquitous in business operations within the next five years; learning it now is a necessary investment.

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這學期，史丹佛有一堂被稱為「科技圈夢幻演唱會」的AI課， 講師陣容超夢幻，有黃仁勳、山姆奧特曼、微軟執行長納德拉．．． 其實大家分享的重點在於AI的未來： 它能做遠比寫文章、做簡報、修照片多更多，你會有個是超棒的助手，只要你是懂得指揮它的那個人．．． 這堂課到底在講些什麼？雖然我們沒有機會上課，不過還是可以看一下他的大綱，肯定有幫助！ / 【吳淡如｜房商必修課】低於4折優惠中

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