TL;DR— It’s obvious — 2025 was the last normal year. 

We are now man­u­fac­tur­ing arti­fi­cial intel­li­gence at an indus­tri­al scale. Those who mere­ly buy it become depen­dent con­sumers. Those who embody it — embed­ding it into the phys­i­cal sys­tems, work­flows, and judg­ment they already mas­ter, while keep­ing the human above the loop— will shape what comes next.

Wis­dom, not speed alone, is the deci­sive capability.

Manufacturing Intelligence

We are no longer sim­ply train­ing mod­els. We are man­u­fac­tur­ing non-biological intel­li­gence: turn­ing sand into sil­i­con, pow­er­ing it with gigawatts, and build­ing the fac­to­ries that pro­duce thought. Capa­bil­i­ty con­tin­ues to accel­er­ate. Inference-time scal­ing and agen­tic archi­tec­tures are expand­ing what these sys­tems can do. 

The con­test over who con­trols the full stack—energy, hard­ware, mod­els, appli­ca­tions, and integration—is becom­ing the cen­tral com­pe­ti­tion of the cen­tu­ry. The con­straints are phys­i­cal and human: ener­gy, semi­con­duc­tors, tal­ent, and pub­lic trust.

The great­est risk is not a rogue super­in­tel­li­gence. It is that we fail to build the capa­bil­i­ty to secure our own future.

From Tool to Utility

When the cost of a thought falls close to zero, intel­li­gence stops being a tool you pur­chase and becomes a util­i­ty you build on — like elec­tric­i­ty. Most orga­ni­za­tions still treat it as a pro­duc­tiv­i­ty plu­g­in. The time saved van­ish­es into ver­i­fi­ca­tion, coor­di­na­tion, and meet­ings. Struc­tures remain unchanged. Per­for­mance does not improve.

The more use­ful path is nar­row­er and more delib­er­ate. Rebuild high-volume work­flows with agent stacks. Let the sys­tems sense, draft, and route. Keep humans on the con­straints, the approvals, and the stop but­ton. Run the new ver­sion in par­al­lel with the old one, inside the fire­wall, on live work. 

Mea­sure cycle time, error rate, and cost. After a few weeks, the data will tell you whether to retire the old process. This is how intel­li­gence becomes infra­struc­ture rather than a pur­chased fea­ture. Learn­ing stays inside the organization.

The Industrial Advantage

Ger­many and the Euro­pean indus­tri­al base are par­tic­u­lar­ly well posi­tioned for this. Deep process knowl­edge, engi­neer­ing excel­lence, and phys­i­cal sys­tems exper­tise are pre­cise­ly the assets required to inte­grate intel­li­gence into the real world rather than mere­ly con­sume it. 

The risk is not a short­age of engi­neers. It is becom­ing a pure con­sumer of for­eign intel­li­gence while con­tin­u­ing to run process­es designed for scarce cognition.

The Human Above the Loop

Pure capa­bil­i­ty nar­ra­tives are incom­plete. Accel­er­at­ing intel­li­gence with­out a cor­re­spond­ing wis­dom rev­o­lu­tion pro­duces chaos, lia­bil­i­ty, and resis­tance. Speed and tech­no­log­i­cal power are not enough. We need the capac­i­ty to hold com­plex­i­ty with­out col­laps­ing into entropy—the aware­ness and respon­si­bil­i­ty that keep the human above the loop.

Agen­tic sys­tems can exe­cute. Humans must own the mean­ing, the judg­ment, and the account­abil­i­ty. Deter­min­is­tic guardrails, eval­u­a­tion suites, and clear lines of respon­si­bil­i­ty are not brakes. They are what make the trans­for­ma­tion legit­i­mate and sustainable. 

When an orga­ni­za­tion rebuilds one nar­row, high-impact work­flow at a time and gen­er­ates mea­sur­able results, it cre­ates the evi­dence that con­verts skep­tics and builds the mus­cle for broad­er change.

Sovereignty in the Upper Layers of the AI Pyramid

Most com­pa­nies will never com­pete at the AI pyra­mid base lay­ers of ener­gy and semi­con­duc­tors. Their sov­er­eign­ty lies in appli­ca­tions and trans­for­ma­tion­al inte­gra­tion: link­ing dig­i­tal deci­sions to phys­i­cal objects with proof, turn­ing process knowl­edge into liv­ing sys­tems that learn, and becom­ing AI-native rather than AI-adjacent.

The alter­na­tive is famil­iar — more tools, more pilots, more time spent ver­i­fy­ing out­puts that never change the oper­at­ing sys­tem. In an envi­ron­ment of ris­ing geopo­lit­i­cal stakes and pub­lic skep­ti­cism, that path leads to depen­den­cy and inter­nal fric­tion. The embod­i­ment path leads to short­er cycles, fewer errors, retained con­trol, and com­pound­ing inter­nal learning.

How We Choose to Respond

2026 is the take­off year. The ques­tion is no longer what machines can do. It is how we choose to respond—how we evolve along­side them toward a mean­ing­ful coex­is­tence of human and arti­fi­cial intelligence.

If you are lead­ing an orga­ni­za­tion through this tran­si­tion, the prac­ti­cal start­ing point remains the same: name one high-volume work­flow that can be rebuilt, run the new ver­sion in par­al­lel, and keep humans on the judg­ment gates. Move slow to be fast.

I am speak­ing about these themes in keynotes and strate­gic con­ver­sa­tions. If this res­onates, I would wel­come the dialogue.

AGI evolut industrial intelligence nina schick