Libertarian

[Marc Beierschoder @MBeierschoder]

💡 One thought I kept coming back to after writing this:

Last night, we discussed another AI Factory program that we won at a team event.

It made me wonder: What if we could connect the learning across all our won AI factory programs - without ever crossing client confidentiality, security or policy boundaries?

Today, much of that knowledge travels through people. You call someone who has solved the problem before, hope they have time, and wait for an answer.

𝗪𝗵𝗮𝘁 𝗶𝗳 𝗔𝗜 𝗰𝗼𝘂𝗹𝗱 𝗺𝗮𝗸𝗲 𝘁𝗵𝗮𝘁 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗰𝗼𝗺𝗽𝗼𝘂𝗻𝗱?

Not by sharing confidential information, but by securely turning patterns, lessons and reusable knowledge from every program into intelligence that makes the next one better.

Maybe the real advantage of running many AI transformations won’t just be experience.

𝗜𝘁 𝘄𝗶𝗹𝗹 𝗯𝗲 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗺𝗮𝗸𝗲 𝘁𝗵𝗮𝘁 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗰𝗼𝗺𝗽𝗼𝘂𝗻𝗱 𝗮𝘁 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝘀𝗽𝗲𝗲𝗱.

The Deloitte research that triggered my original thought:
https://t.co/eX7wcO696m

[Marc Beierschoder @MBeierschoder]

𝗪𝗵𝗮𝘁 𝗶𝗳 𝗺𝗮𝗻𝗮𝗴𝗲𝗿𝘀 𝗮𝗿𝗲 𝗺𝗼𝗿𝗲 𝗮𝘁 𝗿𝗶𝘀𝗸 𝘁𝗵𝗮𝗻 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗲𝘀?

We keep asking which jobs AI will replace.

I increasingly think we are looking at the wrong layer of the organization.

For more than a century, we have built companies around a simple idea:

𝗣𝗲𝗼𝗽𝗹𝗲 𝗺𝗮𝗻𝗮𝗴𝗲 𝗽𝗲𝗼𝗽𝗹𝗲.

And there was a good reason for it.

A CEO cannot coordinate 10,000 people. A manager cannot coordinate 1,000.

So we created layers.

Teams need managers. Managers need managers. Information moves up. Decisions move down.

𝗧𝗵𝗲 𝗼𝗿𝗴 𝗰𝗵𝗮𝗿𝘁 𝗶𝘀, 𝗶𝗻 𝗽𝗮𝗿𝘁, 𝗮 𝗺𝗮𝗽 𝗼𝗳 𝗵𝘂𝗺𝗮𝗻 𝗰𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗰𝗼𝘀𝘁.

But what happens when that cost collapses?

This is what makes a recent Deloitte study interesting.

Today, only 6% of surveyed leaders say more than 40% of their organizational processes are automated or AI-enabled.

By 2028, 42% expect that to be the case.

𝗙𝗿𝗼𝗺 𝟲% 𝘁𝗼 𝟰𝟮% 𝗶𝗻 𝗷𝘂𝘀𝘁 𝘁𝘄𝗼 𝘆𝗲𝗮𝗿𝘀.

But the interesting implication isn’t automation.

𝗜𝘁’𝘀 𝗰𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻.

AI agents don’t just execute work.

They can increasingly route it, coordinate it, monitor it and escalate exceptions - across humans and machines.

Deloitte describes this as a shift from managing people to 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗻𝗴 𝘄𝗼𝗿𝗸.

I increasingly see the same question emerging in conversations about agentic organizations:

Companies are redesigning workflows while quietly assuming that the hierarchy around those workflows stays intact.

𝗪𝗵𝘆?

Imagine a leader with 20 people and 200 digital workers.

Would we really recreate three layers of management underneath?

I doubt it.

𝗦𝗼 𝘄𝗵𝘆 𝗮𝗿𝗲 𝘄𝗲 𝗮𝘀𝘀��𝗺𝗶𝗻𝗴 𝘁𝗼𝗺𝗼𝗿𝗿𝗼𝘄’𝘀 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝘄𝗶𝗹𝗹 𝗵𝗮𝘃𝗲 𝘁𝗼𝗱𝗮𝘆’𝘀 𝗼𝗿𝗴 𝗰𝗵𝗮𝗿𝘁?

This doesn’t make leadership less important.

𝗜𝘁 𝗺𝗮𝗸𝗲𝘀 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗹𝗲𝘀𝘀 𝘀𝘆𝗻𝗼𝗻𝘆𝗺𝗼𝘂𝘀.

Maybe the biggest organizational disruption from AI won’t be the disappearance of jobs.

Maybe it will be the disappearance of management layers.

Because hierarchy itself may have been a technology for solving human coordination limits.

And those limits are changing.

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘁𝗵𝗲 𝗼𝗿𝗴 𝗰𝗵𝗮𝗿𝘁 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴?

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘁𝗵𝗲 𝗿𝗲𝗮𝘀𝗼𝗻 𝘄𝗲 𝗻𝗲𝗲𝗱𝗲𝗱 𝗶𝘁 𝗶𝘀 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴?

𝘝𝘪𝘥𝘦�� 𝘤𝘳𝘦𝘥𝘪𝘵𝘴 𝘵𝘰 𝘴𝘪𝘮𝘰𝘯𝘮𝘦𝘺𝘦𝘳_𝘥𝘪𝘳𝘦𝘤𝘵𝘰𝘳

[Marc Beierschoder @MBeierschoder]

𝐀𝐈 𝐢𝐬𝐧’𝐭 𝐦𝐚𝐤𝐢𝐧𝐠 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐟𝐚𝐬𝐭𝐞𝐫.
𝐈𝐭’𝐬 𝐬𝐡𝐨𝐰𝐢𝐧𝐠 𝐰𝐡𝐢𝐜𝐡 𝐨𝐧𝐞𝐬 𝐰𝐞𝐫𝐞 𝐚𝐥𝐫𝐞𝐚𝐝𝐲 𝐬𝐥𝐨𝐰.

Need a presentation?
Minutes.

Need a business case?
Minutes.

Need software?
Much faster than before.

Creating things is becoming incredibly fast.
Moving the business isn’t.

Because the bottleneck was never PowerPoint.
Or code.
Or documents.

The bottleneck is still the same.

❌ Too many approvals.
❌ Too many handovers.
❌ Too many meetings.
❌ Too many decisions waiting for one person.

Many companies are giving employees AI…
…while asking them to work inside the same slow organization.

𝐓𝐡𝐚𝐭’𝐬 𝐥𝐢𝐤𝐞 𝐩𝐮𝐭𝐭𝐢𝐧𝐠 𝐚 𝐅𝐨𝐫𝐦𝐮𝐥𝐚 𝟏 𝐞𝐧𝐠𝐢𝐧𝐞 𝐢𝐧𝐭𝐨 𝐚 𝐭𝐫𝐚𝐟𝐟𝐢𝐜 𝐣𝐚𝐦.

The companies that win won’t be the ones with the best AI.
They’ll be the ones that remove the traffic.

✔️ Fewer approvals.
✔️ Clear decision rights.
✔️ Less bureaucracy.
✔️ Less waiting.

Because here’s the uncomfortable truth:

𝐀𝐈 𝐢𝐬𝐧’𝐭 𝐰𝐚𝐢𝐭𝐢𝐧𝐠 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐬𝐭𝐞𝐞𝐫𝐢𝐧𝐠 𝐜𝐨𝐦𝐦𝐢𝐭𝐭𝐞𝐞.

Why should your people?

Most companies think AI is changing work.

𝐈 𝐭𝐡𝐢𝐧𝐤 𝐢𝐭’𝐬 𝐞𝐱𝐩𝐨𝐬𝐢𝐧𝐠 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐭𝐡𝐚𝐭 𝐰𝐞𝐫𝐞 𝐚𝐥𝐫𝐞𝐚𝐝𝐲 𝐛𝐫𝐨𝐤𝐞𝐧.

The companies that lose won’t lose because their AI models were worse.

𝐓𝐡𝐞𝐲’𝐥𝐥 𝐥𝐨𝐬𝐞 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐞𝐢𝐫 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐬𝐲𝐬𝐭𝐞𝐦 𝐧𝐞𝐯𝐞𝐫 𝐜𝐚𝐮𝐠𝐡𝐭 𝐮𝐩.

𝘝𝘪𝘥𝘦𝘰 𝘤𝘳𝘦𝘥𝘪𝘵𝘴 𝘵𝘰 𝘢���𝘯𝘰𝘥3

[Marc Beierschoder @MBeierschoder]

Stop bragging about AI productivity.

If nobody knows whether the output is correct…
…what exactly have you become more productive at?

Imagine AI writes 10,000 lines of code today instead of the 500 your team wrote yesterday.

Sounds like a huge productivity gain.

But your best engineer didn’t suddenly become 20× better at reviewing it.

That’s the real bottleneck.

AI is making production abundant.
Trust isn’t scaling at the same speed.

I think many executives are optimizing the wrong KPI.

The question isn’t:
“How fast can we deploy AI?”

It’s:
“How fast can we verify it?”

AI will make productivity abundant.

Trust will become the rarest competitive advantage.

Do you agree?

𝘝𝘪𝘥𝘦𝘰 𝘤𝘳𝘦𝘥𝘪𝘵𝘴 𝘵𝘰 𝘬𝘢𝘵𝘦𝘴𝘢𝘭𝘪𝘯𝘨𝘦𝘳

[Marc Beierschoder @MBeierschoder]

𝐋𝐚𝐬𝐭 𝐰𝐞𝐞𝐤, 𝐈 𝐫𝐞𝐚𝐥𝐢𝐳𝐞𝐝 𝐦𝐨𝐬𝐭 𝐛𝐨𝐚𝐫𝐝𝐬 𝐚𝐫𝐞 𝐦𝐞𝐚𝐬𝐮𝐫𝐢𝐧𝐠 𝐭𝐡𝐞 𝐰𝐫𝐨𝐧𝐠 𝐀𝐈 𝐊𝐏𝐈.

Over the past three years, I’ve sat in dozens of AI investment discussions.

The questions are almost always the same.

❓How much will the licenses cost?
❓How many employees really need access?
❓What’s the ROI?

Important questions.

But I’ve noticed something surprising.

Almost nobody asks:

“How much faster are our people learning than our competitors?”

Across organizations, I see the same pattern emerging.

🔹 Some give their people the trust and freedom to experiment.
🔹 Others optimize for control before learning.

The result is that their workforces are quietly splitting into two groups:

🔹 Those who compound their capabilities every single week…
🔹 …and those who stop experimenting.

𝐈𝐭 𝐡𝐢𝐭 𝐦𝐞. 𝐓𝐡𝐞 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐀𝐈 𝐢𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭 𝐢𝐬𝐧’𝐭 𝐭𝐡𝐞 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦.

𝐈𝐭’𝐬 𝐭𝐫𝐮𝐬𝐭.

And I wonder whether boards are measuring the wrong balance sheet.

𝐄𝐯𝐞𝐫𝐲 𝐀𝐈 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐜𝐫𝐞𝐚𝐭𝐞𝐬 𝐭𝐰𝐨 𝐛𝐚𝐥𝐚𝐧𝐜𝐞 𝐬𝐡𝐞𝐞𝐭𝐬.

🔹 One measures risk.
🔹 The other measures learning.

Most boards spend far more time reviewing the first.

Yet learning compounds.

Every week of experimentation creates capabilities that competitors cannot simply buy.

Every week of hesitation widens the gap.

𝐓𝐡𝐞 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐀𝐈 𝐫𝐢𝐬𝐤 𝐦𝐚𝐲 𝐧𝐨 𝐥𝐨𝐧𝐠𝐞𝐫 𝐛𝐞 𝐚 𝐛𝐚𝐝 𝐦𝐨𝐝𝐞𝐥.

𝐈𝐭 𝐦𝐚𝐲 𝐛𝐞 𝐚 𝐜𝐨𝐦𝐩𝐚𝐧𝐲 𝐭𝐡𝐚𝐭 𝐭𝐞𝐚𝐜𝐡𝐞𝐬 𝐢𝐭𝐬 𝐩𝐞𝐨𝐩𝐥𝐞 𝐭𝐨 𝐬𝐭𝐨𝐩 𝐞𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐢𝐧𝐠.

𝐓𝐡𝐞 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐭𝐡𝐚𝐭 𝐰𝐢𝐧 𝐭𝐡𝐞 𝐀𝐈 𝐞𝐫𝐚 𝐰𝐨𝐧’𝐭 𝐧𝐞𝐜𝐞𝐬𝐬𝐚𝐫𝐢𝐥𝐲 𝐡𝐚𝐯𝐞 𝐭𝐡𝐞 𝐛𝐞𝐬𝐭 𝐦𝐨𝐝𝐞𝐥𝐬.

𝐓𝐡𝐞𝐲’𝐥𝐥 𝐡𝐚𝐯𝐞 𝐭𝐡𝐞 𝐟𝐚𝐬𝐭𝐞𝐬𝐭 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 ���𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞.

If you could add just one AI KPI to your next board meeting, what would it measure?

#AI #Leadership #BoardLeadership #FutureOfWork

𝘝𝘪𝘥𝘦𝘰 𝘤𝘳𝘦𝘥𝘪𝘵𝘴 𝘵𝘰 𝘢𝘭𝘦𝘹_𝘶𝘴𝘱𝘬

[Dr. Jonas Toegel @JonasToegel]

Der Unternehmer Christian Beer sagt: man möchte Deutschland und Russland spalten und den Verantwortlichen geht es nur um Macht und Geld.

Diese Strategie sei schwachsinnig, sagt Beer. Wir dürfen der Propaganda nicht erliegen und sollten uns um ein friedliches Miteinander mit Russland bemühen.

Das gesamte Gespräch gibt es hier: https://t.co/ITrJTlI6pa

Folgen Sie mir auf Telegram: https://t.co/wC1x0C7toA

[Andrew Bridgen @ABridgen]

Leaked video of Mark Zuckerberg where he warned his Facebook execs NOT TO GET VACCINATED with the mRNA (COVID) vaccines because "we don't know the long-term side effects of modifying people's DNA & RNA"

He censored doctors, scientists, and the sick who denounced the ‘Vaccines"

[Libertarian @MarcSebast9977]

Models will be shared.

Productivity gains will be copied.

Data advantages will shrink.

𝗧𝗵𝗲 𝗻𝗲𝘅𝘁 𝗺𝗼𝗮𝘁 𝗺𝗮𝘆 𝗯𝗲 𝘀𝗶𝗺𝗽𝗹𝗲:

Which organization learns faster than its competitors?

[DNI Tulsi Gabbard @DNIGabbard]

Today, on my final day as Director of National Intelligence, I’m releasing never-before-seen communications and documents exposing how Dr. Fauci provided millions in US taxpayer dollars to fund dangerous gain-of-function research at the Wuhan lab, worked with politicized elements within the Intelligence Community to suppress the truth about his actions and hide the virus’ lab-leak origins, and lied to Congress while under oath in 2024. It’s time you know the truth.

https://t.co/3YJSstB7d4

[Stefan Homburg @SHomburg]

„Das Risiko von Pandemien nimmt zu” ...

... glaubt @Ricarda_Lang. Man ist sprachlos, aber Lang verhinderte 2024 als Parteichefin eine Enquete und beschäftigte sich nie mit den Fakten. Wähler sollten das wissen, denn die nächste Fakepandemie kommt bestimmt.

[Marc Beierschoder @MBeierschoder]

This morning a company gave me something I absolutely didn’t deserve.
Most executives would call it bad business.
But it might be the smartest thing they’ve ever done.

[Dr. Jonas Toegel @JonasToegel]

Die Sprengung der Nordstream-Pipeline hat die Haupt-Schlagader für günstige Energie zerstört und die deutsche Wirtschaft massiv geschädigt.

Der Erfolgs-Regisseur Moritz Enders ("Toxic NATO") untersucht in seinem neuen Dokumentarfilm "Nordstream - Die Sprengung" die Hintergründe, mögliche Täter und die bis heute andauernde, massive Propaganda um den Vorfall.

Es war mir eine Ehre und eine große Freude, gemeinsam mit Ray McGovern, Dirk Pohlmann, Prof. Theodore Postol, Dr. Werner Rügemer, Prof. Ola Tunander, Erik Andersson und Harald Kujat an der Aufklärung mitwirken zu dürfen.

Die Premiere ist am 2. Juli um 19.30 Uhr im Babylon in Berlin.

Nähere Informationen gibt es hier: https://t.co/VkqXrXRlZk

Folgen Sie mir auf Telegram: https://t.co/wC1x0C7toA

[StRiWi @CStRiWi]

WUSSTEN SIE? 🇷🇺 EINMARSCH IN DIE UKRAINE BEENDETE CORONA ÜBER NACHT
Corona endete in🇩🇪 genau an dem Tag,an dem 🇷🇺 Truppen in die 🇺🇦 einmarschierten.
Tägliche Infektionszahlen,Inzidenzen,Hospitalisierungen und Verordnungen.Dann kam der 24.02.2022
Plötzlich war Schluss !

[Marc Beierschoder @MBeierschoder]

Physical AI won’t fail because robots aren’t good enough.

It will fail because companies aren’t.

Everyone wants humanoids.

Nobody wants to fix:

Bad processes
Bad data
Bad governance
Bad incentives

A robot can expose an organization’s weaknesses faster than any consultant.

The next robotics race won’t be won by better machines.

It will be won by better operating models.

#PhysicalAI #Robotics

[Marc Beierschoder @MBeierschoder]

𝐆𝐞𝐫𝐦𝐚𝐧𝐲 𝐝𝐢𝐝𝐧’𝐭 𝐥𝐨𝐬𝐞 𝐦𝐞 𝐭𝐨 𝐒𝐰𝐢𝐭𝐳𝐞𝐫𝐥𝐚𝐧𝐝 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐨𝐟 𝐭𝐚𝐱𝐞𝐬.

𝐆𝐞𝐫𝐦𝐚𝐧𝐲 𝐥𝐨𝐬𝐭 𝐦𝐞 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐒𝐰𝐢𝐭𝐳𝐞𝐫𝐥𝐚𝐧𝐝 𝐬𝐭𝐢𝐥𝐥 𝐫𝐞𝐰𝐚𝐫𝐝𝐬 𝐭𝐡𝐞 𝐛𝐞𝐡𝐚𝐯𝐢𝐨𝐫 𝐈 𝐯𝐚𝐥𝐮𝐞.

This chart isn’t really about public-sector jobs.

It’s about incentives.

Over the last 20 years:

+820,000 public-sector jobs

-800,000 self-employed

After three years in Switzerland, one difference stands out:

Switzerland often asks:

𝐃𝐨𝐞𝐬 𝐢𝐭 𝐰𝐨𝐫𝐤?

Germany increasingly asks:

𝐈𝐬 𝐢𝐭 𝐬𝐨𝐜𝐢𝐚𝐥𝐥𝐲 𝐚𝐜𝐜𝐞𝐩𝐭𝐚𝐛𝐥𝐞?

𝐌𝐚𝐲𝐛𝐞 𝐆𝐞𝐫𝐦𝐚𝐧𝐲 𝐢𝐬 𝐬𝐥𝐨𝐰𝐥𝐲 𝐦𝐚𝐤𝐢𝐧𝐠 𝐫𝐢𝐬𝐤-𝐭𝐚𝐤𝐢𝐧𝐠, 𝐚𝐦𝐛𝐢𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐢𝐧𝐝𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐭 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐥𝐞𝐬𝐬 𝐚𝐭𝐭𝐫𝐚𝐜𝐭𝐢𝐯𝐞.

[Marc Beierschoder @MBeierschoder]

The first AI agent feels like software. The fiftieth feels like management. The challenge is no longer technical. It's organisational.

[Marc Beierschoder @MBeierschoder]

Everyone talks about smarter models.

Almost nobody talks about better context.

Without context, AI scales mistakes faster.

The next competitive advantage may not be intelligence.

It may be knowing what matters.

Deeper insights:
https://t.co/JRBuvVdkil

[Marc Beierschoder @MBeierschoder]

Most executive discussions are happening one layer too low.

Leaders are comparing models.

Investors are valuing operating models.

A near-$1T Anthropic valuation is not a bet on intelligence.

It's a bet that millions of people will change how they work.

And that may be the most speculative assumption in tech today.

The real challenge was never building the technology.

The challenge is getting organisations to adopt it.

That's a very different game.

#EnterpriseAI #Leadership #Transformation

[Marc Beierschoder @MBeierschoder]

Most AI governance frameworks were built for software.

Not for systems that learn, reason and act.

The real challenge isn't controlling AI.

It's redesigning accountability.

Many organisations haven't realised that yet.

Deeper insights:
https://t.co/nXH9v7cHU4

[Marc Beierschoder @MBeierschoder]

𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰 𝗶𝘀 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵𝗶𝗻𝗴 𝗮 $𝟭 𝘁𝗿𝗶𝗹𝗹����𝗼𝗻 𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻.
I keep asking myself:
What assumptions must be true for that valuation to make sense?

Not because the technology isn't impressive.
It is.

Not because the models won't improve.
They will.

But a $1 trillion valuation is not a bet on technology alone.
It's a bet on what happens next.

𝗧𝗼 𝗷𝘂𝘀𝘁𝗶𝗳𝘆 𝘁𝗵𝗮𝘁 𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻, 𝗺𝗶𝗹𝗹𝗶𝗼𝗻𝘀 𝗼𝗳 𝗽𝗲𝗼𝗽𝗹𝗲 𝘄𝗼𝘂𝗹𝗱 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝗹𝘆 𝗰𝗵𝗮𝗻𝗴𝗲 𝗵𝗼𝘄 𝘁𝗵𝗲𝘆 𝘄𝗼𝗿𝗸.
Companies would need to redesign processes.
Decision making would need to accelerate.
Operating models would need to evolve.
And productivity gains would need to show up at scale.

Yet, when I meet executive teams, I see a very different reality.
Most aren't debating which model is smartest.
They're asking a much simpler question:
Where is the ROI?

That's what makes this moment so fascinating.
We've largely solved the problem of making the technology smarter.

𝗜'𝗺 𝗻𝗼𝘁 𝘀𝘂𝗿𝗲 𝘄𝗲'𝘃𝗲 𝗲𝘃𝗲𝗻 𝘀𝘁𝗮𝗿𝘁𝗲𝗱 𝘁𝗼 𝘀𝗼𝗹𝘃𝗲 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗼𝗳 𝗺𝗮𝗸𝗶𝗻𝗴 𝗼𝗿𝗴𝗮𝗻𝗶𝘀𝗮𝘁𝗶𝗼𝗻𝘀 𝗿𝗲𝗮𝗱𝘆 𝗳𝗼𝗿 𝗶𝘁.
Processes are largely the same.
Responsibilities are largely the same.
Decision bottlenecks are largely the same.
Employees are still trying to fit a new technology into old ways of working.

𝗧𝗵𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗶𝘀 𝗺𝗼𝘃𝗶𝗻𝗴 𝗮𝘁 𝗲𝘅𝗽𝗼𝗻𝗲𝗻𝘁𝗶𝗮𝗹 𝘀𝗽𝗲𝗲𝗱.
𝗢𝗿𝗴𝗮𝗻𝗶𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗵𝗮𝗻𝗴𝗲 𝗶𝘀𝗻'𝘁.

So perhaps the real question isn't whether Anthropic deserves a $1 trillion valuation.
Perhaps the real question is:

Are we underestimating how hard organisational transformation really is?
Because if we are, the biggest bottleneck ahead may not be the models.
It may be us.

#Leadership #EnterpriseTransformation #FutureOfWork #TechnologyStrategy