at a significant automotive producer, watching engineers have a good time what they thought was a breakthrough. They’d used generative AI to optimize a suspension part: 40% weight discount whereas sustaining structural integrity, accomplished in hours as a substitute of the same old months. The room buzzed with pleasure about effectivity beneficial properties and value financial savings.
However one thing bothered me. We have been utilizing expertise that would reimagine transportation from scratch, and as a substitute, we have been making barely higher variations of elements we’ve been manufacturing because the Nineteen Fifties. It felt like utilizing a supercomputer to steadiness your checkbook: technically spectacular, however lacking the purpose totally.
After spending three years serving to automotive corporations deploy AI options, I’ve observed this sample in all places. The business is making a elementary mistake: treating generative AI as an optimization device when it’s truly a reimagination engine. And this misunderstanding may cost a little conventional automakers their future.
Why This Issues Now
The automotive business stands at an inflection level. Electrical automobiles have eliminated the central constraint that formed automobile design for a century—the inner combustion engine. But most producers are nonetheless designing EVs as if they should accommodate an enormous metallic block beneath the hood. They’re utilizing AI to make these outdated designs marginally higher, whereas a handful of corporations are utilizing the identical expertise to ask whether or not automobiles ought to appear to be automobiles in any respect.
This isn’t nearly expertise; it’s about survival. The businesses that determine this out will dominate the subsequent period of transportation. People who don’t will be a part of Kodak and Nokia within the museum of disrupted industries.
The Optimization Lure: How We Received Right here
What Optimization Appears to be like Like in Apply
In my consulting work, I see the identical deployment sample at nearly each automotive producer. A crew identifies a part that’s costly or heavy. They feed current designs right into a generative AI system with clear constraints: scale back weight by X%, preserve energy necessities, keep inside present manufacturing tolerances. The AI delivers, everybody celebrates the ROI, and the mission will get marked as successful.
Right here’s precise code from a conventional optimization strategy I’ve seen applied:
from scipy.optimize import reduce
import numpy as np
def optimize_component(design_params):
"""
Conventional strategy: optimize inside assumed constraints
Drawback: We're accepting current design paradigms
"""
thickness, width, top, material_density = design_params
# Reduce weight
weight = thickness * width * top * material_density
# Constraints based mostly on present manufacturing
constraints = [
{'type': 'ineq', 'fun': lambda x: x[0] * x[1] * 1000 - 50000},
{'kind': 'ineq', 'enjoyable': lambda x: x[0] - 0.002}
]
# Bounds from current manufacturing capabilities
bounds = [(0.002, 0.01), (0.1, 0.5), (0.1, 0.5), (2700, 7800)]
end result = reduce(
lambda x: x[0] * x[1] * x[2] * x[3], # weight perform
[0.005, 0.3, 0.3, 7800],
methodology='SLSQP',
bounds=bounds,
constraints=constraints
)
return end result # Yields 10-20% enchancment
# Instance utilization
initial_design = [0.005, 0.3, 0.3, 7800] # thickness, width, top, density
optimized = optimize_component(initial_design)
print(f"Weight discount: {(1 - optimized.enjoyable / (0.005*0.3*0.3*7800)) * 100:.1f}%")This strategy works. It delivers measurable enhancements — sometimes 10-20% weight discount, 15% value financial savings, that form of factor. CFOs find it irresistible as a result of the ROI is obvious and rapid. However take a look at what we’re doing: we’re optimizing inside constraints that assume the present design paradigm is appropriate.
The Hidden Assumptions
Each optimization embeds assumptions. Whenever you optimize a battery enclosure, you’re assuming batteries needs to be enclosed in separate housings. Whenever you optimize a dashboard, you’re assuming automobiles want dashboards. Whenever you optimize a suspension part, you’re assuming the suspension structure itself is appropriate.
Common Motors introduced final 12 months they’re utilizing generative AI to revamp car elements, projecting 50% discount in growth time. Ford is doing related work. So is Volkswagen. These are actual enhancements that may save tens of millions of {dollars}. I’m not dismissing that worth.
However right here’s what retains me up at evening: whereas conventional producers are optimizing their current architectures, Chinese language EV producers like BYD, which surpassed Tesla in world EV gross sales in 2023, are utilizing the identical expertise to query whether or not these architectures ought to exist in any respect.
Why Good Folks Fall into This Lure
The optimization entice isn’t about lack of intelligence or imaginative and prescient. It’s about organizational incentives. Whenever you’re a public firm with quarterly earnings calls, it is advisable present outcomes. Optimization delivers measurable, predictable enhancements. Reimagination is messy, costly, and won’t work.
I’ve sat in conferences the place engineers introduced AI-generated designs that would scale back manufacturing prices by 30%, solely to have them rejected as a result of they’d require retooling manufacturing strains. The CFO does the maths: $500 million to retool for a 30% value discount that takes 5 years to pay again, versus $5 million for optimization that delivers 15% financial savings instantly. The optimization wins each time.
That is rational decision-making inside current constraints. It’s additionally the way you get disrupted.
What Reimagination Really Appears to be like Like
The Technical Distinction
Let me present you what I imply by reimagination. Right here’s a generative design strategy that explores the total chance house as a substitute of optimizing inside constraints:
import torch
import torch.nn as nn
import numpy as np
class GenerativeDesignVAE(nn.Module):
"""
Reimagination strategy: discover whole design house
Key distinction: No assumed constraints on kind
"""
def __init__(self, latent_dim=128, design_resolution=32):
tremendous().__init__()
self.design_dim = design_resolution ** 3 # 3D voxel house
# Encoder learns to signify ANY legitimate design
self.encoder = nn.Sequential(
nn.Linear(self.design_dim, 512),
nn.ReLU(),
nn.Linear(512, latent_dim * 2)
)
# Decoder generates novel configurations
self.decoder = nn.Sequential(
nn.Linear(latent_dim, 512),
nn.ReLU(),
nn.Linear(512, self.design_dim),
nn.Sigmoid()
)
def reparameterize(self, mu, logvar):
"""VAE reparameterization trick"""
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
def ahead(self, x):
"""Encode and decode design"""
h = self.encoder(x)
mu, logvar = h.chunk(2, dim=-1)
z = self.reparameterize(mu, logvar)
return self.decoder(z), mu, logvar
def generate_novel_designs(self, num_samples=1000):
"""Pattern latent house to discover potentialities"""
with torch.no_grad():
z = torch.randn(num_samples, 128)
designs = self.decoder(z)
return designs.reshape(num_samples, 32, 32, 32)
def calculate_structural_integrity(design):
"""
Simplified finite factor evaluation approximation
In manufacturing, this might interface with ANSYS or related FEA software program
"""
# Convert voxel design to emphasize distribution
design_np = design.cpu().numpy()
# Simulate load factors (simplified)
load_points = np.array([[16, 16, 0], [16, 16, 31]]) # prime and backside
# Calculate materials distribution effectivity
material_volume = design_np.sum()
# Approximate structural rating based mostly on materials placement
# Increased rating = higher load distribution
stress_score = 0
for level in load_points:
x, y, z = level
# Test materials density in load-bearing areas
local_density = design_np[max(0,x-2):x+3,
max(0,y-2):y+3,
max(0,z-2):z+3].imply()
stress_score += local_density
# Normalize by quantity (reward environment friendly materials use)
if material_volume > 0:
return stress_score / (material_volume / design_np.measurement)
return 0
def calculate_drag_coefficient(design):
"""
Simplified CFD approximation
Actual implementation would use OpenFOAM or related CFD instruments
"""
design_np = design.cpu().numpy()
# Calculate frontal space (simplified as YZ airplane projection)
frontal_area = design_np[:, :, 0].sum()
# Calculate form smoothness (gradient-based)
# Smoother shapes = decrease drag
gradients = np.gradient(design_np.astype(float))
smoothness = 1.0 / (1.0 + np.imply([np.abs(g).mean() for g in gradients]))
# Approximate drag coefficient (decrease is healthier)
# Actual Cd ranges from ~0.2 (very aerodynamic) to 0.4+ (boxy)
base_drag = 0.35
drag_coefficient = base_drag * (1.0 - smoothness * 0.3)
return drag_coefficient
def assess_production_feasibility(design):
"""
Consider how simply this design will be manufactured
Considers elements like overhangs, inside voids, assist necessities
"""
design_np = design.cpu().numpy()
# Test for overhangs (more durable to fabricate)
overhangs = 0
for z in vary(1, design_np.form[2]):
# Materials current at stage z however not at z-1
overhang_mask = (design_np[:, :, z] > 0.5) & (design_np[:, :, z-1] < 0.5)
overhangs += overhang_mask.sum()
# Test for inside voids (more durable to fabricate)
# Simplified: rely remoted empty areas surrounded by materials
internal_voids = 0
for x in vary(1, design_np.form[0]-1):
for y in vary(1, design_np.form[1]-1):
for z in vary(1, design_np.form[2]-1):
if design_np[x,y,z] < 0.5: # empty voxel
# Test if surrounded by materials
neighbors = design_np[x-1:x+2, y-1:y+2, z-1:z+2]
if neighbors.imply() > 0.6: # largely surrounded
internal_voids += 1
# Rating from 0 to 1 (larger = simpler to fabricate)
total_voxels = design_np.measurement
feasibility = 1.0 - (overhangs + internal_voids) / total_voxels
return max(0, feasibility)
def calculate_multi_objective_reward(physics_scores):
"""
Pareto optimization throughout a number of targets
Steadiness weight, energy, aerodynamics, and manufacturability
"""
weights = {
'weight': 0.25, # 25% - reduce materials
'energy': 0.35, # 35% - maximize structural integrity
'aero': 0.25, # 25% - reduce drag
'manufacturability': 0.15 # 15% - ease of manufacturing
}
# Normalize every rating to 0-1 vary
normalized_scores = {}
for key in physics_scores[0].keys():
values = [score[key] for rating in physics_scores]
min_val, max_val = min(values), max(values)
if max_val > min_val:
normalized_scores[key] = [
(v - min_val) / (max_val - min_val) for v in values
]
else:
normalized_scores[key] = [0.5] * len(values)
# Calculate weighted reward for every design
rewards = []
for i in vary(len(physics_scores)):
reward = sum(
weights[key] * normalized_scores[key][i]
for key in weights.keys()
)
rewards.append(reward)
return torch.tensor(rewards)
def evaluate_physics(design, targets=['weight', 'strength', 'aero']):
"""
Consider towards a number of targets concurrently
That is the place AI finds non-obvious options
"""
scores = {}
scores['weight'] = -design.sum().merchandise() # Reduce quantity (unfavorable for minimization)
scores['strength'] = calculate_structural_integrity(design)
scores['aero'] = -calculate_drag_coefficient(design) # Reduce drag (unfavorable)
scores['manufacturability'] = assess_production_feasibility(design)
return scores
# Coaching loop - that is the place reimagination occurs
def train_generative_designer(num_iterations=10000, batch_size=32):
"""
Prepare the mannequin to discover design house and discover novel options
"""
mannequin = GenerativeDesignVAE()
optimizer = torch.optim.Adam(mannequin.parameters(), lr=0.001)
best_designs = []
best_scores = []
for iteration in vary(num_iterations):
# Generate batch of novel designs
designs = mannequin.generate_novel_designs(batch_size=batch_size)
# Consider every design towards physics constraints
physics_scores = [evaluate_physics(d) for d in designs]
# Calculate multi-objective reward
rewards = calculate_multi_objective_reward(physics_scores)
# Loss is unfavorable reward (we need to maximize reward)
loss = -rewards.imply()
# Backpropagate and replace
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Monitor finest designs
best_idx = rewards.argmax()
if len(best_scores) == 0 or rewards[best_idx] > max(best_scores):
best_designs.append(designs[best_idx].detach())
best_scores.append(rewards[best_idx].merchandise())
if iteration % 1000 == 0:
print(f"Iteration {iteration}: Greatest reward = {max(best_scores):.4f}")
return mannequin, best_designs, best_scores
# Instance utilization
if __name__ == "__main__":
print("Coaching generative design mannequin...")
mannequin, best_designs, scores = train_generative_designer(
num_iterations=5000,
batch_size=16
)
print(f"nFound {len(best_designs)} novel designs")
print(f"Greatest rating achieved: {max(scores):.4f}")See the distinction? The primary strategy optimizes inside a predefined design house. The second explores your complete chance of house, searching for options people wouldn’t naturally think about.
The important thing perception: optimization assumes what beauty like. Reimagination discovers what good might be.
Actual-World Examples of Reimagination
Autodesk demonstrated this with their generative design of a chassis part. As a substitute of asking “how will we make this half lighter,” they requested “what’s the optimum construction to deal with these load circumstances?” The end result: a design that diminished half rely from eight items to at least one whereas reducing weight by 50%.
The design seems alien: natural, nearly organic. That’s as a result of it’s not constrained by assumptions about how elements ought to look or how they’ve historically been manufactured. It emerged purely from bodily necessities.
Right here’s what I imply by “alien”: think about a automobile door body that doesn’t appear to be a rectangle with rounded corners. As a substitute, it seems like tree branches — natural, flowing constructions that observe stress strains. In a single mission I consulted on, this strategy diminished the door body weight by 35% whereas truly enhancing crash security by 12% in comparison with conventional stamped metal designs. The engineers have been skeptical till they ran the crash simulations.
The revealing half: once I present these designs to automotive engineers, the most typical response is “clients would by no means settle for that.” However they mentioned the identical factor about Tesla’s minimalist interiors 5 years in the past. Now everybody’s copying them. They mentioned it about BMW’s kidney grilles getting bigger. They mentioned it about touchscreens changing bodily buttons. Buyer acceptance follows demonstration, not the opposite means round.
The Chassis Paradigm
For 100 years, we’ve constructed automobiles round a elementary precept: the chassis supplies structural integrity, the physique supplies aesthetics and aerodynamics. This made excellent sense if you wanted a inflexible body to mount a heavy engine and transmission.
However electrical automobiles don’t have these constraints. The “engine” is distributed electrical motors. The “gasoline tank” is a flat battery pack that may function a structural factor. But most EV producers are nonetheless constructing separate chassis and our bodies as a result of that’s how we’ve at all times completed it.
Whenever you let generative AI design car construction from scratch with out assuming chassis/physique separation it produces built-in designs the place construction, aerodynamics, and inside house emerge from the identical optimization course of. These designs will be 30-40% lighter and 25% extra aerodynamically environment friendly than conventional architectures.
I’ve seen these designs in confidential periods with producers. They’re bizarre. They problem each assumption about what a automobile ought to appear to be. Some look extra like plane fuselages than automobile our bodies. Others have structural components that stream from the roof to the ground in curves that appear random however are literally optimized for particular crash eventualities. And that’s precisely the purpose they’re not constrained by “that is how we’ve at all times completed it.”
The Actual Competitors Isn’t Who You Suppose
The Tesla Lesson
Conventional automakers assumed their competitors was different conventional automakers, all enjoying the identical optimization recreation with barely totally different methods. Then Tesla confirmed up and altered the foundations.
Tesla’s Giga casting course of is an ideal instance. They use AI-optimized designs to switch 70 separate stamped and welded elements with single aluminum castings. This wasn’t doable by asking “how will we optimize our stamping course of?” It required asking “what if we rethought car meeting totally?”
The outcomes communicate for themselves: Tesla achieved revenue margins of 16.3% in 2023, in comparison with conventional automakers averaging 5-7%. That’s not simply higher execution; it’s a special recreation.
Let me break down what this truly means in observe:
| Metric | Conventional OEMs | Tesla | Distinction |
| Revenue Margin | 5-7% | 16.3% | +132% |
| Elements per rear underbody | 70+ items | 1-2 castings | -97% |
| Meeting time | 2-3 hours | 10 minutes | -83% |
| Manufacturing CapEx per car | $8,000-10,000 | $3,600 | -64% |
These aren’t incremental enhancements. That is structural benefit.
The China Issue
Chinese language producers are transferring even additional. NIO’s battery-swapping stations, which change a depleted battery in beneath three minutes, emerged from asking whether or not car vary needs to be solved by means of greater batteries or totally different infrastructure. That’s a reimagination query, not an optimization query.
Take into consideration what this truly means: as a substitute of optimizing battery chemistry or charging pace the questions each Western producer is asking, NIO requested “what if the battery doesn’t want to remain within the automobile?” This fully sidesteps vary nervousness, eliminates the necessity for large battery packs, and creates a subscription income mannequin. It’s not a greater reply to the outdated query; it’s a special query totally.
BYD’s vertical integration — they manufacture every little thing from semiconductors to finish automobiles — permits them to make use of generative AI throughout your complete worth chain relatively than simply optimizing particular person elements. Whenever you management the total stack, you possibly can ask extra elementary questions on how the items match collectively.
I’m not saying Chinese language producers will essentially win. However they’re asking totally different questions, and that’s harmful for corporations nonetheless optimizing inside outdated paradigms.
The Sample of Disruption
This is identical sample we’ve seen in each main business disruption:
Kodak had the primary digital digicam in 1975. They buried it as a result of it could cannibalize movie gross sales and their optimization mindset couldn’t accommodate reimagination. They stored optimizing movie high quality whereas digital cameras reimagined pictures totally.
Nokia dominated cellphones by optimizing {hardware} and manufacturing. They’d the very best construct high quality, longest battery life, most sturdy telephones. Then Apple requested whether or not telephones needs to be optimized for calling or for computing. Nokia stored making higher telephones; Apple made a pc that would make calls.
Blockbuster optimized their retail expertise: higher retailer layouts, extra stock, sooner checkout. Netflix requested whether or not video rental ought to occur in shops in any respect.
The expertise wasn’t the disruption. The willingness to ask totally different questions was.
And right here’s the uncomfortable fact: once I speak to automotive executives, most can recite these examples. They know the sample. They simply don’t imagine it applies to them as a result of “automobiles are totally different” or “we have now bodily constraints” or “our clients count on sure issues.” That’s precisely what Kodak and Nokia mentioned.
What Really Must Change
Why “Be Extra Modern” Doesn’t Work
The answer isn’t merely telling automakers to “be extra progressive.” I’ve sat by means of sufficient technique periods to know that everybody desires to innovate. The issue is structural.
Public corporations face quarterly earnings strain. Ford has $43 billion invested in manufacturing amenities globally. You’ll be able to’t simply write that off to strive one thing new. Vendor networks count on a gentle provide of automobiles that look and performance like automobiles. Provider relationships are constructed round particular elements and processes. Regulatory frameworks assume automobiles could have steering wheels, pedals, and mirrors.
These aren’t excuses, they’re actual constraints that make reimagination genuinely troublesome. However some modifications are doable, even inside these constraints.
Sensible Steps Ahead
1. Create genuinely unbiased innovation items
Not “innovation labs” that report back to manufacturing engineering and get judged by manufacturing metrics. Separate entities with totally different success standards, totally different timelines, and permission to problem core assumptions. Give them actual budgets and actual autonomy.
Amazon does this with Lab126 (which created Kindle, Echo, Hearth). Google did it with X (previously Google X, which developed Waymo, Wing, Loon). These items can fail repeatedly as a result of they’re not measured by quarterly manufacturing targets. That freedom to fail is what allows reimagination.
Right here’s what this seems like structurally:
- Separate P&L: Not a value middle inside manufacturing, however its personal enterprise unit
- Totally different metrics: Measured on studying and possibility worth, not rapid ROI
- 3–5-year timelines: Not quarterly or annual targets
- Permission to cannibalize: Explicitly allowed to threaten current merchandise
- Totally different expertise: Researchers and experimenters, not manufacturing engineers
2. Associate with generative AI researchers
Most automotive AI deployments give attention to rapid manufacturing functions. That’s advantageous, however you additionally want groups exploring chance areas with out rapid manufacturing constraints.
Companions with universities, AI analysis labs, or create inside analysis teams that aren’t tied to particular product timelines. Allow them to ask silly questions like “what if automobiles didn’t have wheels?” Most explorations will lead nowhere. The few that lead someplace can be transformative.
Particular actions:
- Fund PhD analysis at MIT, Stanford, CMU on automotive functions of generative AI.
- Create artist-in-residence applications bringing industrial designers to work with AI researchers.
- Sponsor competitions (like DARPA Grand Problem) for radical car ideas.
- Publish analysis overtly attracts expertise by being the place fascinating work occurs.
3. Have interaction clients in a different way
Cease asking clients what they need inside present paradigms. In fact they’ll say they need higher vary, sooner charging, extra comfy seats. These are optimization questions.
As a substitute, present them what’s doable. Tesla didn’t ask focus teams whether or not they needed a 17-inch touchscreen changing all bodily controls. They constructed it, and clients found they liked it. Typically it is advisable present individuals the long run relatively than asking them to think about it.
Higher strategy:
- Construct idea automobiles that problem assumptions
- Let clients expertise radically totally different designs
- Measure reactions to precise prototypes, not descriptions
- Focus teams ought to react to prototypes, not think about potentialities
4. Acknowledge what recreation you’re truly enjoying
The competitors isn’t about who optimizes quickest. It’s about who’s prepared to query what we’re optimizing for.
A McKinsey examine discovered that 63% of automotive executives imagine they’re “superior” in AI adoption, primarily citing optimization use circumstances. In the meantime, another person is utilizing the identical expertise to query whether or not we’d like steering wheels, whether or not automobiles needs to be owned or accessed, whether or not transportation needs to be optimized for people or communities.
These are reimagination questions. And in the event you’re not asking them, another person is.
Attempt This Your self: A Sensible Implementation
Wish to experiment with these ideas? Right here’s a sensible place to begin utilizing publicly out there instruments and information.
Dataset and Methodology
The code examples on this article use artificial information for demonstration functions. For readers desirous to experiment with precise generative design:
Public datasets you need to use:
Instruments and frameworks:
- PyTorch or TensorFlow for neural community implementation
- Trimesh for 3D mesh processing in Python
- OpenFOAM for CFD simulation (open-source)
- FreeCAD with Python API for parametric design
Getting began:
# Set up required packages
# pip set up torch trimesh numpy matplotlib
import trimesh
import numpy as np
import torch
# Load a 3D mannequin from Thingi10K or create a easy form
def load_or_create_design():
"""
# Load a 3D mannequin or create a easy parametric form
"""
# Possibility 1: Load from file
# mesh = trimesh.load('path/to/mannequin.stl')
# Possibility 2: Create a easy parametric form
mesh = trimesh.creation.field(extents=[1.0, 0.5, 0.3])
return mesh# Convert mesh to voxel illustration
def mesh_to_voxels(mesh, decision=32):
"""
Convert 3D mesh to voxel grid for AI processing
"""
voxels = mesh.voxelized(pitch=mesh.extents.max()/decision)
return voxels.matrix
# Visualize the design
def visualize_design(voxels):
"""
Easy visualization of voxel design
"""
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.determine(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
# Plot stuffed voxels
stuffed = np.the place(voxels > 0.5)
ax.scatter(stuffed[0], stuffed[1], stuffed[2], c='blue', marker='s', alpha=0.5)
ax.set_xlabel('X')
ax.set_ylabel('Y')In regards to the Writer
Nishant Arora is a Options Architect at Amazon Net Providers specializing in Automotive and Manufacturing industries, the place he helps corporations implement AI and cloud applied sciences
