diff --git a/test/test_ops.py b/test/test_ops.py
index 883b00c06c..e875e98876 100644
--- a/test/test_ops.py
+++ b/test/test_ops.py
@@ -419,8 +419,8 @@ class TestOps(unittest.TestCase):
     helper_test_op([(45,65)], lambda x: 2.0**x)
     helper_test_op([()], lambda x: x**2.0)
     helper_test_op([()], lambda x: 2.0**x)
-    # TODO: fix 0**x and 0**0 == 1
-    # helper_test_op(None, lambda x: 0**x, vals=[[-2.,-1,0,1,2,3]])
+    # TODO: fix backward
+    helper_test_op(None, lambda x: 0**x, vals=[[-2.,-1,0,1,2,3]], forward_only=True)
     # TODO: fix backward, should be nan
     helper_test_op(None, lambda x: (-2)**x, vals=[[-2.,-1,0,1,2,3]], forward_only=True)
 
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 3a309496c7..7e7fafbc02 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -2420,16 +2420,18 @@ class Tensor:
 
     base, exponent = self._broadcasted(x, reverse=reverse)
     ret = base.abs().log().mul(exponent).exp()
-    # correct sign of negative numbers raised to a power (cos has a period of 2pi so we use it here to get the oddness of the exponent)
+    # correct sign of negative base with odd exponent (cos has a period of 2pi so we use it here to get the oddness of the exponent)
     sign = (exponent * math.pi).cos()
-    # we only need to correct the sign if the base is negative
-    base_sign = ((base.sign()) - 1) / -2
-    # we need 0 to be positive so we need to correct base_sign when the base is 0
-    base_sign = base_sign - (1.5 * (1 - (base.sign().abs())))
-    # inject nan if the base is negative and the exponent is not an integer
-    to_nan = (exponent != exponent.trunc()).detach() * base_sign
+    negative_base = (base < 0).detach().where(1, 0)
+    # 1 for non-negative base or negative even exponent, -1 for negative odd exponent, don't care about non-integer exponent
+    correct_sign = sign * negative_base + (1 - negative_base)
+    # inject nan for negative base is negative and non-integer exponent
+    to_nan = negative_base * (exponent != exponent.trunc()).detach()
+    # 0 -> 1; 1 -> nan
     inject_nan = (-to_nan * 2 + 1).log().add(1)
-    return ret.mul(sign * base_sign + (1 - base_sign)).mul(inject_nan)
+    ret = ret.mul(correct_sign).mul(inject_nan)
+    # fix 0 ** 0 = 1
+    return ((base == 0) * (exponent == 0)).detach().where(1, ret)
 
   def maximum(self, x:Union[Tensor, ConstType]) -> Tensor:
     """